A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes

Abbass, Jabbar K.; Al‐Habaibeh, Amin

doi:10.1109/indin.2015.7281833

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The eddy current sensors IC12-02, two of its kind, are mounted in a biaxial manner connected to PDA 3502A type power supply. MATLAB accompanied by automated signal and sensor processing selection approach is used for extraction of sensitive features [18]. The main parameters and factors used in an indirect and online monitoring system for machining process are acoustic emission, microphone, vibration and spindle sensor data.…”

Section: E Features Analysis and Tool Health Monitoringmentioning

confidence: 99%

Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term Memory Networks: A Deep Learning Approach

Khan,

Kamal,

Ratlamwala

et al. 2023

IEEE Access

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The manufacturing industry has experienced remarkable progress as a result of integrating automated and intelligent production processes fueled by technological innovation, leading to substantial advancements. These advanced processes involve the use of flexible and high-performance machines, tackling complex and sophisticated processing problems with ease. However, the processing performance can deteriorate due to tool damage or malfunction, which can lead to the discarding of workpieces. Hence, it carries immense importance to have a close look over the condition of the tool throughout the processing to proactively address any potential issues and minimize the possibility of significant tool failures, particularly when manufacturing intricate and costly machine components. Many researchers have looked at the use of machine learning and deep learning approaches for monitoring tool condition. In this study, we are incorporating time series sequential data for which LSTM is the best opted technique. It further explains, how deep learning using Long Short-Term Memory Networks (LSTM) and the acoustic data acquired through a microphone during the metallic milling process has a potential and achieved impressive results. In our study, the accuracy of the model was assessed for different workpiece materials, including Aluminum, Mild Steel, and Brass, and demonstrated the model's ability to make highly accurate predictions. Specifically, the model achieved an average test accuracy of 99.03% for Aluminum workpieces, while achieving very good test accuracies of 97.16% and 97.83% for Mild Steel and Brass workpieces, respectively. These results were benchmarked against previous work in the same domain, confirming the efficacy of the model. INDEX TERMSLong short-term memory networks (LSTM), model test accuracy, tool condition monitoring.

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Section: E Features Analysis and Tool Health Monitoringmentioning

confidence: 99%

Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term Memory Networks: A Deep Learning Approach

Khan,

Kamal,

Ratlamwala

et al. 2023

IEEE Access

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“…Regarding the machine tool spindle, approaches are primarily focused on analysing the forces and/or vibrations occurring during the manufacturing process. However, approaches also consider the power consumption of the spindle motor [34][35][36][37]. The cutting tool condition is then often considered using any one of a threshold, time series, or a neural network style approach [17].…”

Section: Tool Condition Monitoringmentioning

confidence: 99%

The practical exploitation of tacit machine tool intelligence

Hill

Prickett

Grosvenor

et al. 2019

Int J Adv Manuf Technol

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Manufacturing equipment embraces an increasing measure of tacit intelligence, in both capacity and value. However, this intelligence is yet to be exploited effectively. This is due to both the costs and limitations of developed approaches and a deficient understanding of data value and data origin. This work investigates the principal limitations of typical machine tool data and encourages consideration of such inherent limitations in order to improve potential monitoring strategies. This work presents a novel approach to the acquisition and processing of machine tool cutting data. The approach considers the condition of the monitored system and the deterioration of cutting tool performance. The management of the cutting process by the machine tool controller forms the basis of the approach, and hence, makes use of the tacit intelligence that is deployed in such a task. By using available machine tool controller signals, the impact on day-to-day machining operations is minimised while avoiding the need to retrofit equipment or sensors. The potential of the approach in the contexts of the emerging internet of things and intelligent process management and monitoring is considered. The efficacy of the approach is evaluated by correlating the actively derived measure of process variation with an offline measurement of product form. The potential is then underlined through a series of experiments for which the derived variation is assessed as a direct measure of the cutting tool health. The proposed system is identified as both a viable alternative and synergistic addition to current approaches that mainly consider the form and features of the manufactured component.

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“…Various methods have been studied to detect tool wear states, and a large variety of sensors can be used for tool wear condition monitoring. Unfortunately, the performance of monitoring systems is still behind the expectations due to its high cost/performance ratio [2]. Successful condition monitoring is becoming extremely dependent on the ability to interpret multi-sensor data based on advanced signal processing methods [3].…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

Al-Azmi

Al‐Habaibeh

Abbas

2023

Int J Adv Manuf Technol

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This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors’ data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method.

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A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes

Cited by 8 publications

References 11 publications

Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term Memory Networks: A Deep Learning Approach

Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term Memory Networks: A Deep Learning Approach

The practical exploitation of tacit machine tool intelligence

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

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