A framework for variation visualization and understanding in complex manufacturing systems
Journal of Intelligent Manufacturing
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Statistical process control (SPC) methods have been extensively applied to monitor the quality performance of manufacturing processes to quickly detect and correct out-of-control conditions. As sensor and measurement technologies advance, there is a continual need to adapt and refine SPC methods to effectively and efficiently use these new data-sets. One of the most state-of-the-art dimensional measurement technologies currently being implemented in industry is the 3D laser scanner, which rapidly provides millions of data points to represent an entire manufactured part's surface. Consequently, this data has a great potential to detect unexpected faults, i.e., faults that are not captured by measuring a small number of predefined dimensions. However, in order for this potential to be realized, SPC methods capable of handling these large data-sets need to be developed. This paper presents an approach to performing SPC using point clouds obtained through a 3D laser scanner. The proposed approach transforms high-dimensional point clouds into linear profiles through the use of Q---Q plots, which can be monitored by well established profile monitoring techniques. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios. In addition, experimental studies were performed to determine the effectiveness of the proposed approach using actual point cloud data. The results of these experiments show that the proposed approach can significantly improve the monitoring capabilities for manufacturing parts that are characterized by complex surface geometries.