Statistical process control (SPC) is a collection of problem-solving tools used to achieve process stability and improve process capability through variation reduction. Because of its sound statistical basis and intuitive use of visual displays, SPC has been extensively used in manufacturing, healthcare and service industries. Deploying SPC involves both a technical aspect and a proper environment for continuous improvement activities based on management support and worker empowerment. Many of the commonly used SPC tools, including histograms, fishbone diagrams, scatter plots and defect concentration diagrams, were proposed prior to the advent of microcomputers as efficient methods to record and visualize data for single (or few) variable(s) processes. As the volume, variety and velocity of data continues to evolve, there are opportunities to supplement and improve these methods for understanding and visualizing process variation. In this paper, we propose enhancements to some of the basic quality tools that can be easily applied with a desktop computer. We demonstrate how these updated tools can be used to better characterize, understand, and/or diagnose variation in a case-study involving a U.S. manufacturer of structural tubular metal products. Finally, we create the Quality Visualization Toolkit (QVT) to allow practitioners to implement some of these visualization tools without the need for training, extensive statistical background and/or specialized statistical software.

The Excel files containing the macros for the QVT can be found by clicking here.

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 parts 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.

Using three-dimensional animation software, this paper provides a framework that allows industrial practitioners to visualize the most significant variation patterns within their process. 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 the process with respect to the final product; and 3) utilize the new insights regarding the process variability to identify opportunities for process improvement.

In that paper, we have provided a standard method to handle three dimensional geometrical profiles, which is an important contribution to statistical process control (SPC) since current techniques are only suited to deal with two dimensional profiles. We have shown that simply extending current methods to complex three dimensional data will not be necessarily informative, and instead that it is necessary to utilize the information on both the product geometry and the monitored variation. Accordingly, we developed an approach that combines the use of SPC and CAD to easily visualize and interpret dimensional variation patterns occurring in manufacturing systems. This allows engineers and operators to use their knowledge of the process to quickly understand and identify common-cause variability, the result of which is faster identification of opportunities for process improvement, and an overall increase in product quality. The significance of the proposed framework is illustrated through a case study using actual dimensional data from a U.S. automotive assembly plant (as can be seen in the video).