A systematic adaptable platform architecture design methodology for early product development

ABSTRACT

Adaptable product platforms, which express a platform's modular and scalable combinations, are well suited for representing the complex mechanical composition of series products. Current adaptable platform design methods, however, still have certain limitations, such as an insufficient basis for functional modules classification, unstable classified results due to experience-based thresholds, and limited scope because of a reliance on performance mathematical models. Given the hierarchical model of adaptable platform architecture, a two-stage methodology for adaptable platform design based on quantitative indices and fuzzy arithmetic is proposed in this paper. In the first stage, the variety index and change propagation class are applied to cluster the acquired modules into standardised and flexible ones using visualised fuzzy clustering. In the second stage, common and scalable indices based on product lifecycle factors are developed in order to subdivide the components within flexible modules into common and scalable types. Trapezoidal fuzzy arithmetic is introduced in these two stages to deal with the imprecise, approximate, or qualitative linguistic assessments. The proposed approach is demonstrated using the concrete spaying machine family as an exemplary product. Sensitivity analysis showed that the results were robust for the disturbance of design variables. Comparisons with other studies’ results point to a higher stability, lower requirements for basic data, and a general effectiveness of the proposed method.

KEYWORDS:

• Adaptable platform
• platform architecture design
• modularisation
• parameters identification
• fuzzy arithmetic

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is financially supported by the National Natural Science Foundation of China [grant number 51475459]; the Fundamental Research Funds for the Central Universities of China [grant number 2012QNB15]; the Priority Academic Program Development of Jiangsu Higher Education Institutes [grant PAPD]; funding the research group Cascade Use from German Federal Ministry of Education and Research [grant number 01LN1310A (2014-2018)].