Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring

1990 ◽  
Vol 112 (3) ◽  
pp. 219-228 ◽  
Author(s):  
S. Rangwala ◽  
D. Dornfeld
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Noise Suppression ◽  
Success Rates ◽  
Intelligent Sensor ◽  
Multiple Sensors ◽  
Intelligent Sensors ◽  
Tool Condition ◽  
Human Operators ◽  
Manufacturing Environments

A framework for intelligent sensors in unmanned machining is proposed. In the absence of human operators, the process monitoring function has to be performed with sensors and associated decision-making systems which are able to interpret incoming sensor information and decide on the appropriate control action. In this paper, neural networks are used to integrate information from multiple sensors (acoustic emission and force) in order to recognize the occurrence of tool wear in a turning operation. The superior learning and noise suppression abilities of these networks enable high success rates for recognizing tool wear under a range of machining conditions. The parallel computation ability of these networks offers the potential for constructing intelligent sensor systems that are able to learn, perform sensor fusion, recognize process abnormalities, and initiate control actions in real-time manufacturing environments.

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

Tool Condition Monitoring in Microturning of Aluminium Alloy Using Multiple Sensors

2014 ◽  
Vol 592-594 ◽  
pp. 796-800
Author(s):  
A. Gopikrishnan ◽  
M. Kanthababu ◽  
R. Balasubramaniam ◽  
Prabhat Ranjan
Keyword(s):  
Tool Wear ◽  
Aluminium Alloy ◽  
Cutting Forces ◽  
Thrust Force ◽  
Tangential Force ◽  
Vibration Signals ◽  
Multiple Sensors ◽  
Morphological Studies ◽  
Tool Condition ◽  
Feed Force

In the present work, an attempt has been made to monitor the tool condition status during microturning of aluminium alloy (AA 6061) using multiple sensors such as cutting force dynamometer, acoustic emission (AE) and accelerometer. The tool wear (nose wear) is correlated with surface roughness (Ra), chip width, thrust force (Fx), tangential force (Fy), feed force (Fz), AERMS and vibration signals. It is observed that Ra, chip width and cutting forces are increased with increase in the tool wear. Among the cutting forces, the tangential force (Fy) is found to be more sensitive to the tool wear status compared to that of the thrust force (Fx) and feed force (Fz). From the signal analysis, it is observed that during machining with good tool condition, the dominant frequency of the AERMS and vibration signals are found to be 81 kHz-110 kHz and 2.07 kHz-3.84 kHz respectively, whereas with the worn out tool the dominant frequencies are shifted to higher levels. Chip morphological studies indicated that favourable type of chips are formed upto 40th minute and unfavourable chips are observed from 41st minute to 60th minute.

scholarly journals Algorithm of the tool condition monitoring system based on many neural networks

Mechanik ◽  
2017 ◽  
Vol 90 (3) ◽  
pp. 220-223
Author(s):  
Sebastian Bombiński ◽  
Joanna Kossakowska
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Rbf Neural Networks ◽  
Standard Time ◽  
Tool Condition ◽  
Condition Monitoring System

Presented is a comparison of different methods of estimating tool wear – obtained for group of RBF neural networks, hierarchical methods and the standard time counting. The analysis of the signals from the machining process carried out for three different experiments, clearly demonstrating the effect of presented methods. The results obtained for group of RBF neural networks are similar to results obtained for hierarchical methods.

Smart Tool*/Smart Tool - Intelligent sensor-based tool management

2016 ◽  
Vol 106 (03) ◽  
pp. 106-110
Author(s):  
E. Abele ◽  
T. Grosch ◽  
E. Schaupp
Keyword(s):  
Tool Wear ◽  
Real Time ◽  
Industrie 4.0 ◽  
Tool Management ◽  
Intelligent Sensor ◽  
Individual Level ◽  
Tool Holder ◽  
Industrial Internet ◽  
Current Location

Im Kontext von Industrie 4.0 bietet eine Optimierung des Werkzeugmanagements zahlreiche Potentiale. Durch ein Track & Trace-System, welches in den gesamten Werkzeugkreislauf integriert wird, lässt sich der aktuelle Aufenthaltsort der Werkzeuge auf Individuumsebene in Echtzeit bestimmen. Eine im Werkzeughalter untergebrachte Sensorik liefert zusätzliche Informationen über den aktuellen Zustand der Werkzeuge, beispielsweise den Verschleiß.   In the context of the industrial internet (Industrie 4.0), tool management offers great potential. With a track&trace-system integrated in the whole tool cycle the current location of tools can be determined at individual level in real time. Furthermore, sensors placed in the tool holder provide information about the tool`s current condition, e.g. tool wear.

scholarly journals Use of Neural Networks in Tool Wear Prediction

2019 ◽  
Vol 299 ◽  
pp. 04003
Author(s):  
Juraj Kundrík ◽  
Marek Kočiško ◽  
Martin Pollák ◽  
Monika Telišková ◽  
Anna Bašistová ◽  
...  
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Production Process ◽  
Machine Tools ◽  
Cutting Tool ◽  
Production Quality ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Tool Wear Prediction ◽  
Status Data

Modern CNC machine tools include a number of sensors that collect machine status data. These data are used to control the production process and for control of the CNC machine status. No less importantpart of the production process is also a machine tool. The condition of the cutting tool is important for the production quality and its failure can cause serious problems. Monitoring the condition of thecutting tool is complicated due to its dimensions and working conditions. The article describes how the tool wear can be predicted from the measured values of vibration and pressure by using neural networks.

Effect of minimum quantity lubrication and vortex tube cooling on laser-assisted micromilling of a difficult-to-cut steel

2020 ◽  
Vol 234 (11) ◽  
pp. 1422-1432 ◽  
Author(s):  
Pushparghya Deb Kuila ◽  
Shreyes Melkote
Keyword(s):  
Tool Wear ◽  
Cutting Forces ◽  
Vortex Tube ◽  
Minimum Quantity Lubrication ◽  
Dimensional Accuracy ◽  
Burr Formation ◽  
Workpiece Material ◽  
Minimum Quantity ◽  
Tool Condition ◽  
Cooling Methods

Laser-assisted micromilling is a promising micromachining process for difficult-to-cut materials. Laser-assisted micromilling uses a laser to thermally soften the workpiece in front of the cutting tool, thereby lowering the cutting forces, improving the dimensional accuracy, and reducing the tool wear. Thermal softening, however, causes the workpiece material to adhere to the tool and form a built-up edge. To mitigate this problem and to enhance micromachinability of the workpiece in laser-assisted micromilling, this article investigates the following lubrication and cooling methods: (1) minimum quantity lubrication and (2) vortex tube cooling. Experiments utilizing the two methods are carried out on a difficult-to-cut stainless steel (A286), and the surface morphology, tool condition, burr formation, groove dimensional accuracy, surface finish, and cutting forces are analyzed. Results show that the combination of laser-assisted micromilling and minimum quantity lubrication yields the least amount of tool wear, lower resultant force, better groove dimensional accuracy, and no built-up edge. While vortex tube cooling with laser-assisted micromilling produces smaller burrs compared to minimum quantity lubrication, it yields larger changes in groove dimensions and is characterized by built-up edge formation. Possible physical explanations for the experimental observations are given.

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