Machine learning based predictive modeling and control of surface roughness generation while machining micro boron carbide and carbon nanotube particle reinforced Al-Mg matrix composites

• Published in: Particulate science and technology Vol. 40; no. 3; pp. 355 - 372
• Main Authors: Sekhar, Ravi, Singh, T. P., Shah, Pritesh
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 03-04-2022; Taylor & Francis Ltd
• Subjects: Aluminum matrix composites; Boron carbide; complex order controller; Controllers; Errors; fractional order controller; Machine learning; Machining; Magnesium; Metal matrix composites; model predictive controller; Modelling; Multi wall carbon nanotubes; Parameter estimation; Particulate composites; Performance prediction; Prediction models; Predictive control; predictive modeling; Surface roughness
• DOI: 10.1080/02726351.2021.1933282
• EISSN: 1548-0046
• ISSN: 0272-6351

Machine learning has revolutionized the way complex problems are solved in engineering. In the current work, machine learning methodology has been applied for predictive modeling of surface roughness generation during machining of Al-Mg based metal matrix composites (MMCs) reinforced with micro boron carbide and multiwalled carbon nanotube particles. Machine learning was used for parameter estimation of modeling structures such as auto regressive with exogenous variables (ARX), auto regressive moving average with exogenous variables (ARMAX), Box Jenkins (BJ) and Output Error (OE). The identified models were validated on the basis of FIT, final prediction error (FPE) and mean squared error (MSE). The PID, fractional order PID (FOPID), complex order PID (COPID) and model predictive controllers (MPC) were employed to effectively control machined surface roughness based on the best performing predictive models. Primary results indicate that: (1) CNT MMCs generate surface roughness comparable to that due to the micro MMCs with tenfold higher reinforcement fractions (2) ARX441 and ARMAX3331 are the best performing predictive models for the nano and micro MMCs respectively (3) PID and MPC are the best controllers for micro and nano MMC systems respectively considering the peak overshoots as the foremost performance metric (safety), followed by settling time (productivity).