Production & Manufacturing Research· 2016Q1· Review
Machine learning in manufacturing: advantages, challenges, and applications
- 1,265citations
- Q1SCImago
- 2016year
Short summary
Machine learning offers solutions to complex manufacturing challenges, but its broadness hinders adoption; this paper structures the field and highlights successful applications.
AI-generated from the title and abstract; the full text is not read.
Key points
- Manufacturing systems face increasing complexity and dynamism.
- Machine learning offers potential solutions to these challenges.
- The broadness and complexity of ML hinder its practical application in manufacturing.
- This paper structures the ML field and provides examples of successful manufacturing applications.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Production & Manufacturing Research, 2016 · DOI ↗
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Field: Industrial and Manufacturing Engineering
Industrial and Manufacturing EngineeringEngineering