Sensor Signal Segmentation for Tool Condition Monitoring

Bombiński, Sebastian; Błażejak, Krzysztof; Nejman, Mirosław; Jemielniak, Krzysztof

doi:10.1016/j.procir.2016.03.203

Cited by 34 publications

(13 citation statements)

References 8 publications

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“…The resulting deformation causes a gradual breakdown of the tool, and deteriorates its cutting [ 9 ]. The main goal is to give a warning before a certain level of tool wear is reached that could cause sudden tool failure [ 10 ]. During the machining, many parameters that change with the increase of wear, such as cutting forces, cutting temperatures, vibrations in the tool, the sound, chip and current, can be used as information sources to determine the severity of tool wear.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

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Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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“…The developed TCM only requires the operator to mark tool changes. The basis of the algorithms consists of the following steps: Detection of cutting, i.e., the system automatically recognizes cutting and separates cutting periods into signals from fast feeds, as well as run-up and run-down; Automatic selection of signal segments with a fixed waveform [ 51 ]; Determination of hundreds of signal features from the selected signal segments; Automatic selection of useful features (correlated with the tool condition) and elimination similar features (correlated with each other) for each individual segment [ 52 ]; Determination of the used part of tool life for each feature separately and integration of indications into one evaluation, displayed to an operator as a percentage of tool wear. …”

Section: Edge-computing-based Tool Condition and Cutting Process Modulementioning

confidence: 99%

Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry

Bombiński

Kossakowska

Nejman

et al. 2021

Sensors

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In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining; specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt & Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.

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“…In the Department of Automation and Metal Cutting at Warsaw University of Technology, an algorithm was developed for the automatic selection of segments from stable machining fragments well suited for tool condition diagnostics. A detailed description of the segment selection procedure is described in [66]. Each segment represented a separate diagnostic signal, subjected to determination and SFs.…”

Section: Signal Processing 221 Preprocessing-signal Segmentationmentioning

confidence: 99%

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

2021

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There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.

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Sensor Signal Segmentation for Tool Condition Monitoring

Cited by 34 publications

References 8 publications

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

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