A design framework for product families implemented with additive manufactured variable platforms
Date of Issue2017-02-27
School of Mechanical and Aerospace Engineering
Challenges exist in developing new product family design methodologies that can utilize the additive manufacturing (AM) advantages to improve the product family competitiveness in a cost efficient way. In this research, the author attempts to compensate the performance compromise in conventional product families by proposing the novel concept of an additive manufactured variable platform (VP); and the major objective of this thesis is to develop a design framework that helps designers implement additive manufactured VP modules within product families. During the development of the design framework, the following methodologies have been proposed: 1) a representative mathematical model to formulated the VP-based product family configuration; 2) a hybrid machine learning approach to select appropriate AM design features for addtive manufactured VP modules; 3) a fuzzy time-driven activity-based costing method to predict the AM production cost variation as a function of VP design adjustments, 4) a novel evaluation metric for VP-based product family designs, and 5) a multiobjective design optimization technique to identify the modular configuration and engineering design parameters of a product family. A case study of an R/C racing car family design is used throughout this thesis to demonstrate the usefulness of the proposed methodology. The implementation of additive manufactured VP modules shows a significant improvement in a product family’s performance at the price of a slight decrease in design commonality. As the major contribution of this research, the proposed design framework provides designers and enterprises with a guideline in developing high-performance and cost-efficient product families by utilizing AM-enabled design flexibility. The two previously separate research domains, i.e. AM and product family design, are integrated using the proposed framework and the constituent methodologies.