Abstract

Mechanical engineering has over the years evolved from mere “chassis-on-wheels” to “luxury-on-move” with the introduction of advanced design and manufacturing sectors, such as additive manufacturing, Computer-Numerically Controlled (CNC), mechatronics, robotics and more recently the Internet-of-Things (IoT). However, challenges in terms of optimizing the process parameters, selection of materials, standardization of the processes used and quality control, have gained increased importance and attention. Hence, a multi-disciplinary approach involving both data science and data analytics have helped in synergizing the application of Machine Learning (ML) techniques to address the aforementioned challenges. In recent years, several technical publications have surfaced in this direction and show much promise and excitement towards a future which will essentially be “intelligence-on-the-move”. In this technical paper, an attempt is made to look at the current scenario in terms of application of machine learning to the various sectors in the field of mechanical engineering.

Keywords:Additive manufacturing; Machine learning; Data mining; Quality control