References
- Acatech. (2015). Acatech: Umsetzungsstrategie Industrie 4.0 Ergebnisbericht der Plattform Industrie 4.0. www.vdma.org.
- Aiman, M., Bahrin, K., Othman, F., Hayati, N., Azli, N., & Talib, F. (2016). Industry 4.0: A review on industrial automation and robotic (Vol. 78). www.jurnalteknologi.utm.my.
- American Society for Quality. (n.d.). What is statistical process control?
- Ansari, F., Khobreh, M., Seidenberg, U., & Sihn, W. (2018). A problem-solving ontology for human-centered cyber physical production systems. CIRP Journal of Manufacturing Science and Technology, 22, 91–106. https://doi.org/10.1016/j.cirpj.2018.06.002
- ASQ. (n.d.). QUALITY GLOSSARY. Retrieved November 16, 2022, from https://asq.org/quality-resources/quality-glossary/q.
- Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598
- Bagheri, B., Yang, S., Kao, H. A., & Lee, J. (2015). Cyber-physical systems architecture for self-aware machines in industry 4.0 environment. IFAC-PapersOnLine, 48(3), 1622–1627. https://doi.org/10.1016/j.ifacol.2015.06.318
- Baheti, R., Gill, H., Lee, J., Bagheri, B., & Kao, H. A. (2015). Cyber-physical Systems. Manufacturing Letters, 3(October 2017).
- Bai, Y., Li, C., Sun, Z., & Chen, H. (2017). Deep neural network for manufacturing quality prediction. 2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings, https://doi.org/10.1109/PHM.2017.8079165
- Bai, Y., Xie, J., Wang, D., Zhang, W., & Li, C. (2021). A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge. Computers & Industrial Engineering, 155, 107227. https://doi.org/10.1016/j.cie.2021.107227
- 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
- Behnke, M., Guo, S., & Guo, W. (2021). Comparison of early stopping neural network and random forest for in-situ quality prediction in laser based additive manufacturing. Procedia Manufacturing, 53, 656–663. https://doi.org/10.1016/j.promfg.2021.06.065
- Bensalem, S., Gallien, M., Ingrand, F., Kahloul, I., & Thanh-Hung, N. (2009). Designing autonomous robots: Toward a more dependable software architecture. IEEE Robotics & Automation Magazine, 16, https://doi.org/10.1109/MRA.2008.931631
- Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, 114820. https://doi.org/10.1016/j.eswa.2021.114820
- Burggräf, P., Wagner, J., Heinbach, B., Steinberg, F., Pérez, M. A. R., Schmallenbach, L., Garcke, J., Steffes-Lai, D., & Wolter, M. (2021). Predictive analytics in quality assurance for assembly processes: Lessons learned from a case study at an industry 4.0 demonstration cell. Procedia CIRP, 104, 641–646. https://doi.org/10.1016/j.procir.2021.11.108
- Bustillo, A., Urbikain, G., Perez, J. M., Pereira, O. M., & Lopez de Lacalle, L. N. (2018). Smart optimization of a friction-drilling process based on boosting ensembles. Journal of Manufacturing Systems, 48, 108–121. https://doi.org/10.1016/j.jmsy.2018.06.004
- Caiazzo, B., di Nardo, M., Murino, T., Petrillo, A., Piccirillo, G., & Santini, S. (2022). Towards Zero Defect Manufacturing paradigm: A review of the state-of-the-art methods and open challenges. Computers in Industry, 134, 103548. https://doi.org/10.1016/j.compind.2021.103548
- Chhor, J., Gerdhenrichs, S., & Schmitt, R. H. (2021). Predictive quality for hypoid gear in drive assembly. Procedia CIRP, 104, 702–707. https://doi.org/10.1016/j.procir.2021.11.118
- Colosimo, B. M., del Castillo, E., Jones-Farmer, L. A., & Paynabar, K. (2021). Artificial intelligence and statistics for quality technology: An introduction to the special issue. Journal of Quality Technology, 53(5), 443–453. https://doi.org/10.1080/00224065.2021.1987806
- Connor, P. D. T. O. (1986). Quality, productivity and competitive position, W. Edwards Deming, Massachusetts Institute of Technology. Center for Advanced Engineering Study, 1982. No. of pages: 373. Quality and Reliability Engineering International, 2(4), https://doi.org/10.1002/qre.4680020421
- Davis, S. (1997). Future perfect: Tenth Anniversary Edition.
- Dengler, S., Lahriri, S., Trunzer, E., & Vogel-Heuser, B. (2021). Applied machine learning for a zero defect tolerance system in the automated assembly of pharmaceutical devices. Decision Support Systems, 146), https://doi.org/10.1016/j.dss.2021.113540
- Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. Expert Systems with Applications, 166, 114060. https://doi.org/10.1016/j.eswa.2020.114060
- Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenario-based approach to learning for the future of production. Procedia CIRP, 54, 13–18. https://doi.org/10.1016/j.procir.2016.03.162
- Fu, Y., Downey, A. R. J., Yuan, L., Zhang, T., Pratt, A., & Balogun, Y. (2022). Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, 75, 693–710. https://doi.org/10.1016/j.jmapro.2021.12.061
- Gejji, A., Shukla, S., Pimparkar, S., Pattharwala, T., & Bewoor, A. (2020). Using a support vector machine for building a quality prediction model for center-less honing process. Procedia Manufacturing, 46, 600–607. https://doi.org/10.1016/j.promfg.2020.03.086
- Godfrey, A. B. (1986). Report: The history and evolution of quality in AT&T. AT&T Technical Journal, 65(2), 9–20. https://doi.org/10.1002/j.1538-7305.1986.tb00289.x
- Godfrey, A. B., & Kenett, R. S. (2007). Joseph M. Juran, a perspective on past contributions and future impact. Quality and Reliability Engineering International, 23(6), 653–663. https://doi.org/10.1002/qre.861
- Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
- Gyasi, E. A., Kah, P., Penttilä, S., Ratava, J., Handroos, H., & Sanbao, L. (2019). Digitalized automated welding systems for weld quality predictions and reliability. Procedia Manufacturing, 38, 133–141. https://doi.org/10.1016/j.promfg.2020.01.018
- Institute of Electrical and Electronics Engineers. (n.d.). 2019 Ieee Workshop on Metrology for Industry 4.0 and Internet of Things : proceedings : Naples, Italy, June 4-6, 2019. “Advanced Process Defect Monitoring Model and Prediction Improvement by Artificial Neural Network in Kitchen Manufacturing Industry: a Case of Study.”.
- Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. Proceedings of 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2014. https://doi.org/10.1109/AQTR.2014.6857843.
- Juran, J. M. (1986). The Quality Trilogy A Universal Approach to Managing for Quality.
- Kagermann, H., Wahlster, W., Helbig, J., Hellinger, A., Stumpf, M. A. V., Treugut, L., Blasco, J., Galloway, H., & Findeklee, U. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0.
- Kalpande, S. D., & Toke, L. K. (2021). Assessment of green supply chain management practices, performance, pressure and barriers amongst Indian manufacturer to achieve sustainable development. International Journal of Productivity and Performance Management, 70(8), 2237–2257. https://doi.org/10.1108/IJPPM-02-2020-0045
- Kang, Z., Catal, C., & Tekinerdogan, B. (2020). Machine learning applications in production lines: A systematic literature review. Computers and Industrial Engineering, 149, 106773. https://doi.org/10.1016/j.cie.2020.106773
- Krauß, J., Pacheco, B. M., Zang, H. M., & Schmitt, R. H. (2020). Automated machine learning for predictive quality in production. Procedia CIRP, 93, 443–448. https://doi.org/10.1016/j.procir.2020.04.039
- Landherr, M., Schneider, U., & Bauernhansl, T. (2016). The application center industrie 4.0 - industry-driven manufacturing, research and development. Procedia CIRP, 57, 26–31. https://doi.org/10.1016/j.procir.2016.11.006
- Lee, K. B., Cheon, S., & Kim, C. O. (2017). A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 30(2), 135–142. https://doi.org/10.1109/TSM.2017.2676245
- Lindemann, B., Fesenmayr, F., Jazdi, N., & Weyrich, M. (2019). Anomaly detection in discrete manufacturing using self-learning approaches. Procedia CIRP, 79, 313–318. https://doi.org/10.1016/j.procir.2019.02.073
- Liu, C., Tian, W., & Kan, C. (2022). When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development. Journal of Manufacturing Systems, https://doi.org/10.1016/j.jmsy.2022.04.010
- Luo, M., Yan, H. C., Hu, B., Zhou, J. H., & Pang, C. K. (2015). A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Computers & Industrial Engineering, 85, 414–422. https://doi.org/10.1016/j.cie.2015.04.008
- Marilungo, E., Papetti, A., Germani, M., & Peruzzini, M. (2017). From PSS to CPS design: A real industrial use case toward industry 4.0. Procedia CIRP, 64, 357–362. https://doi.org/10.1016/j.procir.2017.03.007
- Martinez, P., Al-Hussein, M., & Ahmad, R. (2022). A cyber-physical system approach to zero-defect manufacturing in light-gauge steel frame assemblies. Procedia Computer Science, 200, 924–933. https://doi.org/10.1016/j.procs.2022.01.290
- Mohammadi, P., & Wang, Z. J. (2016). Machine learning for quality prediction in abrasion-resistant material manufacturing process. Canadian Conference on Electrical and Computer Engineering, 2016-October. https://doi.org/10.1109/CCECE.2016.7726783.
- Mozaffar, M., Liao, S., Xie, X., Saha, S., Park, C., Cao, J., Liu, W. K., & Gan, Z. (2022). Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives. Journal of Materials Processing Technology, 302, 117485. https://doi.org/10.1016/j.jmatprotec.2021.117485
- Nazarenko, A. A., Sarraipa, J., Camarinha-Matos, L. M., Grunewald, C., Dorchain, M., & Jardim-Goncalves, R. (2021). Analysis of relevant standards for industrial systems to support zero defects manufacturing process. Journal of Industrial Information Integration, 23, 100214. https://doi.org/10.1016/j.jii.2021.100214
- Pang, J., Zhang, N., Xiao, Q., Qi, F., & Xue, X. (2021). A new intelligent and data-driven product quality control system of industrial valve manufacturing process in CPS. Computer Communications, 175, 25–34. https://doi.org/10.1016/j.comcom.2021.04.022
- Petri, K. I., Billo, R. E., & Biclanda, B. (1998). A neural network process model for abrasive flow machining operations. Journal of Manufacturing Systems, 17(1), 52–64. https://doi.org/10.1016/S0278-6125(98)80009-5
- Powell, D., Magnanini, M. C., Colledani, M., & Myklebust, O. (2022). Advancing zero defect manufacturing: A state-of-the-art perspective and future research directions. Computers in Industry, 136, 103596. https://doi.org/10.1016/j.compind.2021.103596
- Psarommatis, F., May, G., Dreyfus, P. A., & Kiritsis, D. (2020). Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research. International Journal of Production Research, 58(1), 1–17. https://doi.org/10.1080/00207543.2019.1605228
- Rifkin, J. (2011). The Third Industrial Revolution : How Lateral Power is Transforming Energy, the Economy and the World.
- Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0 The Future of Productivity and Growth in Manufacturing Industries.
- Saadallah, A., Abdulaaty, O., Büscher, J., Panusch, T., Morik, K., & Deuse, J. (2022). Early quality prediction using deep learning on time series sensor data. Procedia CIRP, 107, 611–616. https://doi.org/10.1016/j.procir.2022.05.034
- Sahoo, S., & Lo, C.-Y. (2022). Smart manufacturing powered by recent technological advancements: A review. Journal of Manufacturing Systems, 64, 236–250. https://doi.org/10.1016/j.jmsy.2022.06.008
- Schorr, S., Möller, M., Heib, J., & Bähre, D. (2020). Comparison of machine learning methods for quality prediction of drilled and reamed bores based on NC-internal signals. Procedia CIRP, 101, 77–80. https://doi.org/10.1016/j.procir.2020.09.190
- Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J. P. (2014). Collaboration mechanisms to increase productivity in the context of industrie 4.0. Procedia CIRP, 19(C), 51–56. https://doi.org/10.1016/j.procir.2014.05.016
- Shewhart, W. A. (1926). Correction of Data for Errors of Measurement.
- Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., & Cesarotti, V. (2020). Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry, 123, https://doi.org/10.1016/j.compind.2020.103335
- Stock, S., Pohlmann, S., Günter, F. J., Hille, L., Hagemeister, J., & Reinhart, G. (2022). Early quality classification and prediction of battery cycle life in production using machine learning. Journal of Energy Storage, 50, https://doi.org/10.1016/j.est.2022.104144
- Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, 40, 536–541. https://doi.org/10.1016/j.procir.2016.01.129
- Su, T. J., Chen, Y. F., Cheng, J. C., & Chiu, C. L. (2018). An artificial neural network approach for wafer dicing saw quality prediction. Microelectronics Reliability, 91, 257–261. https://doi.org/10.1016/j.microrel.2018.10.013
- Taguchi, G. (1987). Systems of experimental design (D. Clausing, Ed.; Vols. 1–2). UNIPUB/Kraus International Publications.
- Tannock, J. D. T. (1992). Automating quality systems. In Automating quality systems. Springer Netherlands. https://doi.org/10.1007/978-94-011-2366-2
- Toke, L. K., & Kalpande, S. D. (2020). Total quality management in small and medium enterprises: An overview in Indian context. Quality Management Journal, 27(3), 159–175. https://doi.org/10.1080/10686967.2020.1767008
- Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 - A Glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034
- Vinitha, K., Ambrose Prabhu, R., Bhaskar, R., & Hariharan, R. (2020). Review on industrial mathematics and materials at Industry 1.0 to Industry 4.0. Materials Today: Proceedings, 33, 3956–3960. https://doi.org/10.1016/j.matpr.2020.06.331
- Vishnu, V. S., Varghese, K. G., & Gurumoorthy, B. (2021). A data-driven digital twin of CNC machining processes for predicting surface roughness. Procedia CIRP, 104, 1065–1070. https://doi.org/10.1016/j.procir.2021.11.179
- Vrabel, M., Maňková, I., & Beňo, J. (2016). Monitoring and control of manufacturing process to assist the surface workpiece quality when drilling. Procedia CIRP, 41, 735–739. https://doi.org/10.1016/j.procir.2015.12.092
- Wang, G., Ledwoch, A., Hasani, R. M., Grosu, R., & Brintrup, A. (2019). A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing, 85, 105683. https://doi.org/10.1016/j.asoc.2019.105683
- Wang, Q., Jiao, W., Wang, P., & Zhang, Y. M. (2021). A tutorial on deep learning-based data analytics in manufacturing through a welding case study. Journal of Manufacturing Processes, 63, 2–13. https://doi.org/10.1016/j.jmapro.2020.04.044
- Warnecke, G., & Kluge, R. (1998). Control of tolerances in turning by predictive control with neural networks.
- Weese, M., Martinez, W., Megahed, F. M., & Jones-Farmer, L. A. (2016). Statistical learning methods applied to process monitoring: An overview and perspective. Journal of Quality Technology, 48(1), 4–24. https://doi.org/10.1080/00224065.2016.11918148
- Witkowski, K. (2017). Internet of things, Big data, industry 4.0 - innovative solutions in logistics and supply chains management. Procedia Engineering, 182, 763–769. https://doi.org/10.1016/j.proeng.2017.03.197
- Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517
- Zhang, J., Wang, P., & Gao, R. X. (2020). Attention mechanism-incorporated deep learning for AM part quality prediction. Procedia CIRP, 93, 96–101. https://doi.org/10.1016/j.procir.2020.04.051
- Zolotová, I., Papcun, P., Kajáti, E., Miškuf, M., & Mocnej, J. (2020). Smart and cognitive solutions for Operator 4.0: Laboratory H-CPPS case studies. Computers & Industrial Engineering, 139, 105471. https://doi.org/10.1016/j.cie.2018.10.032
- Zonnenshain, A., & Kenett, R. S. (2020). Quality 4.0—the challenging future of quality engineering. Quality Engineering, 32(4), 614–626. https://doi.org/10.1080/08982112.2019.1706744
- Zorriassatine, F., & Tannock, J. D. T. (1997). A review of neural networks for statistical process control.