Abstract:
Injection molding machine is a versatile machine in the field of manufacturing. It is mainly used in mass production to replicate plastic parts. Now-a-days, a quick shipment of finished products to the customer is necessary. Thus, cycle time needs to be minimized. However, besides quick delivery of products, maintaining quality of the finished plastic products is also necessary. Pressure drop is a vital factor to maintain the quality of the finished products. If pressure drop is large in magnitude then it can hamper the quality of the products. Loss of pressure hinders plastics to travel through the nozzle into the mold and plastic may be dried up in before reaching the mold cavity. Thus, to shorten lead time and to obtain good quality products both cycle time and pressure drop need to be minimized. However, these two objectives are conflicting in nature, which means minimizing cycle time, maximizes pressure drop and vice versa. Optimization of injection molding system is also multidisciplinary in nature. Multi-disciplinary injection molding system is a complex engineering system consisting of four distinctive physically different sub-systems among which feed-forward and feed-back coupling variables are also present. The role of uncertainty management is increasingly being recognized in the design of complex systems that require multidisciplinary analyses. Inclusion of uncertainty in the design variables and the system parameters further adds another level of complexity in the design of injection molding systems. The overall objective of this thesis is to find optimum values of design variables of this injection molding system using multidisciplinary design optimization MDO methodology and considering both feed-forward and feed-back couplings as well as uncertainty in both design variables and system parameters. Specifically, this thesis accomplishes this objective through development offormulations and algorithms for design optimization of injection molding system under uncertainty, from the perspective of system robustness.
