Key characteristics-based sensor distribution in multi-station assembly processes

This paper presents a novel approach for optimal key characteristics-based sensor distribution in a multistation assembly process, for the purpose of diagnosing variation sources responsible for product quality defects in a timely manner.. Current approaches for sensor distribution are based on the assumption that measurement points can be allocated at arbitrary locations on the part or subassembly. This not only presents challenges in the implementation of these approaches but additionally does not allow required product assurance and quality control standards to be integrated with them, due to lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). Furthermore, it causes difficulty in calibration of measurement system and increases the likelihood of measurement error due to the introduction of measurement points not defined in GD&T. In the proposed approach, we develop methodology for optimal sensor allocation for 6-sigma root cause analysis that maximizes the number of measurement points placed at critical design features called Key Characteristics (KCs) which are classified into: Key Product Characteristics and Key Control Characteristics and N. Shukla · D. Ceglarek The International Digital Lab, WMG, University of Warwick, Coventry CV4 7AL, UK N. Shukla (B) SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia e-mail: [email protected] D. Ceglarek Department of Industrial & Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA M. K. Tiwari Department of Industrial and Engineering Management, Indian Institute of Kharagpur, Kharagpur 721302 , India represent critical product and process design features, respectively. In particular, KCs have defined dimensional and geometric tolerances which provides necessary design reference model for process control and diagnosis of product 6-sigma variation faults. The proposed approach allows obtaining minimum required production system 6-sigma diagnosability. A feature-based procedure is proposed which includes Genetic Algorithm-based approach (allowing predefined KCs as the measurement points) and state-of-theart approaches (unrestricted location of measurement points) to iteratively include arbitrary measurement points together with KCs in the final sensor layout. A case study of automotive assembly processes is used to illustrate the proposed feature-based approach.