Title: Commonality and performance metrics to evaluate and optimise the design of additive manufactured product families

Authors: Xiling Yao; Seung Ki Moon; Guijun Bi; Hungsun Son

Addresses: Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore; Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, 638075, Singapore ' Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore ' Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, 638075, Singapore ' School of Mechanical and Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea

Abstract: With the capability of fabricating parts directly from digital models without tooling, additive manufacturing (AM) technologies have great potentials for customised products with complex shapes and superior performances. This paper develops commonality and performance metrics that can be used to evaluate the design of product families implemented with additive manufactured modules. The complex commonality index (CCI) is proposed to measure parametric, modular, and process sharing within a product family. Design and production costs of additive manufactured modules are incorporated in the formulation of the CCI. The market share (MS) is formulated to measure a product family's performance based on customer-perceived utilities. A product family design optimisation problem with the CCI and MS as objective functions is proposed. A case study on an R/C racing car family design demonstrates the proposed methodologies, and the result provides designers with Pareto-optimal solutions for additive manufactured module selection and design parameter identification. [Received 18 April 2016; Revised 2 September 2016; Accepted 9 November 2016]

Keywords: additive manufacturing; commonality metric; performance metric; product family design; variable platforms.

DOI: 10.1504/IJMR.2017.083654

International Journal of Manufacturing Research, 2017 Vol.12 No.1, pp.44 - 63

Received: 18 Apr 2016
Accepted: 08 Nov 2016
Published online: 13 Apr 2017 *

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