Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques : a review

• Main Authors: Pandiyan, Vigneashwara, Shevchik, Sergey, Wasmer, Kilian, Castagne, Sylvie, Tjahjowidodo, Tegoeh
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Mechanical engineering; Abrasive Machining; Machine Learning
• Online Access: https://hdl.handle.net/10356/143497
• Institution: Nanyang Technological University

Abrasive finishing processes such as grinding, lapping or disc polishing are one of the most practical means for processing materials to manufacture products with fine surface finish, surface quality and dimensional accuracy. However, they are one of the most difficult and least-understood processes for two main reasons. Firstly, the abrasive grains present in the tool surface are randomly oriented. Secondly, they undergo complex interactions in the machining zone. Given the advances in sensor technologies, the finishing processes can now be sensorized, and the vast amount of data produced can be exploited to model and monitor the processes using Artificial Intelligence techniques. Data-driven models have turned into a hot focus in engineering with the rise of machine learning and deep learning algorithms, which have greatly spread all through the academic community. The scope of this paper is mainly to review the application of Artificial Intelligence as well as supporting sensing and signal processing techniques in modelling and monitoring on different types of abrasive processes in metal finishing. The paper gives a detailed background on the key mechanisms and defects in the different abrasive finishing process and lists the suitable sensing techniques for their monitoring. The paper reports that most of the Artificial Intelligence algorithms available are not fully exploited for monitoring and modelling in abrasive finishing and emphasizes on bridging this gap. The probable research tendency on data-driven monitoring and modelling for abrasive finishing is also forecasted.