Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization

Abstract

Product family design (PFD) is a widely adopted strategy for product realization, especially when design requirements are diversified and multi-faceted. Due to ever-changing customer needs and the increasingly complex and integrated product design structure, PFD and its optimization have been concerned more about a rapid and contextual product analysis and variant derivation based on a multi-objective optimization scheme subject to design concerns, which are often cross disciplinary, such as product service, carbon footprint, user experience, esthetics, etc. Existing PFD modeling approaches, which are primarily structured using component attributes and assembly relationships, possess notable limitations in representing complex component and design relationships. Hence, it has restricted comprehensive PFD analysis in an agile and contextual manner. Previously, we have studied and demonstrated the feasibility of using ontology for product family modeling and have suggested a framework of faceted information search and retrieval for product family design. In this paper, several new perspectives towards PFD based on ontology modeling are presented. Firstly, new metrics of ontology-based commonality that better reveal conceptual similarity under various design perspectives are formed. Secondly, faceted concept ranking is proposed as a new ranking approach for ontology-based component search under complex and heterogeneous design requirements. Thirdly, using these ranked results, a platform selection approach that considers a maximum aggregated ranking with a minimal platform modification among various platform choices is researched. From the selected platform and the newly proposed metrics, a modified multi-objective evolutionary algorithm with an embedded feature of configuration incompatibility check is studied and deployed for the optimal selection of components. A case study of PFD using four laptop computer families is reported as our first attempt to showcase how faceted component analysis, selection, and optimization can be accomplished based on the proposed family ontology.

A computational tool for creative idea generation based on analogical reasoning and ontology

“…Darlington and Culley (2008) showed how ontologies support the capture of the engineering design requirement. Nanda et al (2005) and Liu et al (2013) used ontology for product family design. Chandrasegaran et al (2013) indicated that ontologies are required for encoding design knowledge as well as facilitating semantic interoperability.…”

Ontology-Based Representation of Design Decision Hierarchies

“…Although the research on ontology has its roots in computer science, ontologies have often been used for knowledge modeling in the engineering domain. Applications are typically categorized as follows: knowledge sharing [38,39], knowledge retrieval [40,41], and knowledge documentation [42,43]. In knowledge sharing, the functionality of ontologies in representing a common understanding of a domain is emphasized and is used for sharing and exchanging information among applications.…”

“…In this paper, we focus on the application of ontology in knowledge documentation. It is recognized that several ontologies have been developed for documenting knowledge in the domain of engineering design, such as a design optimization ontology [43], a design for manufacturing ontology [44], a design for additive manufacturing ontology [45], a functional design ontology [46], and a product family design ontology [41]. In engineering design, decisions determine how design resources are allocated and how design objectives are achieved, which are critical, and this knowledge should also be documented for reuse.…”

A multi-population co-evolutionary genetic programming approach for optimal mass customisation production

“…Hong et al (2008) developed a genetic programming method to identify the optimal product configuration and its parameters based on individual customer requirements on performance and costs in one-of-a-kind production. Liu, Lim, and Lee (2013) proposed a multi-objective evolutionary algorithm with an embedded feature of configuration incompatibility check to identify the optimal customised product from a product family. Goswami and Tiwari (2015) used mixed integer quadratic programming to identify the products considering commercial objectives of the enterprise and engineering-level constraints of the product.…”