Abstract
An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.
Highlights
In digital manufacturing environments, various types of data are accumulated over time in databases at various stages of product design and manufacturing
binary particle swarm optimization- (BPSO-)association rule mining (ARM) does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases
The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing
Summary
Various types of data are accumulated over time in databases at various stages of product design and manufacturing. A strong association exists between the design of products and the machine capabilities of their manufacturing. Due to rapid change in market and customer needs, the lifecycles of products and manufacturing systems are both getting shorter and shorter. Manufacturers and designers are motivated to adapt their manufacturing systems to the frequent products changes and utilize all available machine capabilities before introducing product features that require extra capabilities [2]. The need for an efficient and costeffective product development and production is constant and growing. It is necessary to develop an effective analytical tool to automatically discover hidden associations between product and manufacturing elements from the historical data
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