A framework for variation visualization and understanding in complex manufacturing systems

作者：Wells Lee J^*; Megahed Fadel M; Camelio Jaime A; Woodall William H

来源：Journal of Intelligent Manufacturing, 2012, 23(5): 2025-2036.

DOI：10.1007/s10845-011-0529-1

• Chance-cause variability
• Computer-aided design
• Principal components
• Process variation
• Statistical process control
• Visual analytics

This paper provides a framework that allows industrial practitioners to visualize the most significant variation patterns within their process using three-dimensional animation software. In essence, this framework complements Phase I statistical monitoring methods by enabling users to: (1) acquire detailed understanding of common-cause variability (especially in complex manufacturing systems); (2) quickly and easily visualize the effects of common-cause variability in a process with respect to the final product; and (3) utilize the new insights regarding the process variability to identify opportunities for process improvement. The framework is illustrated through a case study using actual dimensional data from a US automotive assembly plant.

• 出版日期2012-10
• 单位美国弗吉尼亚理工大学(Virginia Tech)