Discussion of big data (BD) has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that big data have changed problem solving: the problems we choose to solve, the strategy we seek, and the tools we employ. This paper illustrates this point by showing how the big data style of thinking enabled the development of a new quality assurance philosophy called process monitoring for quality (PMQ). PMQ is a blend of process monitoring and quality control (QC) that is founded on big data and big model (BDBM), which are catalysts for the next step in the evolution of the quality movement. Process monitoring (PM) for quality was used to evaluate the performance of the ultrasonically welded battery tabs in the new Chevrolet Volt, an extended range electric vehicle.
Lift-gate is a rear closure system present in sports utility vehicles (SUV) and minivans. It has multiple design variables affecting multiple functional requirements. A wedge is a small piece of hardware used to stabilize the lift-gate especially in cross-car direction by providing the load path from the lift-gate to the surrounding structure. But, a wedge may increase the closing effort due to the reaction force in the swing direction. Furthermore, wedges need to accommodate the gap variation to bridge the lift-gate and the body frame. This paper presents the characteristics of two different types of wedges, their effects on the lift-gate system, and design methodologies to improve the wedge performance.
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