Introduction
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.

Suitability of machine learning application with regard to today’s manufacturing challenges


Before looking into the suitability of machine learning (ML) based on the previously derived requirements toward a future solution approach, the used terms are briefly introduced. ML is known for its ability to handle many problems of NP-complete nature, which often appear in the domain of smart manufacturingThe application of ML techniques increased over the last two decades due to various factors,

                                    e.g. the availability of large amounts of complex data with little transparency and the increased usability and power of available ML toolsNevertheless, the main definition of ML, allowing computers to solve problems without being specifically programmed to do sois still valid today. ML is connected to other terms, like DM, KD, AI, and others  Today, ML is already widely applied in different areas of manufacturing, e.g. optimization, control, and troubleshooting