Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool

Patel, Vallabh D.; Gandhi, Anish H.

doi:10.1016/j.measurement.2019.01.077

Cited by 56 publications

(22 citation statements)

References 13 publications

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“…Linear relationship was found between process parameters and surface roughness. 20 The tool wear can be indirectly monitored with cutting force, vibration and surface roughness of the machined components 21 with decision-making algorithms like with NN, SVM, 22 self-organising map 23 radial bias function 24 and adaptive neuro-fuzzy inference system (ANFIS). 25…”

Section: Introductionmentioning

confidence: 99%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time.

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Section: Introductionmentioning

confidence: 99%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Therefore, in some cases, observations do not agree with predictions [21]. Although a lot of literature suggests that process parameters, such as workpiece and cutting tool characteristics, have a decisive influence on the generation of surface roughness [22], [23], their role in surface roughness mechanisms remains unknown. As there are many factors involved that have complex interactions, it is difficult to generate explicit analytical models for hard turning processes.…”

Section: B Surface Roughnessmentioning

confidence: 99%

“…Therefore, in order to obtain more information related to surface roughness states and effectively analyze it for online surface roughness prediction, soft computing techniques, or called indirect measurement methods in this article, are widely employed in research and development. Such techniques can predict surface roughness without interfering with the hard turning process, thereby increasing efficiency and allowing online adjustments [22]. To achieve this, machining status information such as vibrations, cutting forces, images, electric current, cutting heat, acoustic emissions, sound, and chip formation [23]- [25] can be used to monitor the quality of the machining process.…”

Section: B Surface Roughnessmentioning

confidence: 99%

Soft Computing Techniques for Surface Roughness Prediction in Hard Turning: A Literature Review

Gao

Zhao

2019

IEEE Access

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Hard turning has become an attractive alternative to the more time-consuming and costly grinding technique. Unfortunately, high-quality prediction of the surface roughness generated during hard turning is difficult due to the technical complexities involved. Hence, it is currently receiving much research attention. The objective of this paper is to survey the current state of the soft computing techniques for surface roughness prediction in hard turning. It focuses on three areas: data acquisition, feature selection, and prediction model of surface roughness. First, the characteristics of hard turning and surface roughness are introduced, and a framework of the soft computing techniques is presented. Then, the three key areas are surveyed thoroughly. Finally, the recommendations and challenges faced by industry and academia are discussed, and the conclusions are drawn.INDEX TERMS Surface roughness prediction, soft computing techniques, hard turning, review.

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“…Several factors such as cutting parameters, workpiece, and tool variables affect the surface roughness and tool wear in the hard turning process (Salimiasl and Rafighi 2017; Patel and Gandhi 2019;Sarnobat and Raval 2019). Cutting parameters consist of feed rate, cutting speed, and depth of cut.…”

Section: Introductionmentioning

confidence: 99%

Investigation of tool wear, surface roughness, sound intensity, and power consumption during hard turning of AISI 4140 steel using multilayer-coated carbide inserts

Şahinoğlu

Rafighi

2021

JER is an international, peer-reviewed journal that publishes f

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The present study investigated the machinability aspects, namely, surface roughness, sound intensity, power consumption, and crater wear, during dry turning of hardened AISI 4140 steel (63 HRC) employing (TiCN/Al2O3/TiN) multilayer-coated carbide inserts under dry cutting condition. The relationship between machining parameters and output parameters was determined using the Taguchi design. The analysis of variance was employed to evaluate the contributions of input parameters on output parameters. The main effect plots illustrated the impacts of cutting speed, feed, and depth of cut on response variables. Results show that the feed was the most dominant factor that affects surface roughness. Increasing the feed value increases the surface roughness, power consumption, and sound intensity. In the other part of this study, the constant values for feed (0.3 mm/rev), depth of cut (0.7 mm), and cutting speed (150 m/min) have been selected to evaluate a tool life that has 0.3 mm crater wear criteria. The results indicated that multilayer-coated carbide inserts presented very good tool life and reached 0.3 mm in 90 min. The experimental study results showed that chipping and abrasion were found to be the significant wear mechanism during hard turning of AISI 4140 steel. The cutting speed was the most significant parameter on the tool wear, although high cutting speed results the good surface finish but adversely increases the tool crater wear.

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Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool

Cited by 56 publications

References 13 publications

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Soft Computing Techniques for Surface Roughness Prediction in Hard Turning: A Literature Review

Investigation of tool wear, surface roughness, sound intensity, and power consumption during hard turning of AISI 4140 steel using multilayer-coated carbide inserts

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