In-Process Quality Characterization of Grinding Processes: A Sensor-Fusion Based Approach

Abstract

The quality assessment of manufacturing processes has been traditionally based on sample measures performed on the process output. This leads to the common “product-based Statistical Process Control (SPC)” framework. However, there are applications of actual industrial interest where post-process quality measurement procedures involve time-consuming inspections strongly related to the operator’s experience and/or based on expensive equipment. Cylindrical grinding of large rolls may be one of them. The assessment of the final acceptability of a ground cylinder, in terms of surface finish, is a challenging task with traditional measuring tools, and it often depends on operator’s visual inspections and on his subjective evaluations. In this frame, a paradigm shift is required to substitute troublesome post-process monitoring procedures with in-process and signal-based ones. The paper reviews the quality control issues in surface quality monitoring of big ground rolls where process vibrations (i.e. chatter) are one of major concerns. A multi-sensor approach for vibration onset detection, based on the use of a multi-channel implementation of the Principal Component Analysis, is proposed. The potential benefits, the implementation issues, and the main criticalities are discussed by analysing data of a real industrial application.

A Comparison Study of Distribution‐Free Multivariate SPC Methods for Multimode Data

“…The in‐process acquisition of sensor signals has particular industrial relevance because it allows the detection of undesired process phenomena that affect product quality and implementation of adaptive control actions. However, signal data might present a multimode pattern caused by frequent changes of the cutting parameters during each grinding cycle, and this situation makes chatter detection a troublesome task using traditional control charting methods. Despite of a body of literature devoted to the chatter detection problem, few automatic methods have been considered for actual industrial implementation.…”

“…The signal was processed online to compute two synthetic indices denoted by rms j and kurt j , j = 1, 2, …, which consist of the root mean square index of the vibration signal and the kurtosis of its time‐domain distribution within the j th time window. The rms index was chosen because it represents the most basic choice for vibration monitoring in industrial applications . Our previous experimental studies showed that use of the kurt index in combination with the rms index enhances the capability of chatter detection.The result is a bivariate quality characteristic { x j ∈ ℝ 2 , j = 1, 2, …}, where x j = [ rms j , kurt j ] T .…”