Special issue on the application of artificial intelligence in advanced manufacturing

Research in artificial intelligence (AI) and advances in information technologies have resulted in a dramatic paradigm shift in manufacturing. Integration of advanced technologies and the new generation of AI technologies in manufacturing promote the formation of a new generation of intelligent manufacturing.

AI technologies can guide complex processes in an intelligent way. Such technologies have been applied to image recognition, speech recognition, intelligent robot, intelligent driving/automatic driving, fault diagnosis and predictive maintenance, quality monitoring, to name just a few. The role that AI technologies play in manufacturing industry is that of innovating production models and improving production efficiency and product quality. AI technologies can be used not only in traditional manufacturing industries, such as textiles, metallurgy and automobiles, but also in strategic emerging industries, such as high-end equipment manufacturing, robots and new energy. However, the application of AI in advanced manufacturing is still in its infancy, making it a new area with a great chance of identifying new problems and developing solutions to known problems.

This special issue is based on the papers selected from the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM 2019) that was held in Dublin, Ireland in October 2019. The principal aim of this conference is to offer a platform for researchers and practitioners from AI and advanced manufacturing as well as from various application areas to discuss problems and solutions in artificial intelligence and advanced manufacturing, to identify new issues and to shape directions for future research. In AIAM 2019, more than 140 technical papers were presented and approximately one hundred (100) experts, scholars, teachers, students and business representatives from Poland, Canada, Russia, Germany, Netherland, Singapore and other countries participated in it.

The 18 papers included in the special issue address some of the challenges and issues on the application of AI in advanced manufacturing from different perspectives. These papers can be divided into five parts with titles identified below:

• Part 1. Algorithm optimization, to solve the problems of local optimal solutions: two papers.

1.1 A new whale optimization algorithm based on self-adapting parameter adjustment and mix mutation strategy.

1.2 A hybrid algorithm combining genetic algorithm and variable neighborhood search for process sequencing optimization of large-size problem.

• Part 2. Artificial intelligence and factory automation, involving process optimization, risk prediction and cost management: nine papers.

2.1 Design and experiment of electronic seeding system based on response surface method.

2.2 A Novel Adaptive Balance-Drive Mechanism for Industrial Robots Using a Series Elastic Actuator.

2.3 Research on the Optimization of an Angle Grinder Assembly Line Based on Lean Thinking.

2.4 Industry 4.0: Survey from a System Integration Perspective.

2.5 Contextual classification for smart machining based on unsupervised machine learning by Gaussian mixture model.

2.6 Causal Effect Analysis of Logistics Processes Risks in Manufacturing Industries Using Sequential Multi-Stage Fuzzy Cognitive Map: A Case Study.

2.7 Risk-based Life Cycle Cost Analysis using a Two-Level Multi-Objective Genetic Algorithm.

2.8 Holonic Agent-Based Approach for System-Level Remaining Useful Life Estimation with Stochastic Dependence.

2.9 Leather Defect Classification and Segmentation using Deep Learning Architecture.

• Part 3. Application of artificial intelligence in industry, such as fault diagnosis, texture measurements, seismic detection: five papers.

3.1 Fault Diagnosis Method for Disc Slitting Machine Based on Wavelet Packet Transform and Support Vector Machine.

3.2 Artificial Intelligence in Software Engineering and inverse: Review.

3.3 Discriminant Analysis of Mine Quake Type and Intensity Based on Deep Neural Network.

3.4 Automated Test Bed and Real-Time Port Analysis for Reconfigurable Input-Output Boards.

3.5 Angle Analysis of Fabric wrinkle by Projected Profile Light Line Method, Image Processing and Neuro-fuzzy System.

• Part 4. Artificial intelligence and agricultural automation: one paper.

4.1 Research on Vision Navigation and Position System of Agricultural Unmanned Aerial Vehicle.

• Part 5. The use of AI in emotion regulation: one paper.

5.1 A Model for Reappraisal with Personality in Emotion Regulation.

Finally, the guest editors would like to thank the authors of these papers for their contributions and appreciate their time and effort, as well as that of the reviewers, and Professor Stephen T. Newman and Dr Aydin Nassehi plus other editorial and publisher staff for all the background work that made this special issue possible.