Use of acoustic emission and cutting force signals to monitor built-up edge formation in stainless steel turning

Alam, Md. Shafiul; Arif, Abul Fazal M.; Veldhuis, Stephen C.

doi:10.1007/s00170-019-03607-3

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…Oraby and Hayhurst [ 15 ] employed non-linear regression analysis to develop models that can estimate tool wear/life in terms of force ratios, instead of using absolute values of force. During the turning of stainless steel, a sensor system was established with the help of neural networks to determine formation using cutting forces and AE signals [ 16 ]. Tool wear values were measured offline, and AE and power signals were measured on-line.…”

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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“…For this reason, in addition to measuring the components of the cutting force, the cutting force variations were also determined. There are many statistical features that can describe the variability or non-stationarity of the cutting force [21,23,44]. The standard deviation of the F z component signal and the resultant F a milling force was used for this purpose.…”

Section: Tool-life Testingmentioning

confidence: 99%

“…[21]. Various real-time monitoring instruments exist, such as dynamometers, current sensors, vibrations sensors and acoustic emissions [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Among many methods of online tool wear monitoring[21][22][23], the cutting forces measurements have been taken into consideration. Experiments on machining performed to determine the durability of cutting tools for uniform cutting parameters: v c = 25 m/min; f = 100 m/min; a p = 1 mm and a e = 8 mm were analysed in detail taking into account the amplitude of the components of cutting force.…”

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confidence: 99%

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Influence of machining parameters on the polymer concrete milling process

Kępczak

Zgórniak

Lajmert

et al. 2020

Int J Adv Manuf Technol

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The paper examines the wear of end mill cutters, the parameters of a workpiece's surface layer and the values of cutting forces during the machining of polymer concrete, which is one of the most difficult-to-cut materials. Increased abrasive wear of the active cutting edges is a result of the heterogeneous structure of this material, and it has a negative effect on the cutting process, the durability of the cutting tools as well as the roughness of the workpiece. It is very important to determine the machining parameters for this material from a tool durability and quality of machining point of view. The research was divided into two stages. In the first stage, the appropriate machining parameters were determined based on the measurement of the components of the cutting force. In the second stage, durability tests of four end mill cutters were carried out. During the tests, the component cutting forces, tool wear and surface roughness were measured. The highest tool durability was recorded for the monolithic cemented carbide cutter with coating NC Mill G9F42120N 4F. Tools made of high-speed steel should not be used for machining polymer concrete, as they exhibited the highest intensity of tool wear. The rapid loss of machinability of these tools leads to a rapid increase in cutting force and the roughness of the machined surface. From an economic point of view, cemented carbide end mill cutters with coatings seem to be the appropriate choice for machining polymer concrete.

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“…The tool condition is not only the wear stage but also built-up edge formation. All these phenomena and their influence on generating AE signals were considered by Ahmed et al [ 29 ]. Albers et al [ 30 ] on the basis on real time monitoring of AE proposed prediction of the product surface quality.…”

Section: Introductionmentioning

confidence: 99%

The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process

Dudzik

Labuda

2020

Materials

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Ensuring optimal turning conditions has a huge impact on the quality and properties of the machined surface. The condition of the cutting tool is one of the factors to achieve this goal. In order to control its wear during the turning process, monitoring was used. In this study, the acoustic emission method and measure of cutting forces during turning were used for monitoring that process. The research was carried out on a universal lathe center (CU500MRD type) using a Kistler dynamometer with assembled removable insert CCET09T302R-MF by DIJET Industrial CO., LTD. A dynamometer allows to measure forces Fx (radial force), Fy (feed force) and Fz (cutting force). The turning process was performed on a shaft with 60 mm diameter made of 304L stainless steel. The AE research was carried at Physical Acoustics Corporation with the kit that includes: recorder USB AE Node, preamplifier, AE-sensor VS 150M and computer with dedicated software used for recording and analyzing AE data. The aim of this paper is to compare selected diagnostic methods: acoustic emission and cutting forces measurement for monitoring wear of cutting tool edge. Analysis of the research results showed that both selected methods of monitoring the turning process allowed the determination of the beginning of the tool damage process.

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Use of acoustic emission and cutting force signals to monitor built-up edge formation in stainless steel turning

Cited by 21 publications

References 37 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

Influence of machining parameters on the polymer concrete milling process

The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process

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