Abstract
In the realm of large-scale additive manufacturing, the deposition time for each layer, often termed ‘layer time’, plays a pivotal role in shaping the temperature of the layer. This, in turn, has a profound impact on the overall quality and performance of the manufactured product. Hence, the optimization and precise control of layer time emerge as critical challenges. Striking a balance is essential because both excessively high and low layer temperatures can adversely affect the manufacturing process. Traditional layer time optimization models typically introduce constraints on upper and lower temperature limits. However, the imposition of strict, hard constraints on these temperature bounds may lead to infeasible solutions, depending on factors like geometric design, material properties, and size. In response to these challenges, our study introduces a novel layer time control model that incorporates chance constraints, also known as soft constraints. These chance constraints offer a higher degree of flexibility, enhancing the robustness of the control approach. Our proposed model is validated through comprehensive case studies, demonstrating notable improvements in efficiency and feasibility.
