Abstract
The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.
Highlights
The manufacturing industry today is experiencing a never seen increase in available data (Chand & Davis, 2010)
Applying machine learning (ML) in manufacturing may result in deriving pattern from existing data-sets, which can provide a basis for the development of approximations about future behavior of the system (Alpaydin, 2010; Nilsson, 2005)
The general advantages of ML have been established in previous sections stating that ML techniques are able to handle NP complete problems which often occur when it comes to optimization problems of intelligent manufacturing systems (Monostori et al, 1998)
Summary
New developments in certain domains like mathematics and computer science (e.g. statistical learning) and availability of easy-to-use, often freely available (software) tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. The field of machine learning is very diverse and many different algorithms, theories, and methods are available. Argue from a manufacturing perspective why machine learning is an appropriate and promising tool for today’s and future challenges;. Introduce the terminology used in the respective fields; present an overview of the different areas of machine learning and propose an overall structuring; provide the reader with a high-level understanding of the advantages and disadvantages of certain methods with respect to manufacturing application. The current challenges manufacturing faces are illustrated. This provides a basis for the later argumentation of machine learning being an appropriate tool to for manufacturers to face those challenges head on
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