Integrating a Proactive Quality Control Concept into Machining Operation of a Crankshaft Manufacturing Process
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
Competition in the manufacturing industry is ever increasingly intense. Manufacturing organizations that want to grow and prosper must embrace a discipline of constant improvement. Their engineering departments are tasked with improving existing manufacturing processes in terms of quality and throughput, which is vital to competing on a global scale. Manufacturers strive to utilize technologies to extract efficiencies from their existing processes. Reducing scrap and rework is the paramount goal in increasing a processes’ efficiency. The foundation of this study is to analyze a production line to determine the quality status throughout the manufacturing process. The intention is to react to process instability before the production becomes non-compliant (scrap/rework) which will significantly improve productivity.
By incorporating the proposed technology into the production process, the desired achievement will be to spot process variables at the earliest stages so that counter measures can be taken to stabilize the process before production drifts into non-compliance. Furthermore, the technology will communicate with machining operations to initiate counter measures such as program offsets, tool changes and wheel dressings. The main goal of this study is to develop a system that will concurrently monitor the compliance of respective machining operations and automatically issue corrective actions to keep production in compliance much faster than the current system, which relies on production workers to acknowledge that the process is producing scrap of rework parts and then determine what corrective action to take.
CitationBazzi, L., "Integrating a Proactive Quality Control Concept into Machining Operation of a Crankshaft Manufacturing Process," SAE Technical Paper 2019-01-0507, 2019, https://doi.org/10.4271/2019-01-0507.
Data Sets - Support Documents
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