Data-driven product family design for additive manufacturing
Date of Issue2016
School of Mechanical and Aerospace Engineering
Platform based product family design is a promising approach to meet diverse customer needs and achieve organizational objectives. We have dedicated this work to improve product family design by incorporating advanced information and new manufacturing technologies. Our effort resulted in a data-driven product family design for Additive Manufacturing (AM) method. The proposed data-driven approach used data mining to extract meaningful information from market data. The extracted information was interpreted by advanced machine learning algorithms to form a Decision Support System (DSS) that helps designers make informed decisions for market segmentation and product positioning. Based on the identified market segments, an AM process model for product family design was developed to offer affordable customization for each targeted market segment. Finally, a utility-based compromise decision support problem was formulated to serve as a mathematical framework for modeling a multi-objective product family design problem. The thesis highlights data-driven decision making and the opportunities for AM based product family design to operate in a much broader design space that is free from constraints which arise in traditional product family designs from finding a compromise between commonality and product performances. The data-driven product family design for AM method was tested and verified through three distinct case studies. The first case study focused on the design of a DSS for market segmentation and product positioning based on US automotive market data. The proposed DSS automates market segmentation and product positioning and provides a framework for the construction of a robust DSS. In the second case study, we used the proposed method to design a product family of cantilever beams. We found that our process model reflects the ability of AM to produce arbitrarily complex structures with virtually no tooling effort, and it makes these powerful properties available to practitioners working in the field of product family design. The final case study centered on the design of a dialysis finger pump family. The proposed method translates the benefits of AM into improved customization and cost reduction without compromising individual product performances. We created new knowledge in the product family design area by describing the theoretical and empirical validation process of the data-driven product family design for AM method. The main result of this research is a systematic framework which seamlessly integrates AM technologies into product family design to facilitate improved customization. The primary contribution of the framework is a data-driven DSS that advances market segmentation and product positioning. It is expected that the proposed method will redefine how we think about customization in product family design.