Quantitative platform selection in optimal design of product families, with application to automotive engine design

Abstract

Product variants with similar architecture but different functional requirements may have common parts. We define a product family to be a set of such products, and refer to the set of common parts as the product platform. Product platforms enable rapid adjustment to changing market needs while keeping development costs and time-cycles low. In many cases, however, the individual product requirements are conflicting when designing a product family. The designer must balance the tradeoff between maximizing commonality and minimizing individual product performance deviations. The design challenge is to select the product platform that will generate family designs with minimum deviation from individual optima. We propose a methodology that combines two previous approaches developed for making commonality decisions. In the first approach optimal values and sensitivity information from the individually optimized variants are used to indicate components that are probable candidates for sharing. In the second approach a relaxed combinatorial problem is formulated to maximize sharing among variants subject to bounds on performance reduction for the individually optimized values. In the combined methodology the first approach is used to identify an initial set of shared components and define the candidate platform to be considered by the second approach. The computational load is reduced significantly and the platform-selection problem is solved in a more robust manner. The proposed methodology is demonstrated on the design of an automotive engine family.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 48 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.