Parametric topology optimization approach for sustainable development of customized orthotic appliances using additive manufacturing

The rapidly growing applications of additive manufacturing (AM) are transforming the scenario of the medical industry for sustainably availing customized orthotic products. The adaptive interlinking attribute of 3D printing enables it to replicate the optimization results of a trauma-based anatomical problem, designed and solved with artificial intelligence (AI) for structural mechanics. In the present hybrid approach, the potential of simulation engineering has been used to solve the physics of a mallet trauma transforming existing geometry into an efficacious splint design capable of delivering improved recovery outcomes. The physical measurements have been precisely converted into a customized orthosis, by executing 3D scanning and printing over the optimized parameters availed by data analysis coupled with artificial intelligence of statistical tools. The topological simulation-predicted design was processed for creating the tailored 3D printed mallet splint with a prediction accuracy of 98.49%, availing 42.6% (weight 2.87 gm for 58.28% mass splint) lighter orthosis than the original (weight 5 gm for 100% mass splint) that provides effective ventilation with 149.19% greater heat dissipation. The noninvasive interaction for anthropometric data collection and appealing custom appliances fabricated with bio-sourced materials can trigger faster psychological and physiological healing in patients. The results recorded with an implemented hybrid approach can change the perspective of medical professionals for adopting the AM fused with simulation engineering, as a sustainable alternative to traditional services. The biocompatibility of 3D printed orthotic products facilitates the removal of the excess burden of medical waste and thus the applications of the adopted approach can be practiced for providing sustainable responses to other medical branches like podiatry, dentistry, orthopedics, rheumatology and neuropathy.