Machine Learning Approaches in Metal-Based Additive-Manufactured Wear-Resistant Bioimplants

Advancement in technology is a boon to the medical world. The world of medicine is in a transition phase from traditional to modern techniques. Bioimplants printed using additive manufacturing (AM) are the new era requirement for replacing physical transplants acquired from donors. Biomaterials used in AM support osseointegration. Using computer-assisted design, physical implant models with defined structure and shape are printed layer by layer. This layer-by-layer printing allows a high level of design freedom, material waste reduction, uniform lead time, and the creation of different shapes of geometrics that are platforms based on medical applications such as complex parts with desirable properties. Printing of complex parts using suitable biomaterials and printing methods ensure the development of a realistic sophisticated structure of organs. Thus the optimization of the printing process using attributes of printing like speed of printing, diameter of the nozzle, and dispensing pressure is the demand of the hour. The machine learning (ML) technique can serve as a powerful tool for optimizing not only attributes but also procedures of bioprinting. The learning process of ML algorithms is based on empirical data, which further make predictions or decisions, thus giving high-precision performance compared to physical-based models. Hence this chapter deals with the review of metal-based additive manufacturing for wear-resistant bioimplant materials printing using ML to improve the process development, printing strategies, flexibility in printing, etc.