Recent Advances on Machine Learning Applications in Machining Processes

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.

Hydrogen Cooling of Turbo Aggregates and the Problem of Rotor Shafts Materials Degradation Evaluation

“…The analysis of chips formed during machining can be used to detect defects in steel parts. For this purpose, various research methods are used: the detection of material defects based on measurements of force and acoustic emission during processing [127][128][129][130][131][132][133][134][135][136][137][138][139][140][141]; a metallographic analysis; a surface roughness analysis; and various types of computer modeling [132][133][134][135][136][137][138][139][140][141][142] and machine learning, including artificial intelligence [143][144][145][146][147][148][149][150][151][152].…”

Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures

“…By constraining the machining parameters to those in the stable region of the SLD, it is possible to prevent the occurrence of chatter. However, from a practical standpoint, on-line monitoring and control of the machining parameters to eliminate instabilities prior to their occurrence seem a more favorable action (Aggogeri et al, 2021). One of the drawbacks associated with the approach of constraining machining parameters to prevent chatter is that it sets limits on the machining capabilities.…”

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

“…It is possible to find different trends in the modeling, design, simulation, and optimization of complex systems such as mineral processing using techniques such as computational fluid dynamics, discrete element simulation, surface response methodology, machine learning algorithms such as artificial neural networks, support vector machines or random forest, and uncertainly analysis or sensitivity analysis [39]. These techniques have been applied to different fields of practical industry [40], such as machining processes [41], chemical and process industries [42], geomechanics [43], the development of hybrid intelligent systems (combining human intelligence with artificial intelligence) [44], industrial control systems [45], decision support systems [46,47], applications in the pharmaceutical industry [48], integration through digital twins and Industry 4.0 in the food industry [49], improvement in the efficiency of industrial boilers through the detection, diagnosis, and forecasting of failures [50], and applications of discrete event simulation to metallurgical processes [51,52]. Directly related to the work carried out in the present manuscript, there was a survey of applications of machine learning algorithms in mineral processing, differing in categories such as data-based modeling, fault detection and diagnosis, and computer vision [53].…”