Machine learning in manufacturing: advantages, challenges, and applications

By Thorsten Wuest, Daniel Weimer, Christopher Irgens, Klaus-Dieter Thoben. 2016

1,205 citations recorded by the source corpus.

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.

Subfields: Industrial and Manufacturing Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9d46dc975aeed3f96bddb144079b50238f746ecd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Fernando Antonio Forcellini
2. Dmitry A. Ivanov
3. Ian M. Cavalcante
4. Enzo Morosini Frazzon

In Statistics and Probability

1. Thomas Zimmermann
2. Stefan Wrobel
3. Christian Bird
4. R. Deline
5. Saleema Amershi
6. Dmitry A. Ivanov
7. Stefan Rüping

Citations in the corpus

• A review of machine learning for the optimization of production processes (2019, background): “Thus, it remains a hot topic in latest research, starting with the development of scientific methods for sensor placement, going through data storage and processing architectures, and ending with machine learning and optimization algorithms [118].”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, neutral): “, LR, SVM, DT, and RF) [56], [57].”
• Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, methodology): “• Unsupervised learning where there is no feedback provided from anyone and the algorithm finds patterns in unknown data sets (clustering, association rules, self-organised maps) [15,19,21] and so, unlabeled data are used for training purposes.”
• Software Engineering for Machine Learning: A Case Study (2019, background): “However, ongoing research and surveys show that engineers still struggle to operationalize and standardize working processes [9], [24], [23].”
• A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “Techniques involving supervised machine learning (SML) have already become powerful tools with various applications within intelligent manufacturing systems [25, 26].”
• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “However, compared with the successes of ML in speci fi c applications of process monitoring, optimization, and PHM, utilization is limited at the system level of decision-making [68].”
• Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, methodology): “Moreover, as stated by RL, problems differ from supervised learning, as they can be described by the absence of labeled examples of “good” and “bad” behavior [23].”