Investigation of progressive tool wear for determining of optimized machining parameters in turning

Kuntoğlu, Mustafa; Sağlam, Hacı

doi:10.1016/j.measurement.2019.04.022

Cited by 152 publications

(82 citation statements)

References 31 publications

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“…The sustainability of a production chain becomes very important in mass production, especially in today’s competitive manufacturing industry [ 2 ]. For this reason, there is a need for a TCMS (Tool Condition Monitoring System) that works as a source of information related to machine tools and processes [ 3 , 4 ]. Therefore, the integration of various types of sensors into machine tools is essential for them to provide their analysis and optimal values under different conditions of product manufacturing.…”

Section: Introductionmentioning

confidence: 99%

“…Sensors can monitor a mechanical system to detect the changes and transmit the information for further evaluation with computer software. From this point of view, it is seen that it has relevance to sensor system [ 3 , 13 , 14 ] and wear-roughness inspection [ 48 , 49 ] studies. Investigating the effects of cutting parameters via ANOVA provides information on the availability of each sensor’s data in the monitoring of response parameters, i.e., flank wear and SR.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…In Table 8 a comparison of the experimental and predicted results is shown for the surface roughness and vibration components. To check the validity of the model, it is necessary to compare the predicted values with experimental ones [ 17 ]. The obtained results are in good agreement with the experimental values which can be obtained from the acceptable error rates within 1–10%.…”

Section: Resultsmentioning

confidence: 99%

“…ANOVA implies the significance of design parameters on the investigated response parameter over diverse statistical value [ 17 ]. The importance of design or control parameters can be determined and confirmed via these statistical parameters [ 55 ].…”

Section: Methodsmentioning

confidence: 99%

“…R a value is determined according to workpiece specifications which can be detailed by the producer or the consumer [ 16 ]. The desired surface quality of a machined part can be achieved by finding the optimum cutting conditions [ 17 ]. A good surface finish provides better mechanical properties for a machine element related to the useful remaining tool life [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

et al. 2020

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AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (Ra) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as Vc = 190 m/min, f = 0.06 mm/rev, κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.

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Resolution of the Friction Coefficient of Adhesion Under Cutting

Івченко

Zhyhylii

Zaloha

et al. 2020

Lecture Notes in Mechanical Engineering

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Investigation of progressive tool wear for determining of optimized machining parameters in turning

Cited by 152 publications

References 31 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

Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

Resolution of the Friction Coefficient of Adhesion Under Cutting

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