Ahmad Barari PhD, PE, Assistant Professor, University of Ontario Institute of Technology
Amirali Lalehpour, PhD Student, University of Ontario Institute of Technology
Additive manufacturing (AM) has made it possible to manufacture variety of design complexities, such as organic shapes resulting by Topology Optimization (TO) processes algorithms, which are unfeasible for manufacturing using the traditional methods. However, AM has its own manufacturing limitations as well, which also need to be considered during a TO process. It is crucial to develop TO algorithms that takes AM constrains into account. This research intends to introduce a new paradigm to identify the unfeasible solutions for AM obtained during TO iterations and modify them to make the design feasible for additive manufacturing. The areas with unfeasible support slops and bridges are dynamically identified by a quantitative sensitivity factor. The developed solution currently operates as a filter applied to TO iterations. The convergence of the developed algorithm is studied by considering proper termination threshold which is a function of the volume fraction in each iteration and the geometric area of the unfeasible sections. The filter becomes more sensitive when the volume fraction approaches to the target volume and when the unfeasible section become larger. The results of this methodology are found to be manufacturable by AM with minimum cost.