Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system

This paper outlines a comparative study to compare between using the power of the spindle and the infrared images of the cutting tool to design a condition monitoring system. This paper compares the two technologies for the development of a tool condition monitoring for milling processes. Wavelet analysis is used to process the power signal. Image gradient and Wavelet analyses are used to process the infrared images. The results show that the image gradient and wavelet analysis are powerful image processing techniques in detecting tool wear. The power of the motor of the spindle has shown less sensitivity to tool conditions in this case when compared to infrared thermography

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

The design of an effective sensor fusion model for condition monitoring systems of turning processes

High energy price and the increasing requirements of quality and low cost of products have created an urgent need to implement new technologies in current automated manufacturing environments. Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the essential technologies that provide the competitive advantage in many manufacturing environments. This research aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Multi-sensors combined with a novelty detection algorithm and Learning Vector Quantisation (LVQ) neural networks are used in this research to detect tool wear and provide diagnostic and prognostic information. A novel approach, termed ASPST, (Automated Sensor and Signal Processing Selection System for Turning) is used to select the most appropriate sensors and signal processing methods. The aim is to reduce the number of sensors needed in the overall system and reduce the cost. The ASPST approach is based on simplifying complex sensory signals into a group of Sensory Characteristic Features (SCFs) and evaluating the sensitivity of these SCFs in detecting tool wear. A wide range of sensory signals (cutting forces, strain, acceleration, acoustic emission and sound) and signal processing methods are also implemented to verify the capability of the approach. A cost reduction method is also implemented based on eliminating the least utilised sensor in an attempt to reduce the overall cost of the system without sacrificing the capability of the condition monitoring system. The experimental results prove that the suggested approach provides a responsive and effective solution in monitoring tool wear in turning with reduced time and cost

Using spindle noise to monitor tool wear in a turning process

A tool condition monitoring system can increase the competitiveness of a machining process by increasing the utilised tool life and decreasing instances of part damage from excessive tool wear or tool breakage. This article describes an inexpensive and non-intrusive method of inferring tool condition by measuring the audible sound emitted during machining. The audio signature recorded can be used to develop an effective in-process tool wear monitoring system where digital filters retain the signal associated with the cutting process but remove audio characteristics associated with the operation of the machine spindle. This study used a microphone to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The audio signal is compared to the flank wear development on the cutting inserts for several different surface speed and cutting feed combinations. The results show that there is no relationship between the frequency of spindle noise and tool wear, but varying cutting speed and feed rate have an influence on the magnitude of spindle noise and this could be used to indicate the tool wear state during the process

Application of Audible Signals in Tool Condition Monitoring using Machine Learning Techniques

Machining is always accompanied by many difficulties like tool wear, tool breakage, improper machining conditions, non-uniform workpiece properties and some other irregularities, which are some of major barriers to highly-automated operations. Effective tool condition monitoring (TCM) system provides a best solution to monitor those irregular machining processes and suggest operators to take appropriate actions. Even though a wide variety of monitoring techniques have been developed for the online detection of tool condition, it remains an unsolved problem to look for a reliable, simple and cheap solution. This research work mainly focuses on developing a real-time tool condition monitoring model to detect the tool condition, part quality in machining process by using machine learning techniques through sound monitoring. The present study shows the development of a process model capable of on-line process monitoring utilizing machine learning techniques to analyze the sound signals collected during machining and train the proposed system to predict the cutting phenomenon during machining. A decision-making system based on the machine learning technique involving Support Vector Machine approach is developed. The developed system is trained with pre-processed data and tested, and the system showed a significant prediction accuracy in different applications which proves to be an effective model in applying to machining process as an on-line process monitoring system. In addition, this system also proves to be effective, cheap, compact and sensory position invariant. The successful development of the proposed TCM system can provide a practical tool to reduce downtime for tool changes and minimize the amount of scrap in metal cutting industry

Infrared Thermography for Weld Inspection: Feasibility and Application

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required

Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision

Ceramic tools are prone to chipping due to their low impact toughness. Tool chipping significantly decreases the surface finish quality and dimensional accuracy of the workpiece. Thus, in-process detection of chipping in ceramic tools is important especially in unattended machining. Existing in-process tool failure detection methods using sensor signals have limitations in detecting tool chipping. The monitoring of tool wear from the workpiece profile using machine vision has great potential to be applied in-process, however no attempt has been made to detect tool chipping. In this work, a vision-based approach has been developed to detect tool chipping in ceramic insert from 2-D workpiece profile signature. The profile of the workpiece surface was captured using a DSLR camera. The surface profile was extracted to sub-pixel accuracy using invariant moment method. The effect of chipping in the ceramic cutting tools on the workpiece profile was investigated using autocorrelation function (ACF) and fast Fourier transform (FFT). Detection of onset tool chipping was conducted by using the sub-window FFT and continuous wavelet transform (CWT). Chipping in the ceramic tool was found to cause the peaks of ACF of the workpiece profile to decrease rapidly as the lag distance increased and deviated significantly from one another at different workpiece rotation angles. From FFT analysis the amplitude of the fundamental feed frequency increases steadily with cutting duration during gradual wear, however, fluctuates significantly after tool has chipped. The stochastic behaviour of the cutting process after tool chipping leads to a sharp increase in the amplitude of spatial frequencies below the fundamental feed frequency. CWT method was found more effective to detect the onset of tool chipping at 16.5 s instead of 17.13 s by sub-window FFT. Root mean square of CWT coefficients for the workpiece profile at higher scale band was found to be more sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts