Publication Details

Shukla, N., Tiwari, M. Ceglarek, D. (2009). Feature-based Optimal Sensor Distribution for Six-sigma Variation Diagnosis in Multi-Station Assembly Processes. 7th International Conference on Manufacturing Research (ICMR)


This paper presents a novel feature-based sensor distribution approach for root cause analysis and diagnosis of product 6-sigma variation faults in multi-station assembly processes. Traditional approaches in sensor distribution are based on the assumption that measurement points can be selected at arbitrary locations on the part or subassembly. This causes challenges such as difficult calibration of measurement system, increased errors of measured features, and lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). In the proposed approach, we develop methodology to maximize the number of measurement points that are placed at critical design features called Key Characteristics (KCs) which are classified into: Key Product Characteristics (KPCs) and Key Control Characteristics (KCCs) and represent critical product and process design features, respectively. In particular, KCs have dimensional and geometric tolerances which provides necessary design reference model for process control and diagnosis of product 6-sigma variation fault. The proposed approach integrates state-of-the-art approaches with GA-based procedure for optimal sensor distribution. In addition to maximizing the number of measurement points that are placed at KCs, the proposed approach allow to obtain minimum required production system diagnosability by integrating state-of-the-art approaches (unrestricted location of measurement points) with the developed GA-based procedure (restricted location of measurement points to the pre-defined KCs). A case study of automotive assembly processes is used to illustrate the proposed feature-based approach.

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