Abstract
This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.
Highlights
The Fourth Industrial Revolution has enhanced the application of Machine Learning (ML), improving the machining capabilities and reducing the production costs
This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes
This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring
Summary
The Fourth Industrial Revolution has enhanced the application of Machine Learning (ML), improving the machining capabilities and reducing the production costs. Okafor et al [3] in 1995 used an ANN based on time domain features from acoustic emission, vibration, cutting force and time signals in milling for surface roughness and bore tolerance prediction These were some of the first articles that showed the application of ML in machining; these were sparse and limited due to the low computational capacity in their times. The well-known distributions, such as Gaussian, Log-normal, Exponential and Weibull, are the mathematical techniques applied as the lives of tools are explored, and the optimization of the parameters is requested [7] These models are based on the inner-relations searches between the tool wear factor and process parameters or surface roughness, with the aim of optimizing the tool life. Other models differ on the process parameters and signal features, and an optimization step is required for Cox and the selected distribution parameters [9]
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