Machine Learning for Smart Manufacturing for Healthcare Applications Nivesh Gadipudi, I. Elamvazuthi, S. Parasuraman, and Alberto Borboni 1 Introduction The manufacturing industry has been continuously evolving since the seventeenth century to meet mass production, quality, and reliability requirements. The first generation of manufacturing industries used steam and coal-powered machines in substitution for manpower. In the very next phase of evolution, the manufacturing industry adapted the emerging energy solutions like electricity and petrochemicals [1]. Research and developments in integrated circuits and computers constituted industry 3.0. The third generation of industries involved automation and robotics using electronic controllers to enhance the mass production on shop floors. It is interesting to observe that the technological improvements in preceding genera- tions fuelled the upcoming industrial developments. Analogically, developments on integrated circuits and computers at the time of industry 3.0 unleased the required computational capabilities in the era of artificial intelligence and cloud computing. Advancements in technologies like artificial intelligence, cloud-based environments, N. Gadipudi · I. Elamvazuthi (B ) Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Malaysia e-mail: irraivan_elamvazuthi@utp.edu.my N. Gadipudi e-mail: nivesh48@gmail.com S. Parasuraman School of Engineering, Monash University Malaysia, Bandar Sunway, 46150 Subang Jaya, Malaysia e-mail: s.parasuraman@monash.edu A. Borboni Mechanical and Industrial Engineering Department, Universita degli studi di Brescia, Via Branze, 38, 25123 Brescia, Italy e-mail: alberto.borboni@unibs.it © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Palanikumar et al. (eds.), Futuristic Trends in Intelligent Manufacturing, Materials Forming, Machining and Tribology, https://doi.org/10.1007/978-3-030-70009-6_9 145