Machine learning-supported manufacturing: a review and directions for future research

Figures & data

Figure 1. Classification of review articles.

Figure 2. PRISMA diagram for showing the article selection process of relevant documents describing the support of ML in manufacturing.

Figure 3. Number of articles included in the current review based on manufacturing functions and publication years.

Figure 4. Keyword co-occurrence analysis using VOSviewer.

Table 1. Classification of ML support in material selection and parameter prediction articles.

Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(C. C. Zhou et al., 2009)	Developing and optimizing the multi-objectives of material selection	ANN and GA	SL	Mechanical, environmental, and economic properties	An optimum solution as selected material	Multi-objective optimization for sustainable products
(Xiong et al., 2012)	Developing NN and regression-based algorithm for prediction of bead geometry in robotic gas metal arc welding for rapid manufacturing	ANN	SL	Welding speed, nozzle-to-plate distance, wire feed rate, arc voltage	Bed width and height	Robotic gas metal arc welding for metals
(Sood et al., 2012)	Investigation of understanding the effect of raster angle, layer thickness, raster width, part build orientation, and air gap on the compressive strength of a test specimen	ANN	SL	Raster angle, air gap, part orientation, layer thickness	Compressive strength	–
(Pilania et al., 2013)	Developing a quick and simple way to select materials with ML	KRR	SL	Polymeric chains	Atomization and formation energy, lattice parameter, electron affinity, electronic dielectric, spring, and total dielectric constants, bandgap	Polymer manufacturing
(R. Liu et al., 2015)	Decreasing passed time during the material selection	Search path refinement and search space reduction	SL	Youngs modulus, yield strength, magneto strictive strain, composite function, and weighted gaussian function	Youngs modulus, yield strength, magneto strictive strain, composite function, and WGF	HDMD for Galfenol manufacturing
(Strasser et al., 2018)	Developing an ML algorithm for selecting variables, building and evaluating models for adaptive parameter settings in manufacturing processes	ANN, Linear regression, cross-validation, square mean error	SL and UL	Dimension of each part in each step, the weight, temperature, pressure, and forces applied to the produced part	Quality measures	–
(Radetzky et al., 2019)	Determining manufacturing parameter properties for the grinding process	Agglomerative hierarchical clustering and DT (C4.5)	SL and UL	Grinding disc, cutting fluid, linear feed, volume flow of cutting fluid	Avg roughness, Avg surface roughness, and gloss value measured in gloss units	Grinding for cutlery manufacturing
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Lee & Tsai, 2019)	Improvement of yield issues in colour filter manufacturing and identifying important parameters that affect PS which is a part of a colour filter	Stepwise regression, DT, RF, partial least squares,backpropagation NN	SL	Process steps, tool parameters, recipes, and metrology measurements such as RGB colour layers, indium tin oxide layer, black matrix, and PS	PS thickness and 10 more parameters selected	Thin film transistor liquid crystal display components manufacturing
(Gjelaj et al., 2019)	Developing of turning process based on analysing tool path length, tool selection, and machining parameters for the turning process using AI	Multi-objective GA	SL	Feed rate, cutting speed, depth of cut	Cutting force, feed rate, cutting speed, depth of cut	Turning for metal manufacturing
(M. Zhang et al., 2019)	Predicting the high cycle fatigue life of laser powder bed fusion stainless steel 316L with the use of neuro-fuzzy based ML algorithm	ANFIS NN	SL	Layer thickness, layer power, tensile properties, scan speed, post-processing temperature	Fatigue life	LPBF for stainless steel manufacturing
(Srinivasan et al., 2020)	Developing a methodology to accelerate the searching of the AM processing space for suitable printing parameter sets	SVR	SL	Power, velocity, efficiency, and depth-to-width ratio	Efficiency and depth-to-width ratio	General LPBF process
(Kopper et al., 2020)	Predicting the ultimate tensile strength for high-pressure die-casting of aluminium	RF, SVM, DT, NN	SL and UL	Avg fast head and rod pressure, avg intermediate head and rod pressure, avg slow head and rod pressure, biscuit length, cavity fill time, die close tank level and temperature, and so on	UTS, tensile strain, and quality index	High-pressure die-casting for Aluminium manufacturing
(Wanigasekara et al., 2020)	Developing an ML-based predictive model to predict the manufacturing parameters of composites using automated fibre placement	ANN	SL	Lay up speed, hot gas torch temperature, consolidation source	Short-beam strength, interlaminar shear strength, and elastic modulus	Automated fibre replacement for unidirectional composite manufacturing
(Herriott & Spear, 2020)	Investigating the performance of data-driven modelling for material property prediction of a simulated microstructural dataset	Ridge regression, DT, and CNN	SL	Volume, axis lengths, aspect ratio, number of neighbours, equivalent spherical diameter, omega-3, Schmid factor, micromechanical Taylor factor, the average distance to the grain boundary	Yield strength	Metal AM for stainless steel 316L
(Chang et al., 2021)	Developing a parameter-selecting algorithm for magnetic material manufacturing with SLM	DT	SL	Oxygen concentration, laser power, scanning speed, and operating frequency	Iron loss and permeability	SLM for soft magnetic composites
(Torquato et al., 2021)	Developing a multi-objective methodology to optimize the electric arc furnace-based steel production and to reduce energy consumption and scrap cost	Non-dominated sorting GA II	SL	Individual scrap price, scrap availability, tap additives, and ambient temperature	Optimal scrap parameters for lower energy consumption and scrap cost	Electric arc furnace for steel manufacturing
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(H. S. Park et al., 2021)	Improving Ti-6Al-V4 SLM-fabricated part quality and selection of optimum process parameters	DNN	SL	Laser power, laser scanning speed, layer thickness, hatch distance	Density ratio and surface roughness	SLM for Biomedical applications
(Barrionuevo et al., 2021)	Predicting the relative density of 316L stainless steel 3D printed parts	SVM, DT, RF, gradient boosting, gaussian processes, kNN, ANN	SL	Laser power, scanning speed, layer thickness, hatch spacing, and particle size	Relative density	SLM for stainless steel manufacturing
(Moges et al., 2021)	Predicting the melt-pool width of laser powder bed fusion additive manufactured parts	Regression (Polynomial)	SL	Solid and liquid density, solidus and liquidus temperature, solid and liquid specific heat capacity, solid and liquid thermal conductivity, latent heat of fusion, dynamic viscosity, coefficient of thermal expansion, preheat temperature, laser spot radius	Melt-pool width	LPBF
(McGregor et al., 2022)	Predicting the part geometry and use of these predictions to qualify the parts	SVR and SVC	SL	CAD dimensions, x, y, r locations in build, hardware set, material, thermal cure, layout, feature category, and class	The predicted geometry of the parts	–
(Maitra et al., 2022)	Predicting the relative density of Ti-6Al-4V alloy manufactured by SLM	GPR and MLR	SL	Laser power, scanning speed, hatch spacing, layer thickness, volumetric energy density	Relative density of Ti-6Al-4V alloy	SLM for Ti-6Al-4V alloy manufacturing
(He et al., 2022)	Assisting textile manufacturing firms to optimize the overall process performance and product quality as a whole	DQN based RL and RF	RL and SL	Water content, temperature, pH, and treating time	Textile properties: colour depth, colour indexes for colour variation in three dimensions	Textile manufacturing
(P. Wu et al., 2022)	Reducing manufacturing time, labour cost, and additional machining costs	DL and GA	SL	Material removal rate, energy consumption, surface roughness, and maximum cutting force	Feed rate, spindle rotation speed, depth of width, and depth of cut	Machining
(B. Wang et al., 2022)	Optimizing processing window for femtosecond laser-induced nanostructures	k-means clustering, PCA, ANN, RF, DT, SVM, kNN, NB classifier	SL and UL	Laser power, fluence, scanning speed, and scanning times	Optimized operation window for the best quality	Femtosecond laser-induced periodic surface structures manufacturing

Table 2. Classification of ML support in manufacturing scheduling articles.

Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Mönch et al., 2006)	Modelling semiconductor wafer fabrication machines as parallel batch processors in the diffusion and oxidation areas	NN and DT	SL	Machines, job families, batch machine factor, batch processing machine capacity, tightness of due dates, ready time tightness, ready time range	Feasible schedule	Wafer fabrication forsemiconductors
(Csáji et al., 2006)	Developing an adaptive iterative distributed scheduling reinforcement algorithm that works with a market-based production control system	RL	RL	Boltzmann formula modified with branches, machine status, makespan, unexpected events time	Job schedule status (either job cancellation or new job arrival)	–
(Shiue, 2009)	Developing a knowledge base class selection mechanism for supporting various product mix ratio environments	DT	SL	Number of jobs, mean utilization of machines, the standard deviation of machine utilization, mean utilization of load/unload stations, pallet buffers	Ranking of scheduling strategies	–
(Salehi & Tavakkoli-Moghaddam, 2009)	Optimizing initial sequences of process plan and selection of machine, cutting tool, and tool access direction	GA	SL	Geometrical information, tolerance, surface finish, tool information	Process plan	–
(Mehrjoo & Bashiri, 2013)	Developing a robust decision-support methodology based on PCA and LR for detailed production planning	PCA and LR	SL and UL	Machine properties, equipped gasses, daily plan, painted body stock, scheduled receipt, emergency sales demand, actual daily product	Scheduling support system	Production planning and control
(H. Li, 2017)	Decreasing the makespan and deadline of the flexible manufacturing systems	GA and RL	SL and RL	Encoding, gene length, crossover operator, mutation operator	Route planning	Flexible manufacturing system for automotive industry
(Shahrabi et al., 2017)	Enhancing the performance of dynamic job shop scheduling method	DQL	RL	Number of jobs on the shop floor, mean operations processing time	Optimum schedule	–
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Priore et al., 2018)	Developing a dynamic ensemble method to select the most appropriate dispatching rule over time with ML algorithms	DT, case based-reasoning,backpropagation NN, SVM	SL	Flow allowance factor, the mean number of alternative machines for an operation, utilization of each machine, mean number of parts in the system, mean utilization of FMS, the ratio of the utilization of the bottleneck machine to the mean utilization of the FMS	Mean tardiness and mean flow time of the proposed strategies	–
(Lin et al., 2019)	Developing a smart manufacturing factory framework that investigates job shop scheduling problems with edge computing	DQL	RL	Number of jobs, number of machines, features of customer orders, total processing time, average completion time, makespan, average queuing time	Optimized working schedule	Job shop scheduling for semiconductor manufacturing
(De Jong et al., 2019)	Proposed an algorithm to generalize the job shop scheduling for a quick and accurate makespan regression	CNN with TL	SL	Job distribution, job types, job priority, processing time, number of automatic guided vehicles (AGV), and AVG’s speed	Makespan	–
(Tan et al., 2019)	Industrial robot assembly process for planning, scheduling, and modelling based on real-time data acquisition with edge computing, actuator network, and wireless sensors	QL	RL	Assembly configurations, task, processing time, setup time	Makespan	Industrial robot assembly scheduling
(Abidi et al., 2020)	Developing an intelligent scheduling algorithm for FMS using a hybrid ML algorithm	Fuzzy classifier and DBN	SL and RL	Buffer size, the arrival rate of parts, average speed, first come first served performance, shortest processing time performance, earliest due date performance	Scheduling rules: first come first serve, shortest processing time, earliest due date	–
(Gonzalez Rodriguez et al., 2020)	Developing an AI system to help the decision-making process in closed-loop supply chain manufacturing systems	RT and FIS	SL	Remaining production, differences between input and output, available time, classification rate	New classification rate and Externalizations	–
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Jiang et al., 2020)	Achieving the best connection between printing paths in different process parameters to have a better dimension accuracy	DNN	SL	Print speed, line distance, layer height, and filament extrusion speed	Print speed, line distance, layer height, and filament extrusion speed	FDM
(T. Zhou et al., 2021)	Developing a new AI scheduler for data-driven dynamic scheduling of manufacturing jobs with uncertainty for smart factories	DRL	RL	Job type, initialization time, nominal operating time, target completion time, machine type, machining speed factor, energy efficiency factor, waiting time for remaining workloads, remaining buffer length	Job Schedule	Job scheduling for smart factories
(I. B. Park et al., 2020)	Minimizing the makespan of the multichip product scheduling by inspecting die attach and wire bonding stages of a semiconductor production line	QL	RL	Job types, waiting operation, alternative machines, initial setup status	Job Schedule	Semiconductor Production Scheduling
(Altenmüller et al., 2020)	Developing and application of self-learning and autonomous algorithm to address order dispatching with strict time constraints in job shops	QL	RL	Number of machine groups, number of machines per machine group, number of product types, size of buffers before machine groups, order release time interval, setup times, time-coupling constraints	Job shop layout	–
(Kim & Zohdi, 2022)	Generating an optimal tool path for the SLS process	CNN and DL	SL	Ambient and initial temperatures, convection coefficient, thermal conductivity, density, the specific heat capacity of PA12, laser radius, laser scanning speed, optimal extinction coefficient, and material grip gap size	Optimal tool path	–
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(W. Wu et al., 2021)	To increase the response speed of decision-making-based process planning	DRL	RL	Total number of cutting tools, the total number of machine tools, and the tool approaching directions	Production planning model	Process planning for dynamic machining
(X. Shao & Kim, 2021)	Proposed an effective scheduler based on self-SL to solve complex job shop scheduling problems	Self-supervised long short-term memory	SSL	Number of jobs, position order, order of procedure in the machine, the ratio of procedure to all machines, the ratio of processing time to sum, etc.	Makespan	–
(Oluyisola et al., 2022)	Proposed a methodology for production planning and control	DNN and LR	SL	Preliminary production plan	Updated plan model	–
(J. Li et al., 2022)	Proposing a digital twin model for flexible assembly systems	DRL	RL	Workpieces, assembly actuators, surroundings, physical restrictions	Digital factory schedule	Flexible assembly lines

Table 3. Classification of ML support in manufacturing process selection articles.

Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Ip & Regli, 2006)	To determine the best manufacturing process based on shape feature information	SVM	SL	3D CAD Models and surface curvature	Prismatic-machined or cast-then-machined	–
(Deb et al., 2006)	Evaluation of the applicability of the backpropagation NN method for determining all possible machining operations for rotationally symmetric components	ANN	SL	Feature type, dimensions, tolerance, surface finish	Feasible machining operation sequences	Machining for rotational parts
(Sener & Karsak, 2008)	Developing fuzzy regression and fuzzy multiple objective methodologies for advanced manufacturing process selection	Fuzzy regression	SL	Cost, throughput, routing flexibility, volume flexibility, improvement in downtime, work in progress	Selected alternative of manufacturing technology	FMS
(Rajput et al., 2011)	Proposed a time-dependent decision-making methodology that includes the current and future behaviour of alternatives with quantitative and qualitative criteria to finalize an optimal choice for the wafer fabrication process	Fuzzy analytic hierarchy process	SL	Normalized thickness mean, normalized thickness uniformity, engineer’s experience, standard procedures, multi-response, on-line education	Rank of manufacturing alternatives	Wafer fabrication
(Evans et al., 2013)	Generating a distinct experience-based decision support system to calculate confidence factors for the successful adoption of potential technologies for a given set of requirements	Fuzzy DT	SL	Manufacturing decision problem requirements, crisp or fuzzy attribute, technology confidence factor, success rating of the project	Manufacturing technology suggestion	–
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Venkataraman et al., 2015)	Developing a methodology for manufacturing method selection based on ANN	ANN with Levenberg –Marquardt Algorithm	SL	Setup time, material type, material cost, cost of piece, capability, tool change, tool cost, manpower, wastage, production rate, quality status	The rank of the manufacturing processes to select the most suitable one	Planning, forming, shaving, shaping, hobbing, milling processes
(Mukherjee, 2017)	Developing an algorithm for sustainable process selection by ranking the sustainability indicators	k-means, SVM, and partial least squares	SL and UL	Sustainability footprint	Rank of sustainability indicators	Sustainable manufacturing
(Hoefer & Frank, 2018)	Generating a method for manufacturing process selection automatically during conceptual design	KNN, DT, and RF	SL	Three property metrics were used: geometry, machining, and orientation metrics	Machining process or casting then machining process	Casting and machining
(Hamouche & Loukaides, 2018)	Automating and enhancing the sheet forming selection process	ANN	SL	Matrix representations of spinning, deep drawing, stretch forming, air bending, roll bending	Geometrical prediction of the given shape	Sheet forming
(Marini & Corney, 2019)	Process selection methodology to minimize the consumption of raw material and machining by following a near-net shape procedure	Fuzzy logic	SL	Cost, component geometry, production volume, material, tolerances, surface roughness, tool, equipment, mechanical properties	Rank of feasible processes	Casting, forging, and AM technologies
(Ghahramani et al., 2020)	Providing an advanced solution for controlling manufacturing processes and gaining perspective on various dimensions that enable manufacturers to access effective predictive technologies	GA and ANN	SL	Cost function	Manufacturing operation	Smart Manufacturing
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Hodonou et al., 2020)	Developing a new methodology for manufacturing process selection based on fuzzy logic	Fuzzy logic	SL	Manufacturing cost, environmental impact	Performance indicator	Production of aircraft components
(Dohale et al., 2021)	Generating a compatible production system with ML for any manufacturing firm	An integrated three-stage Delphi-MCDM-BN framework	SL	Market needs, competitive priority, monetary investment in resources for the production system	Suggestion of a suitable production system	–
(Simeone et al., 2021)	Developing an intelligent decision-making support system to offer a simple solution for customers in a cloud manufacturing system	Three-layered feed-forward NN	SL	Surface utilization rate, processing time, deadline compatibility	Rank of manufacturing processes according to customer profile	Cloud framework manufacturing for sheet metal cutting
(Zhao et al., 2022)	Developing a novel framework for identifying process sequences used to manufacture a discrete part	PrefixSpan Algorithm	SL	STL file, material properties, quality requirements, manufacturing sequence	Sequence manufacturing pattern	–
(Y. Zhang & Fiona, 2022)	Designing a hybrid ML-assisted, web-based automated manufacturability analyser and recommender for additive manufacturing	Hybrid sparse CNN	SL	Material type, material density, printing speed, layer thickness, infill percent, adhesion type, nozzle temperature, bed temperature, scale	Printability status	Manufacturability for AM

Table 4. Classification of ML support in manufacturing monitoring articles.

Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Saxena & Saad, 2007)	Developing near-optimal design parameters of diagnostic systems for condition monitoring of mechanical systems	GA and ANN	SL	Vibration signals, rotational speed, radial load	Bearing and gear condition	Rotating mechanical systems
(C. Shao et al., 2013)	To increase the precise process knowledge for feature selection and parameter tuning in quality monitoring of manufacturing processes	KNN	SL	Features are defined in the study according to the inspected case study	Welding quality	Ultrasonic metal welding
(Grasso et al., 2017)	Developing a methodology to detect and identify defects during the layer-wise process in the visible range by a machine vision system	PCA and K-means Clustering	UL	Images of each printed layer	Defect regions	SLM
(Das et al., 2017)	Developing an effective methodology for internal defect identification by combining statistical features like asymmetry, dispersion, and excess for the friction stir welding process	SVM, SVR, ANN	SL	Tool rational speed, welding speed, shoulder diameter	Weld status (either defect-free weld or defective weld)	Friction stir welding
(Ren et al., 2018)	Developing a new methodology for bearing remaining useful life prediction based on DL	DNN	SL	Frequency domain features, autoencoder-time features, and time-frequency domain features extracted from vibration signals	Remaining useful life	Bearingmanufacturing
(Godreau et al., 2019)	Developing a new data mining method for the continuous improvement of high-speed machining	Contextual Clustering	UL	Vibration signals, the ID of the tool and workpiece program, spindle power, actual spindle speed, feed rate	Tool condition	High-speed machining
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Okaro et al., 2019)	Developing an automatic fault detection in AM products with ML	Semi-SL	Semi-SL	UTS, the x-y position of the laser, projection vectors of positions	Accept or reject the part	LPBF
(B. Zhang et al., 2019)	Developing a CNN based model to investigate the porosity data for laser AM process	CNN	SL	Coaxial images of melt pool	Porosity attributes	Laser AM
(Martínez-Arellano et al., 2019)	Developing a new big data approach for tool wear classification based on DL and signal imaging which avoids pre-processing or filter methods	CNN	SL	Force signals, images	Tool condition	Dry milling
(Pratama et al., 2020)	Developing a new perspective for online tool condition monitoring based on a novel ensemble classifier	Ensemble Classifier	SL	Cutting speed, feed rate, depth of cut	Tool condition (wear type)	–
(Nasir & Cool, 2020)	Developing a hybrid ML model by using the combination of vibration signals and self-organizing maps for the prediction of cutting power and waviness in the circular sawing process of Douglas-fir wood under very high feed speed conditions	Adaptive neuro-fuzzy inference system, self-organizing map-based NN	SL and UL	Cutting power and corresponding vibration signal for 1 second of cutting	Average cutting power and waviness	Wood machining
(Song et al., 2020)	Developing a new novel approach for determining the wear state of a milling cutter based on clutter signal of spindle current	Deep CNN	SL	Spindle vibration acceleration signals and spindle current signals	Wear status recognition for the milling cutter	Milling
(Gülçür &Whiteside, 2021)	Presenting quality proxies or process fingerprints work in the first place and improving with an additional layer of instrumentations using more quality indicators	Multiple Linear Regression	SL	Piston position, injection pressure, melt temperature, mould temperature, injection velocity, switch-over pressure, packing pressure, packing duration	Microreplication efficiency	Micro-injection moulding
(Xie et al., 2021)	Developing a novel method for accurate and automatic tool wear state identification	Least Squares SVM	SL	Vibration values in X, Y, and Z direction	Milling tool flank wear values	Milling
Source	Objective	Algorithm				Application Domain
		Main Algorithm	Supervision	Inputs	Outputs
(Kubik et al., 2021)	Developing an SVM to classify abrasive wear states on blanking operation based on transformation, pre-processing, and data acquisition	SVM	SL	Punch force, stroke speed, sensor type	Wear States	Blanking and sheet metal forming
(J. B. Wang et al., 2022)	Cutting force embedding for applications in feature selection for having a better performance for condition monitoring of cutting tools	Manifold Learning	UL	Depth of cut, spindle speed, feed rate, vibration signals, cutting force	Tool condition (wear)	Vertical machining centre
(Sandru et al., 2022)	Developing a methodology for modelling the dependency of the device performances under the influence of technology parameters at early stages	Bayesian Optimization, Regression, and NN	SL	Process control parameters	Electrical parameters	Analog circuit manufacturing
(Mei et al., 2022)	Developing an extendable relative degree of contribution-based feature selection methodology was proposed to identify optimal feature combination	DNN	SL	Pressure, motor power, volume flow, temperature, vibration, efficiency factor, cooling efficiency, cooling power	Cooler condition, valve condition, internal pump leakage, hydraulic accumulator	Machinery
(Sun et al., 2022)	Developing an ANN algorithm for status monitoring and milling cutter wear prediction	ANN	SL	Velocity, sound, high-frequency energy signal, current, cutting depth, rotating speed	Healthy, slight wear, significant wear	Milling
(Mahmood et al., 2022)	Developing a high-quality tool wear detection system	PCA	UL	Cutting parameters for different tools, spindle speed, feed rate, type of tool, coolant, working angle of the tool, etc.	Type of the tool wear	Drilling

Table 5. Classification of ML support in Quality Control Articles.

		Algorithm
Source	Objective	Main Algorithm	Supervision	Inputs	Outputs	Application Domain
(Kankar et al., 2011)	Developing a classification of ball bearing faults by using NN and SVM	NN and SVM	SL	Vibration signals, rotational speed, bearing condition, number of loaders	Bearing fault type	Machining for ball bearing production
(Lieber et al., 2013)	Prediction of the physical quality of intermediate products in interlinked manufacturing processes	KNN and DT	SL and UL	Rolling force, rolling speed, and temperature	Quality level and end dimensions of steel bars	Rolling mill process
(Strasser et al., 2018)	Developing an ML algorithm for selecting variables, building and evaluating models for adaptive parameter settings in manufacturing processes	ANN, LR, cross-validation, square mean error	SL and UL	Dimension of each part in each step, the weight, temperature, pressure, and forces applied to the produced part	Quality measures	–
(Escobar & Morales-Menendez, 2018)	Developing a pattern recognition strategy to detect rare quality events.	LR, HCR, ReliefF	SL	Feature correlation matrix, correlation threshold, list of conditional probabilities	Parsimonious predictive model containing the most relevant quality features for the product	–
(D. Wu et al., 2018)	Predicting the surface roughness of the products manufactured with FDM	RF, SVR, RR, least absolute shrinkage, and selection operator (LASSO)	SL	The temperature of the build plate, the temperature of the extruder, the vibration of the build plate, the vibration of the extruder, the temperature of the deposited material	Predictions of surface roughness	FDM
(Scime & Beuth, 2018)	Developing a CNN algorithm for autonomous detection and classification of anomalies occurred during manufacturing	CNN	SL	Images of defected parts	Detected anomalies	LPBF
(Peres et al., 2019)	Application of Predictive manufacturing system solution into quality control in the automotive industry	Gaussian NB, KNN, DT, Random Forest, SVM	SL	Dimensional features	Each car part labeled as OK or NOK	AutomotiveManufacturing
(Hanhirova et al., 2019)	Designing a defect detection ML system to detect power cable surface defects	CNN	SL	Cable outer diameter	Cable surface defects	Extrusion for power cablemanufacturing
(Mahmoudi et al., 2019)	Developing an anomaly detection framework for the LPBF process to distinguish process deviations by using thermal signatures	Gaussian regression and SVM	SL	High-speed thermal images of parts	Places of defects	LPBF
(Papananias et al., 2020)	Developing a new method called inspection by exception: used to decrease the inspection of produced parts	Fuzzy C-means clustering	UL	Material conditions, tempering temperature, vibration, and force signals of the metal-cutting process	Determination of parts that should be inspected	Metal manufacturing
(Z. Zhang et al., 2020)	Developing of a real-time defect identification method for Al allows using arc optical spectroscopy and an integrated learning method for the robotic arc welding process	PCA and RF	SL and UL	Fe I spectrum for wire feeding detection, H I spectrum for porosity detection, AR I spectrum for penetration detection	Normal seam, coupled defects, wire stuck, surface contamination, inner porosity, under penetration	Robotic arc welding for Aluminum alloys
(Cho et al., 2020)	Predicting the quality of plastic products manufactured with extrusion	LR, SVM, RF, Bagging method	SL	Extrusion temperature, extrusion speed, external temperature, water temperature, screw speed, die temperature, cylinder temperature, external humidity	Quality of extruded parts	Plastic Extrusion Process
(Finkeldey et al., 2020)	Developing a novel ML-based methodology to predict quality characteristics of the injection molding process for different process parameters	DT, Least Absolute Shrinkage and Selection Operator	SL	Nozzle contact diameter, cooling channel diameter, mold width, mold depth, mold length	Cavity pressure, injection pressure, flow rate, thickness, weight	Injection molding for plastic products
(Bauhofer & Daraio, 2020)	Developing a semi-empirical numerical model to predict shrinkage-driven distortions in direct laser writing	ANN	SL	Fabrication parameters, material parameters, modelling parameters	Elastic strain energy	Direct laser writing
(T. Wang et al., 2020)	Developing a feature extraction methodology to improve the detection of faulty and abnormal situations	Kernel PCA	SSL	BC3 flow, Cl2 flow, pressure, load, tuner, power	Fault type	–
(X. Zhang et al., 2020)	Analyzing the defect count data observed in steel products based on high overdispersion and nonnegative integers	RF	SL	Slab width, slab length, slab thickness, region of the slab, casting speeds, argon flow index, rolling temperature, the quantity of constituent, rolling width, rolling length, plate thickness, plate width, plate length, plate weight, heating index, annealing time, annealing temperature, in-mold EMS, EMS current index, steelmaking code	Number of defects	Casting, rolling, machining of steel
(Denkena et al., 2021)	Developing an essential approach with ML for achieving a correlation between process parameters and measured surface quality	SVM	SL	Tool feed rate, inclination angle, depth of cut, cutting speed	Roughness of the workpiece	Polishing process
(Leco &Kadirkamanathan, 2021)	Reducing total inspection time with the GPR model to predict part quality through in-process sensors	Gaussian Process Regression	SL	Depth of cut	Prediction of inspection results	Robotic machining
(Bak et al., 2021)	Developing a prediction algorithm for better product quality for the die-casting process	Shallow NN	SL	29 manufacturing parameters, some of which are: molten metal temperature, room temperature, humidity, metal pressure, valve gate open, low acceleration position, low speed	Product Quality	Die-casting for Aluminum
(W. Guo et al., 2021)	Estimating accurate surface roughness in the grinding process and providing feasible monitoring scheme for other manufacturing operations	Recurrent NN and Long short-term memory network	SL	Grinding force, vibration, acoustic emission signals	Surface roughness	Grinding of steel products
(Yan et al., 2021)	Developing a deep transition model with multi-task learning to predict all output sensing variables according to the sequential production line structure	DNN-based multistage multi-task learning	SL	Process sensing variables	Product quality sensing variables	Multistage manufacturing systems
(Charalampous et al., 2021)	Developing a novel method for process parameter selection for improving the dimensional accuracy of manufactured specimens with FDM	Regression modelling	SL	Layer height, printing speed, and printing temperature	Geometrical dimensions in height, depth, and thickness	FDM
(X. Zhang et al., 2021)	Improving the adaptability of the scale changes of surface defects of solar cells	CNN	SL	Defected solar cell images	Crack type and crack place on the solar cell	–
(Kahng & Kim, 2021)	Developing a SSL-based methodology to improve the performance of wafer bin map defect pattern classification	SSL	SSL	Five data augmentation: cropping, cut-out, rotation, shifting, and noise addition	Labeled class: center, donut, edge-ring, edge-loc, loc, scratch, near-full, random, none	Wafer bin map forsemiconductor manufacturing
(Reséndiz-Flores et al., 2022)	Finding an optimal solution for misclassification of variable screening purposes	Kernel-based Mahalanobis-Taguchi system, sequential feature selection, RF	SL	Day tank temperature, level, pump pressure input, recirculation flow, head pressure	Three cases of variable selection	Foam injection and welding
(Tian et al., 2022)	Improving prediction accuracy of broad learning system for surface roughness prediction	Broad learning system with binary grey wolf optimization	SL	Depth of cut, spindle speed, feed rate	Surface roughness	Slot milling
(Guo & Guo, 2022)	Increasing fault detection rates of MTS with BOW feature extraction		MTS based on BOW	SL	Bag-of-words set	Extracted features for fault detection
(Xu et al., 2022)	Developing a self-supervised high-efficiency defect detector methodology that uses image segmentation		SSL	SSL	Defected images of surface defects	Highlighted image of the surface defect
(Z. Liu et al., 2022)	Developing a methodology for metal surfaces with a few-shot defect recognition methodology	Attention embedding network, multi-resolution cropping SSL	SSL	Defected images	Highlighted image for the defect	Few-shot defect recognition
(Link et al., 2022)	Developing a general ML methodology for creating quality prediction models by using small datasets to predict surface roughness as a function of five process variables	Shape-constrained Regression	SL	Diameter of abrasive grits, cutting time, number of revolutions of brush, number of revolutions of the workpiece, cutting depth	Arithmetic-mean surface roughness	Surface roughness prediction for the Brushing process
(Manivannan, 2022)	Developing an ensemble-based CNN semi-supervised approach for wafer bin maps image classification	CNN	Semi-SL	Spatial defect pattern images	Defect patterns	Wafer bin map pattern detection
(Chu et al., 2022)	Developing a morphological feature transformation for 3D wafer bin maps for defect recognition	NN	Semi-SL	3D visuals of defect patterns	Defect patterns	Wafer Defect Recognition

Figure 5. Number of article distribution based on ML algorithm, supervision, and manufacturing function.

Figure 6. General overview of ML support in manufacturing.

Figure 7. Possible scheme to use transfer learning to transfer knowledge from the research proposed by Song et al. (2020) to the study conducted by J. Wang et al. (2022).

Figure 8. Working principle of a self-supervised learning algorithm.

Zhou, C. C., Yin, G. F., & Hu, X. B. (2009). Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Materials & Design, 30(4), 1209–1215. https://doi.org/10.1016/J.MATDES.2008.06.006

 Web of Science ®Google Scholar

Xiong, J., Zhang, G., Hu, J., & Wu, L. (2012). Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. Journal of Intelligent Manufacturing, 25(1), 157–163. https://doi.org/10.1007/S10845-012-0682-1

 Web of Science ®Google Scholar

Sood, A. K., Ohdar, R. K., & Mahapatra, S. S. (2012). Experimental investigation and empirical modelling of FDM process for compressive strength improvement. Journal of Advanced Research, 3(1), 81–90. https://doi.org/10.1016/J.JARE.2011.05.001

 Google Scholar

Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R. (2013). Accelerating materials property predictions using machine learning. Scientific Reports, 3(1), 1–6. https://doi.org/10.1038/srep02810

 Google Scholar

Liu, R., Kumar, A., Chen, Z., Agrawal, A., Sundararaghavan, V., & Choudhary, A. (2015). A predictive machine learning approach for microstructure optimization and materials design. Scientific Reports, 5(1), 1–12. https://doi.org/10.1038/srep11551

 Google Scholar

Strasser, S., Tripathi, S., & Kerschbaumer, R. (2018). An approach for adaptive parameter setting in manufacturing processes. DATA 2018 - Proceedings of the 7th International Conference on Data Science, Technology and Applications, 24–32. https://doi.org/10.5220/0006894600240032

 Google Scholar

Radetzky, M., Rosebrock, C., & Bracke, S. (2019). Approach to adapt manufacturing process parameters systematically based on machine learning algorithms. IFAC-Papersonline, 52(13), 1773–1778. https://doi.org/10.1016/J.IFACOL.2019.11.458

 Google Scholar

Lee, C. Y., & Tsai, T. L. (2019). Data science framework for variable selection, metrology prediction, and process control in TFT-LCD manufacturing. Robotics and Computer-Integrated Manufacturing, 55, 76–87. https://doi.org/10.1016/J.RCIM.2018.07.013

 Web of Science ®Google Scholar

Gjelaj, A., Berisha, B., & Smaili, F. (2019). Optimization of turning process and cutting force using multiobjective genetic algorithm. Universal Journal of Mechanical Engineering, 7(2), 64–70. https://doi.org/10.13189/UJME.2019.070204

 Google Scholar

Zhang, M., Sun, C. N., Zhang, X., Goh, P. C., Wei, J., Hardacre, D., & Li, H. (2019). High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach. International Journal of Fatigue, 128, 105194. https://doi.org/10.1016/J.IJFATIGUE.2019.105194

 Web of Science ®Google Scholar

Srinivasan, S., Swick, B., Groeber, M. A., Srinivasan, S., Swick, B., & Groeber, M. A. (2020). Laser powder bed fusion parameter selection via machine-learning-augmented process modeling. JOM, 72(12), 4393–4403. https://doi.org/10.1007/S11837-020-04383-2

 Web of Science ®Google Scholar

Kopper, A., Karkare, R., Paffenroth, R. C., & Apelian, D. (2020). Model selection and evaluation for machine learning: Deep learning in materials processing. Integrating Materials and Manufacturing Innovation, 9(3), 287–300. https://doi.org/10.1007/s40192-020-00185-1

 Web of Science ®Google Scholar

Wanigasekara, C., Oromiehie, E., Swain, A., Prusty, B. G., & Nguang, S. K. (2020). Machine learning based predictive model for AFP-Based unidirectional composite laminates. IEEE Transactions on Industrial Informatics, 16(4), 2315–2324. https://doi.org/10.1109/TII.2019.2932398

 Web of Science ®Google Scholar

Herriott, C., & Spear, A. D. (2020). Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods. Computational Materials Science, 175, 109599. https://doi.org/10.1016/J.COMMATSCI.2020.109599

 Web of Science ®Google Scholar

Chang, T.-W., Liao, K.-W., Lin, C.-C., Tsai, M.-C., & Cheng, C.-W. (2021). Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning. The International Journal of Advanced Manufacturing Technology, 114(9–10), 3177–3184. https://doi.org/10.1007/s00170-021-07037-y

 Web of Science ®Google Scholar

Torquato, M. F., Martínez-Ayuso, G., Fahmy, A. A., & Sienz, J. (2021). Multi-objective optimization of electric arc furnace using the non-dominated sorting genetic algorithm II. IEEE Access, 9, 149715–149731. https://doi.org/10.1109/ACCESS.2021.3125519

 Google Scholar

Park, H. S., Nguyen, D. S., Le-Hong, T., & van Tran, X. (2021). Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. Journal of Intelligent Manufacturing, 33(6), 1843–1858. https://doi.org/10.1007/s10845-021-01773-4

 Web of Science ®Google Scholar

Barrionuevo, G. O., Ramos-Grez, J. A., Walczak, M., & Betancourt, C. A. (2021). Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting. The International Journal of Advanced Manufacturing Technology, 113(1–2), 419–433. https://doi.org/10.1007/s00170-021-06596-4

 Web of Science ®Google Scholar

Moges, T., Yang, Z., Jones, K., Feng, S., Witherell, P., & Lu, Y. (2021). Hybrid modeling approach for melt-pool prediction in laser powder bed fusion additive manufacturing. Journal of Computing and Information Science in Engineering, 21(5). https://doi.org/10.1115/1.4050044

 PubMed Web of Science ®Google Scholar

McGregor, D. J., Bimrose, M. V., Shao, C., Tawfick, S., & King, W. P. (2022). Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials. Additive Manufacturing, 55, 102848. https://doi.org/10.1016/J.ADDMA.2022.102848

 Web of Science ®Google Scholar

Maitra, V., Shi, J., & Lu, C. (2022). Robust prediction and validation of as-built density of ti-6Al-4V parts manufactured via selective laser melting using a machine learning approach. Journal of Manufacturing Processes, 78, 183–201. https://doi.org/10.1016/J.JMAPRO.2022.04.020

 Web of Science ®Google Scholar

He, Z., Tran, K. P., Thomassey, S., Zeng, X., Xu, J., & Yi, C. (2022). Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. Journal of Manufacturing Systems, 62, 939–949. https://doi.org/10.1016/J.JMSY.2021.03.017

 Web of Science ®Google Scholar

Wu, P., He, Y., Li, Y., He, J., Liu, X., & Wang, Y. (2022). Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS. Journal of Manufacturing Systems, 64, 40–52. https://doi.org/10.1016/J.JMSY.2022.05.016

 Web of Science ®Google Scholar

Wang, B., Wang, P., Song, J., Lam, Y. C., Song, H., Wang, Y., & Liu, S. (2022). A hybrid machine learning approach to determine the optimal processing window in femtosecond laser-induced periodic nanostructures. Journal of Materials Processing Technology, 308, 117716. https://doi.org/10.1016/J.JMATPROTEC.2022.117716

 Web of Science ®Google Scholar

Mönch, L., Zimmermann, J., & Otto, P. (2006). Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines. Engineering Applications of Artificial Intelligence, 19(3), 235–245. https://doi.org/10.1016/J.ENGAPPAI.2005.10.001

 Web of Science ®Google Scholar

Csáji, B. C., Monostori, L., & Kádár, B. (2006). Reinforcement learning in a distributed market-based production control system. Advanced Engineering Informatics, 20(3), 279–288. https://doi.org/10.1016/J.AEI.2006.01.001

 Web of Science ®Google Scholar

Shiue, Y. R. (2009). Development of two-level decision tree-based real-time scheduling system under product mix variety environment. Robotics and Computer-Integrated Manufacturing, 25(4–5), 709–720. https://doi.org/10.1016/J.RCIM.2008.06.002

 Web of Science ®Google Scholar

Salehi, M., & Tavakkoli-Moghaddam, R. (2009). Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning. Engineering Applications of Artificial Intelligence, 22(8), 1179–1187. https://doi.org/10.1016/J.ENGAPPAI.2009.04.005

 Web of Science ®Google Scholar

Mehrjoo, S., & Bashiri, M. (2013). An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: An automotive industry case. Journal of Industrial Engineering International, 9(1), 1–12. https://doi.org/10.1186/2251-712X-9-14

 Google Scholar

Li, H. (2017). Improve the performance of a complex FMS with a hybrid machine learning algorithm. Journal of Software Engineering and Applications, 10(3), 257–272. https://doi.org/10.4236/JSEA.2017.103015

 Google Scholar

Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017). A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Computers & Industrial Engineering, 110, 75–82. https://doi.org/10.1016/J.CIE.2017.05.026

 Web of Science ®Google Scholar

Priore, P., Ponte, B., Puente, J., & Gómez, A. (2018). Learning-based scheduling of flexible manufacturing systems using ensemble methods. Computers & Industrial Engineering, 126, 282–291. https://doi.org/10.1016/J.CIE.2018.09.034

 Web of Science ®Google Scholar

Lin, C. C., Deng, D. J., Chih, Y. L., & Chiu, H. T. (2019). Smart manufacturing scheduling with edge computing using multiclass deep Q network. IEEE Transactions on Industrial Informatics, 15(7), 4276–4284. https://doi.org/10.1109/TII.2019.2908210

 Web of Science ®Google Scholar

De Jong, A. W., Rubrico, J. I. U., Adachi, M., Nakamura, T., & Ota, J. (2019). A generalised makespan estimation for shop scheduling problems, using visual data and a convolutional neural network. International Journal of Computer Integrated Manufacturing, 32(6), 559–568. https://doi.org/10.1080/0951192X.2019.1599430

 Web of Science ®Google Scholar

Tan, Q., Tong, Y., Wu, S., & Li, D. (2019). Modeling, planning, and scheduling of shop-floor assembly process with dynamic cyber-physical interactions: A case study for CPS-based smart industrial robot production. The International Journal of Advanced Manufacturing Technology, 105(9), 3979–3989. https://doi.org/10.1007/S00170-019-03940-7

 Web of Science ®Google Scholar

Abidi, M. H., Alkhalefah, H., Mohammed, M. K., Umer, U., & Qudeiri, J. E. A. (2020). Optimal scheduling of flexible manufacturing system using improved lion-based hybrid machine learning approach. IEEE Access, 8, 96088–50. https://doi.org/10.1109/ACCESS.2020.2997663

 Web of Science ®Google Scholar

Gonzalez Rodriguez, G., Gonzalez-Cava, J. M., & Méndez Pérez, J. A. (2020). An intelligent decision support system for production planning based on machine learning. Journal of Intelligent Manufacturing, 31(5), 1257–1273. https://doi.org/10.1007/S10845-019-01510-Y

 Web of Science ®Google Scholar

Jiang, J., Yu, C., Xu, X., Ma, Y., & Liu, J. (2020). Achieving better connections between deposited lines in additive manufacturing via machine learning. Mathematical Biosciences and Engineering, 4(4), 3382–3394. https://doi.org/10.3934/MBE.2020191

 Google Scholar

Zhou, T., Tang, D., Zhu, H., & Wang, L. (2021). Reinforcement learning with composite rewards for production scheduling in a smart factory. IEEE Access, 9, 752–766. https://doi.org/10.1109/ACCESS.2020.3046784

 Web of Science ®Google Scholar

Park, I. B., Huh, J., Kim, J., & Park, J. (2020). A reinforcement learning approach to robust scheduling of semiconductor manufacturing facilities. IEEE Transactions on Automation Science and Engineering, 17(3), 1420–1431. https://doi.org/10.1109/TASE.2019.2956762

 Web of Science ®Google Scholar

Altenmüller, T., Stüker, T., Waschneck, B., Kuhnle, A., & Lanza, G. (2020). Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints. Production Engineering, 14(3), 319–328. https://doi.org/10.1007/S11740-020-00967-8

 Google Scholar

Kim, D. H., & Zohdi, T. I. (2022). Tool path optimization of selective laser sintering processes using deep learning. Computational Mechanics, 69(1), 383–401. https://doi.org/10.1007/s00466-021-02079-1

 Web of Science ®Google Scholar

Wu, W., Huang, Z., Zeng, J., & Fan, K. (2021). A fast decision-making method for process planning with dynamic machining resources via deep reinforcement learning. Journal of Manufacturing Systems, 58, 392–411. https://doi.org/10.1016/J.JMSY.2020.12.015

 Web of Science ®Google Scholar

Shao, X., & Kim, C. S. (2021). Self-supervised long-short term memory network for solving complex job shop scheduling problem. KSII Transactions on Internet & Information Systems, 15(8), 2993–3010. https://doi.org/10.3837/TIIS.2021.08.016

 Web of Science ®Google Scholar

Oluyisola, O. E., Bhalla, S., Sgarbossa, F., & Strandhagen, J. O. (2022). Designing and developing smart production planning and control systems in the industry 4.0 era: A methodology and case study. Journal of Intelligent Manufacturing, 33(1), 311–332. https://doi.org/10.1007/s10845-021-01808-w

 Web of Science ®Google Scholar

Li, J., Pang, D., Zheng, Y., Guan, X., & Le, X. (2022). A flexible manufacturing assembly system with deep reinforcement learning. Control Engineering Practice, 118, 104957. https://doi.org/10.1016/J.CONENGPRAC.2021.104957

 Web of Science ®Google Scholar

Ip, C. Y., & Regli, W. C. (2006). A 3D object classifier for discriminating manufacturing processes. Computers & Graphics, 30(6), 903–916. https://doi.org/10.1016/J.CAG.2006.08.013

 Web of Science ®Google Scholar

Deb, S., Ghosh, K., Paul, S., Deb, S., Ghosh, K., & Paul, S. (2006). A neural network based methodology for machining operations selection in computer-aided process planning for rotationally symmetrical parts. Journal of Intelligent Manufacturing, 17(5), 557–569. https://doi.org/10.1007/S10845-006-0026-0

 Web of Science ®Google Scholar

Sener, Z., & Karsak, E. E. (2008). A decision making approach based on fuzzy regression and fuzzy multiple objective programming for advanced manufacturing technology selection. 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008, 964–968. https://doi.org/10.1109/IEEM.2008.4738013

 Google Scholar

Rajput, H. C., Milani, A. S., & Labun, A. (2011). Including time dependency and ANOVA in decision-making using the revised fuzzy AHP: A case study on wafer fabrication process selection. Applied Soft Computing, 11(8), 5099–5109. https://doi.org/10.1016/J.ASOC.2011.05.049

 Web of Science ®Google Scholar

Evans, L., Lohse, N., & Summers, M. (2013). A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Systems with Applications, 40(16), 6412–6426. https://doi.org/10.1016/J.ESWA.2013.05.047

 Web of Science ®Google Scholar

Venkataraman, K., Vijaya Ramnath, B., Sarvesh, R., & Rohit Prasanna, C. (2015). Selection of manufacturing method using artificial neural network. Applied Mechanics and Materials, 766–767, 1201–1206. https://doi.org/10.4028/www.scientific.net/amm.766-767.1201

 Google Scholar

Mukherjee, R. (2017). Selection of sustainable process and essential indicators for decision making using machine learning algorithms. Process Integration & Optimization for Sustainability, 1(2), 153–163. https://doi.org/10.1007/S41660-017-0011-4

 Google Scholar

Hoefer, M. J., & Frank, M. C. (2018). Automated manufacturing process selection during conceptual design. Journal of Mechanical Design, Transactions of the ASME, 140(3). https://doi.org/10.1115/1.4038686

 PubMed Web of Science ®Google Scholar

Hamouche, E., & Loukaides, E. G. (2018). Classification and selection of sheet forming processes with machine learning. International Journal of Computer Integrated Manufacturing, 31(9), 921–932. https://doi.org/10.1080/0951192X.2018.1429668

 Web of Science ®Google Scholar

Marini, D., & Corney, J. R. (2019). Process selection methodology for near net shape manufacturing. International Journal of Advanced Manufacturing Technology, 106(5–6), 1967–1987. https://doi.org/10.1007/S00170-019-04561-W

 Web of Science ®Google Scholar

Ghahramani, M., Qiao, Y., Zhou, M., Hagan, A., & Sweeney, J. (2020). AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA Journal of Automatica Sinica, 7(4), 1026–1037. https://doi.org/10.1109/JAS.2020.1003114

 Google Scholar

Hodonou, C., Kerbrat, O., Balazinski, M., & Brochu, M. (2020). Process selection charts based on economy and environment: Subtractive or additive manufacturing to produce structural components of aircraft. International Journal on Interactive Design & Manufacturing (IjideM), 14(3), 861–873. https://doi.org/10.1007/S12008-020-00663-Y

 Google Scholar

Dohale, V., Gunasekaran, A., Akarte, M., & Verma, P. (2021). An integrated Delphi-MCDM-Bayesian network framework for production system selection. International Journal of Production Economics, 242, 108296. https://doi.org/10.1016/j.ijpe.2021.108296

 Web of Science ®Google Scholar

Simeone, A., Zeng, Y., & Caggiano, A. (2021). Intelligent decision-making support system for manufacturing solution recommendation in a cloud framework. The International Journal of Advanced Manufacturing Technology, 112(3–4), 1035–1050. https://doi.org/10.1007/s00170-020-06389-1

 Web of Science ®Google Scholar

Zhao, C., Dinar, M., & Melkote, S. N. (2022). A data-driven framework for learning the capability of manufacturing process sequences. Journal of Manufacturing Systems, 64, 68–80. https://doi.org/10.1016/J.JMSY.2022.05.009

 Web of Science ®Google Scholar

Zhang, Y., & Fiona, Z. Y. (2022). A Web-based automated manufacturability analyzer and recommender for additive manufacturing (MAR-AM) via a hybrid machine learning model. Expert Systems with Applications, 199, 117189. https://doi.org/10.1016/J.ESWA.2022.117189

 Web of Science ®Google Scholar

Saxena, A., & Saad, A. (2007). Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing, 7(1), 441–454. https://doi.org/10.1016/J.ASOC.2005.10.001

 Web of Science ®Google Scholar

Shao, C., Paynabar, K., Kim, T. H., Jin, J., Hu, S. J., Spicer, J. P., Wang, H., & Abell, J. A. (2013). Feature selection for manufacturing process monitoring using cross-validation. Journal of Manufacturing Systems, 32(4), 550–555. https://doi.org/10.1016/J.JMSY.2013.05.006

 Web of Science ®Google Scholar

Grasso, M., Laguzza, V., Semeraro, Q., & Colosimo, B. M. (2017). In-process monitoring of selective laser melting: Spatial detection of defects via image data analysis. Journal of Manufacturing Science and Engineering, 139(5). https://doi.org/10.1115/1.4034715

 Web of Science ®Google Scholar

Das, B., Pal, S., & Bag, S. (2017). Torque based defect detection and weld quality modelling in friction stir welding process. Journal of Manufacturing Processes, 27, 8–17. https://doi.org/10.1016/J.JMAPRO.2017.03.012

 Web of Science ®Google Scholar

Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71–77. https://doi.org/10.1016/J.JMSY.2018.04.008

 Web of Science ®Google Scholar

Godreau, V., Ritou, M., Chové, E., Furet, B., & Dumur, D. (2019). Continuous improvement of HSM process by data mining. Journal of Intelligent Manufacturing, 30(7), 2781–2788. https://doi.org/10.1007/S10845-018-1426-7

 Web of Science ®Google Scholar

Okaro, I. A., Jayasinghe, S., Sutcliffe, C., Black, K., Paoletti, P., & Green, P. L. (2019). Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Additive Manufacturing, 27, 42–53. https://doi.org/10.1016/J.ADDMA.2019.01.006

 Web of Science ®Google Scholar

Zhang, B., Liu, S., & Shin, Y. C. (2019). In-process monitoring of porosity during laser additive manufacturing process. Additive Manufacturing, 28, 497–505. https://doi.org/10.1016/J.ADDMA.2019.05.030

 Web of Science ®Google Scholar

Martínez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104(9–12), 3647–3662. https://doi.org/10.1007/S00170-019-04090-6

 Web of Science ®Google Scholar

Pratama, M., Dimla, E., Tjahjowidodo, T., Pedrycz, W., & Lughofer, E. (2020). Online tool condition monitoring based on parsimonious ensemble+. IEEE Transactions on Cybernetics, 50(2), 664–677. https://doi.org/10.1109/TCYB.2018.2871120

 PubMed Web of Science ®Google Scholar

Nasir, V., & Cool, J. (2020). Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection. The International Journal of Advanced Manufacturing Technology, 108(5–6), 1811–1825. https://doi.org/10.1007/S00170-020-05505-5

 Web of Science ®Google Scholar

Song, K., Wang, M., Liu, L., Wang, C., Zan, T., & Yang, B. (2020). Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal. The International Journal of Advanced Manufacturing Technology, 109(3–4), 929–942. https://doi.org/10.1007/S00170-020-05587-1

 Web of Science ®Google Scholar

Gülçür, M., & Whiteside, B. (2021). A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and industry 4.0 applications. The International Journal of Advanced Manufacturing Technology, 115(5–6), 1943–1954. https://doi.org/10.1007/S00170-021-07252-7

 Web of Science ®Google Scholar

Xie, Y., Zhang, C., & Liu, Q. (2021). Tool wear status recognition and prediction model of milling cutter based on deep learning. IEEE Access, 9, 1616–1625. https://doi.org/10.1109/ACCESS.2020.3047205

 Google Scholar

Kubik, C., Knauer, S. M., & Groche, P. (2021). Smart sheet metal forming: Importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking. Journal of Intelligent Manufacturing, 33(1), 259–282. https://doi.org/10.1007/S10845-021-01789-W

 Web of Science ®Google Scholar

Sandru, E. D., David, E., Kovacs, I., Buzo, A., Burileanu, C., & Pelz, G. (2022). Modeling the dependency of analog circuit performance parameters on manufacturing process variations with applications in sensitivity analysis and yield prediction. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(1), 129–142. https://doi.org/10.1109/TCAD.2021.3054804

 Web of Science ®Google Scholar

Mei, S., Yuan, M., Cui, J., Dong, S., & Zhao, J. (2022). Machinery condition monitoring in the era of industry 4.0: A relative degree of contribution feature selection and deep residual network combined approach. Computers & Industrial Engineering, 168, 108129. https://doi.org/10.1016/J.CIE.2022.108129

 Web of Science ®Google Scholar

Sun, I. C., Cheng, R. C., & Chen, K. S. (2022). Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis. The International Journal of Advanced Manufacturing Technology, 119(9–10), 6451–6468. https://doi.org/10.1007/S00170-021-08526-W

 Web of Science ®Google Scholar

Mahmood, J., Luo, M., & Rehman, M. (2022). An accurate detection of tool wear type in drilling process by applying PCA and one-hot encoding to SSA-BLSTM model. The International Journal of Advanced Manufacturing Technology, 118(11–12), 3897–3916. https://doi.org/10.1007/s00170-021-08200-1

 Web of Science ®Google Scholar

Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38(3), 1876–1886. https://doi.org/10.1016/J.ESWA.2010.07.119

 Web of Science ®Google Scholar

Lieber, D., Stolpe, M., Konrad, B., Deuse, J., & Morik, K. (2013). Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. Procedia CIRP, 7, 193–198. https://doi.org/10.1016/J.PROCIR.2013.05.033

 Google Scholar

Escobar, C. A., & Morales-Menendez, R. (2018). Machine learning techniques for quality control in high conformance manufacturing environment. Advances in Mechanical Engineering, 10(2), 10. https://doi.org/10.1177/1687814018755519

 Web of Science ®Google Scholar

Wu, D., Wei, Y., & Terpenny, J. (2018). Predictive modelling of surface roughness in fused deposition modelling using data fusion. International Journal of Production Research, 57(12), 3992–4006. https://doi.org/10.1080/00207543.2018.1505058

 Web of Science ®Google Scholar

Scime, L., & Beuth, J. (2018). A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 24, 273–286. https://doi.org/10.1016/J.ADDMA.2018.09.034

 Web of Science ®Google Scholar

Peres, R. S., Barata, J., Leitao, P., & Garcia, G. (2019). Multistage quality control using machine learning in the automotive industry. IEEE Access, 7, 79908–79916. https://doi.org/10.1109/ACCESS.2019.2923405

 Web of Science ®Google Scholar

Hanhirova, J., Harjuhahto, J., Harjuhahto, J., & Hirvisalo, V. (2019). A machine learning based quality control system for power cable manufacturing. IEEE International Conference on Industrial Informatics (INDIN), 2019-July, 193–198. https://doi.org/10.1109/INDIN41052.2019.8972281

 Google Scholar

Mahmoudi, M., Ezzat, A. A., & Elwany, A. (2019). Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing. Journal of Manufacturing Science and Engineering, 141(3). https://doi.org/10.1115/1.4042108

 Web of Science ®Google Scholar

Papananias, M., McLeay, T. E., Obajemu, O., Mahfouf, M., & Kadirkamanathan, V. (2020). Inspection by exception: A new machine learning-based approach for multistage manufacturing. Applied Soft Computing, 97, 106787. https://doi.org/10.1016/J.ASOC.2020.106787

 Web of Science ®Google Scholar

Zhang, Z., Ren, W., Yang, Z., & Wen, G. (2020). Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning. Measurement, 156, 107546. https://doi.org/10.1016/J.MEASUREMENT.2020.107546

 Web of Science ®Google Scholar

Cho, E., Jun, J. H., Chang, T. W., & Choi, Y. (2020). Quality prediction modeling of plastic extrusion process. ICIC Express Letters, Part B: Applications, 11(5), 447–. https://doi.org/10.24507/ICICELB.11.05.447

 Google Scholar

Finkeldey, F., Volke, J., Zarges, J. C., Heim, H. P., & Wiederkehr, P. (2020). Learning quality characteristics for plastic injection molding processes using a combination of simulated and measured data. Journal of Manufacturing Processes, 60, 134–143. https://doi.org/10.1016/J.JMAPRO.2020.10.028

 Web of Science ®Google Scholar

Bauhofer, A., & Daraio, C. (2020). Neural networks for trajectory evaluation in direct laser writing. The International Journal of Advanced Manufacturing Technology, 107(5–6), 2563–2577. https://doi.org/10.1007/S00170-020-05086-3

 Web of Science ®Google Scholar

Wang, T., Qiao, M., Zhang, M., Yang, Y., & Snoussi, H. (2020). Data-driven prognostic method based on self-supervised learning approaches for fault detection. Journal of Intelligent Manufacturing, 31(7), 1611–1619. https://doi.org/10.1007/S10845-018-1431-X

 Web of Science ®Google Scholar

Zhang, X., Kano, M., Tani, M., Mori, J., Ise, J., & Harada, K. (2020). Prediction and causal analysis of defects in steel products: Handling nonnegative and highly overdispersed count data. Control Engineering Practice, 95, 104258. https://doi.org/10.1016/J.CONENGPRAC.2019.104258

 Web of Science ®Google Scholar

Denkena, B., Dittrich, M.-A., Nguyen, H. N., & Bild, K. (2021). Self-optimizing process planning of multi-step polishing processes. Production Engineering, 15(3–4), 563–571. https://doi.org/10.1007/s11740-021-01042-6

 Google Scholar

Leco, M., & Kadirkamanathan, V. (2021). A perturbation signal based data-driven Gaussian process regression model for in-process part quality prediction in robotic countersinking operations. Robotics and Computer-Integrated Manufacturing, 71, 102105. https://doi.org/10.1016/J.RCIM.2020.102105

 Web of Science ®Google Scholar

Bak, C., Roy, A. G., & Son, H. (2021). Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique. CIRP Journal of Manufacturing Science and Technology, 33, 327–338. https://doi.org/10.1016/J.CIRPJ.2021.04.001

 Web of Science ®Google Scholar

Guo, W., Wu, C., Ding, Z., & Zhou, Q. (2021). Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding. The International Journal of Advanced Manufacturing Technology, 112(9–10), 2853–2871. https://doi.org/10.1007/S00170-020-06523-Z

 Web of Science ®Google Scholar

Yan, H., Sergin, N. D., Brenneman, W. A., Lange, S. J., & Ba, S. (2021). Deep multistage multi-task learning for quality prediction of multistage manufacturing systems. Journal of Quality Technology, 53(5), 526–544. https://doi.org/10.1080/00224065.2021.1903822

 Web of Science ®Google Scholar

Charalampous, P., Kostavelis, I., Kontodina, T., & Tzovaras, D. (2021). Learning-based error modeling in FDM 3D printing process. Rapid Prototyping Journal, 27(3), 507–517. https://doi.org/10.1108/RPJ-03-2020-0046

 Web of Science ®Google Scholar

Zhang, X., Hou, T., Hao, Y., Shangguan, H., Wang, A., & Peng, S. (2021). Surface defect detection of solar cells based on multiscale region proposal fusion network. IEEE Access, 9, 62093–62101. https://doi.org/10.1109/ACCESS.2021.3074219

 Web of Science ®Google Scholar

Kahng, H., & Kim, S. B. (2021). Self-supervised representation learning for wafer bin map defect pattern classification. IEEE Transactions on Semiconductor Manufacturing, 34(1), 74–86. https://doi.org/10.1109/TSM.2020.3038165

 Web of Science ®Google Scholar

Reséndiz-Flores, E. O., Navarro-Acosta, J. A., & García-Calvillo, I. D. (2022). Smart fault detection and optimal variables identification using kernel mahalanobis distance for industrial manufacturing processes. International Journal of Computer Integrated Manufacturing, 35(9), 942–950. https://doi.org/10.1080/0951192X.2022.2027019

 Web of Science ®Google Scholar

Tian, W., Zhao, F., Min, C., Feng, X., Liu, R., Mei, X., & Chen, G. (2022). Broad learning system based on binary grey wolf optimization for surface roughness prediction in slot milling. IEEE Transactions on Instrumentation and Measurement, 71, 1–10. https://doi.org/10.1109/TIM.2022.3144232

 Web of Science ®Google Scholar

Guo, S., & Guo, W. (2022). Process monitoring and fault prediction in multivariate time series using bag-of-words. IEEE Transactions on Automation Science and Engineering, 19(1), 230–242. https://doi.org/10.1109/TASE.2020.3026065

 Web of Science ®Google Scholar

Xu, R., Hao, R., & Huang, B. (2022). Efficient surface defect detection using self-supervised learning strategy and segmentation network. Advanced Engineering Informatics, 52, 101566. https://doi.org/10.1016/J.AEI.2022.101566

 Web of Science ®Google Scholar

Liu, Z., Song, Y., Tang, R., Duan, G., & Tan, J. (2022). Few-shot defect recognition of metal surfaces via attention-embedding and self-supervised learning. Journal of Intelligent Manufacturing, 2022, 1–15. https://doi.org/10.1007/S10845-022-02022-Y

 Google Scholar

Link, P., Poursanidis, M., Schmid, J., Zache, R., von Kurnatowski, M., Teicher, U., & Ihlenfeldt, S. (2022). Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing. Journal of Intelligent Manufacturing, 33(7), 2129–2142. https://doi.org/10.1007/S10845-022-01975-4

 Web of Science ®Google Scholar

Manivannan, S. (2022). An ensemble-based deep semi-supervised learning for the classification of wafer bin maps defect patterns. Computers & Industrial Engineering, 172, 108614. https://doi.org/10.1016/J.CIE.2022.108614

 Web of Science ®Google Scholar

Chu, M. S., Park, S., Jeong, J., Joo, K., Lee, Y., & Kang, J. (2022). Recognition of unknown wafer defect via optimal bin embedding technique. The International Journal of Advanced Manufacturing Technology, 121(5–6), 3439–3451. https://doi.org/10.1007/S00170-022-09447-Y

 Web of Science ®Google Scholar

Wang, J., Cheng, X., Gao, Y., Wang, X., & Yang, J. (2022). Cutting force embedded manifold learning for condition monitoring of vertical machining center. IEEE Transactions on Instrumentation and Measurement, 71, 1–12. https://doi.org/10.1109/TIM.2022.3216413

 Web of Science ®Google Scholar