Statistical Process Control Using On-Machine Probing Data

Product quality plays a major role in the success of every manufacturing organization. The popular way to study and analyze the quality is through the use of a set of Statistical Process Control (SPC) tools. The correct application of these tools in a manufacturing scenario is fundamental to good process management and reduces process variation.Coordinate Measuring Machines (CMM) and on-machine probing are being extensively used in the inspection of mechanical components for statistical process and quality control in manufacturing processes. SPC uses certain process performance indicators and statistical methods to monitor for changes that might affect the quality of the product.  It is important that we need to understand the true reason behind process variability; and simply not whether a process is in control or parts being manufactured have been accepted or rejected. A preliminary step towards understanding inherent process variation present in cutting process is to dwell into an SPC monitoring system that deals with raw probing data. The MTConnect standard has facilitated extensions to its XML tags to integrate sensory data from the on-machine probes along with control data, which is readily available across the shop floor network. MTConnect adapters are developed with customized XML tags that successfully collect raw data from the on-machine probes. This is accomplished by indirectly establishing communication between the probe sensor and the MTConnect Agent via the machine controller. An SPC monitoring application based upon the data collected through such an MTConnect implementation is presented. The application is used to collect real machining data under multiple cutting process conditions, thereby demonstrating how certain SPC performance indicators (trending, shifting) are related to avoidable and inherent variations in the cutting process (tool wear, tool macro-geometry disparities). Improved SPC monitoring methods that incorporate knowledge of variations in estimating performance indicators are discussed.

Reference: Statistical Process Control Using MTConnect; Atluru S. Deshpande A.; Proceedings of the ASME 2012 International Manufacturing Science and Engineering Conference (MSEC2012); June 4-8, 2012, Notre Dame, Indiana, USA.