| Title | Machine learning in cutting processes as enabler for smart sustainable manufacturing |
|---|---|
| ID_Doc | 17487 |
| Authors | du Preez, A; Oosthuizen, GA |
| Title | Machine learning in cutting processes as enabler for smart sustainable manufacturing |
| Year | 2019 |
| Published | |
| Abstract | Machine learning is becoming an increasingly popular concept in the modern world since its most common goal is to optimize systems by allowing one to make smarter use of products and services. In the manufacturing industry machine learning can lead to cost savings, time savings, increased quality and waste reduction. At the same time, it enables systems to be designed for managing human behaviour. This research study used a systematic review to investigate the different machine learning algorithms within the sustainable manufacturing context. The study focuses specifically on cutting processes. (C) 2019 The Authors. Published by Elsevier B.V. |
| https://doi.org/10.1016/j.promfg.2019.04.102 |
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