Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems.

E García Plaza,E Beamud González,P Núñez López

doi:10.3390/s18124381

Abstract

Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

Highlights

Current quality control techniques require slow and costly measurement procedures for inspecting finished products
TDAmethod method directly signal registered by the in thein time with no transformation or decomposition, for fast at a low cost
87.8%, r improved the model in all of the evaluation indices of the individual analyses, with a Radjadjof 87.8%, sharp fall ininthe relative error reaching a value ofaf14.6%, and a significant in reliability to to. These revealed feed vibration was the component withincrease the greatest impact on sharp fall theresults relative errorer ethat r reaching a value of 14.6%, and a significant increase in reliability. These results revealed that feed vibration a was the component with the greatest impact on surface finish (Ra), and that the radial a p and tangential a c vibration complemented the information f to 91.1%

Summary

Introduction

Current quality control techniques require slow and costly measurement procedures for inspecting finished products. Machining monitoring systems are an ideal tool for overcoming these deficiencies, since they permit the real-time monitoring and control of the cutting process, detect in-process malfunctions, and apply corrective measures to avoid the manufacture of defective products. Several aspects of machining processes can be supervized using monitoring techniques. Numerous publications have focused on the analysis of tool condition [1,2,3,4] and chatter [5,6,7,8], whereas other aspects such as surface finish [9,10,11,12], dimensional precision [11,12,13,14], Sensors 2018, 18, 4381; doi:10.3390/s18124381 www.mdpi.com/journal/sensors. The appropriate selection of sensors is crucial for monitoring techniques to be efficacious

Methods

Results

Conclusion