Abstract
Full automation of metal cutting processes has been a long held goal of the manufacturing industry. One key obstacle to achieving this ambition has been the inability to monitor completely the condition of the cutting tool in real time, as premature tool breakage and heavy tool wear can result in substantial costs through damage to the machinery and increasing the risk of non‐conforming items that have to be scrapped or reworked. Instead, the condition of the tool has to be indirectly monitored using modern sensor technology that measures the acoustic emission, sound, spindle power and vibration of the tool during a cut. An online monitoring procedure for such data is proposed. Firstly, the standard deviation is extracted from each sensor signal to summarise the state of the tool after each cut. Secondly, a multivariate autoregressive state space model is specified for estimating the joint effects and cross‐correlation of the sensor variables in Phase I. Then we apply a distribution‐free monitoring scheme to the model residuals in Phase II, based on binomial type statistics. The proposed methodology is illustrated using a case study of titanium alloy milling (a machining process used in the manufacture of aircraft landing gears) from the Advanced Manufacturing Research Centre in Sheffield, UK, and is demonstrated to outperform alternative residual control charts in this application. © 2016 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.
Highlights
There is a global drive towards increased productivity in the advanced manufacturing sector in order to meet the growing demands of lower cost targets, increased volume and increased process capability
The high-frequency sampling rate of these sensors means that large amounts of data is amassed in the short space of time it takes to cut a workpiece, acoustic emission data, which is typically sampled at a rate of over one million observations per second
The resulting multivariate time series is non-stationary, and it is important to develop a monitoring procedure that can distinguish between local variation in the data that is not attributable to out-of-control behaviour and longer term changes that are associated with tool wear
Summary
There is a global drive towards increased productivity in the advanced manufacturing sector in order to meet the growing demands of lower cost targets, increased volume and increased process capability. Productivity could be substantially improved via the automation of decision-making tasks previously taken, usually very conservatively, by the operator of the machine This is theoretically possible because of the development of modern sensor technology that enables the automatic collection of data related to the performance of the metal machining process. We focus on one important limitation to machining productivity, tool wear, and develop a statistical process control (SPC) technique that could potentially be used to automate the decision as to when to replace the tool. This problem is challenging for several reasons.
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