Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0

Dear Colleagues,

Artificial intelligence (AI) makes the core of the Industry 4.0 revolution. AI, especially subset machine learning (ML), has been advancing the mechanical engineering area. In particular, ML could help fine-tune product quality and optimize operations during the manufacturing process for improving product quality and reducing time to market. In addition, ML-based predictive failure enables optimal maintenance time, which saves cost and time. Furthermore, optimal control incorporated with reinforcement learning plays a key role in scheduling in production, supply chain, and Industry 4.0 systems. In the meantime, stakeholders achieve optimal product management through novel optimization architectures enabled by ML surrogate modeling. In summary, ML, optimal control, and optimization together have been pushing forward the leading edge in manufacturing and Industry 4.0.

This Special Issue on “Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0” targets original and novel research products on ML, control, and optimization, with application emphasis on practical mechanical engineering problems.

Topics include, but are not limited to:

1. Novel ML algorithm development demonstrated on mechanical engineering problems (including manufacturing, aerospace engineering, etc.).
2. State-of-the-art ML methods introduced for large-scale optimal control or practical mechanical engineering applications.
3. Challenging analysis or design under uncertainty for mechanical engineering problems through ML methods.

Dr. Xiaosong Du
Guest Editor

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