Effect of Tool Vibration on Flank Wear and Surface Roughness During High-Speed Machining of 1040 Steel

Swain, Santosh Kumar; Panigrahi, Isham; Sahoo, Ashok Kumar; Panda, Amlana; Kumar, Ramanuj

doi:10.1007/s11668-020-00905-x

Cited by 32 publications

(16 citation statements)

References 29 publications

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“…Thus, resulting in lower surface roughness of the machined component. 66 Swain et al 67 experimentally found that the optimum machining temperature is necessary for the improvement of both surface quality and tool life. As per the experimental research, when the machining is done at high cutting speed with efficient cooling-lubrication strategy, due to control machining temperature as well as thermal softening effect leading to decrease in surface hardness.…”

Section: Analysis On Surface Roughnessmentioning

confidence: 99%

Turning of hardened AISI H13 steel with recently developed S3P-AlTiSiN coated carbide tool using MWCNT mixed nanofluid under minimum quantity lubrication

Mahapatra

Das

Jena

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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This article aims to analyze the tool vibration, surface roughness, and chip morphology in hard turning of hot work AISI H13 steel under multi-walled carbon nanotubes mixed nanofluid with minimum quantity lubrication. Recently developed new-generation nanocomposite AlTiSiN coating deposited on carbide insert via scalable pulsed power plasma (S3P) strategy is used as cutting tool material. The experiments associated with 30 number of trials are performed by considering various machining parameters that includes cutting speed, nose radius, depth of cut, and feed. In perspective of predictive modeling and multi-response optimization, response surface methodology has been engaged as a means to minimize the tool vibration and surface roughness. At last, with the measured tool life and based on the Gilbert’s machining economic model, a distinctive cost analysis has been implemented to demonstrate the cost-effectiveness of coated carbide tool in hard turning. Statistical analysis confirms that nose radius with 36.65% has the highest contribution for surface roughness and cutting speed with 53.88% has the highest contribution for tool vibration. Increased flank wear and tool vibration are responsible for degradation of machined surface finish. Machining under nanofluid-MQL, chip morphology revealed the production of segmented type serrated saw-toothed chips. At higher cutting speed resulted: (a) obvious shape of saw tooth chip, (b) increase of distance among saw tooth, (c) reduction in chip segmentation frequency, (d) decrease of chip thickness. At optimum combination of process variables ( v = 55 m/min, d = 0.2 mm, r = 1.2 mm, f = 0.07 mm/rev), the life of AlTiSiN coated carbide tool is found to be 42 min under nanofluid-MQL and estimated the total machining cost expenditure per finished part of Rs. 153.52. New-generation AlTiSiN coated carbide tools can be competently and productively utilized for machining of hot work tool steel under ecologically cognizant minimum quantity cooling-lubrication, is the most accepted in industrial applications.

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Section: Analysis On Surface Roughnessmentioning

confidence: 99%

Turning of hardened AISI H13 steel with recently developed S3P-AlTiSiN coated carbide tool using MWCNT mixed nanofluid under minimum quantity lubrication

Mahapatra

Das

Jena

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Again, the modulus (absolute value) of deviation assists calculation of signal to noise ratio. Finally, the CQL is optimized reduced by Taguchi technique [55][56][57][58]. Taguchi technique is inadequate to resolve a multiple response optimization dilemmas.…”

Section: Wpca Optimizationmentioning

confidence: 99%

“…Taguchi technique is inadequate to resolve a multiple response optimization dilemmas. So as to conquer this drawback, this multivariate WPCA technique has been combined with Taguchi approach in the research and the stepwise procedure for multiple output responses optimization using WPCA is given in Figure 16 [58].…”

Section: Wpca Optimizationmentioning

confidence: 99%

Investigation on hard turning temperature under a novel pulsating MQL environment: An experimental and modelling approach

Roy

Kumar

Sahoo

et al. 2020

Mechanics & Industry

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Generation of total heat in hard turning largely influenced the cutting tool wear, tool life and finishing quality of work-surface. Thus, the measurement of this heat in terms of temperature becomes a necessity for achieving favourable machining performances. Therefore, this work presents a novel study on temperature measurement in three different zones during hard turning operation of 4340 grade steel under pulsating MQL environment. Temperatures are measured at three different locations namely chip-tool interface, flank face, and machined work surface (near to tool-work contact) and the location wise temperature is termed as chip tool interface temperature (T), flank face temperature (Tf) and machined work surface temperature (Tw) correspondingly. The temperature T and Tf are measured with help of K-type thermocouple while Tw is measured by Fluke make infra-red thermal camera. Pulsating MQL significantly reduced the temperature as the maximum temperature is noticed 110 °C which corresponds to chip-tool interface temperature (T) at highest speed (200 m/min) condition. In each test, the order of temperature follow the trend as: T > Tf > Tw. Considering average of all 16 temperatures, T is 14.42% greater than Tf and 39.36% larger than Tw while Tf is 21.79% greater than Tw. Experimental results concludes that the cutting speed is the most influencing factor followed by depth of cut for both T and Tf, whereas depth of cut is the most influencing factor for Tw. Further, these temperatures are predicted using linear regression, and absolute mean error (MAE) for responses T, Tf, and Tw is noticed as 1.848%, 0.542%, and 3.766% individually. Additionally, the optimum setting of input terms are estimated using WPCA (weighted principal component analysis) and found to be dc1 (0.1 mm) − fr2 (0.08 mm/rev) − vc2 (100 m/min) − Pt2 (2 s).

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“…3 Because of its extensive use, several studies have been conducted to investigate the machinability of AISI 1040 steel. [4][5][6][7] Using ML approaches has resulted in a significant improvement in the quality of the machining process for AISI 1040 steel. 8,9 Due to the time and technical constraints that exist in the industry today, conducting experiments at all parameters is not practicable.…”

Section: Introductionmentioning

confidence: 99%

The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Agrawal

Mukhopadhyay

2022

Proceedings of the Institution of Mechanical Engineers, Part J:

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AISI 1040 steel offers a wide range of industrial applications due to its mechanical characteristics and applicability. The present work investigates the wear performance of AISI 1040 steel under dry sliding conditions and its optimization using combined machine learning (ML) and metaheuristic algorithm. Sliding wear test were carried out on a pin-on-disc tribometer by varying the load (10–100 N), sliding speed (0.5–1.5 m/s) and sliding distance (400–1000 m). The test parameters were varied at three levels. Experiments were carried out following combinations in Taguchi's L27 orthogonal array (OA). Artificial neural network (ANN) was used to model the process parameters with wear rate. The trained network was optimized using genetic algorithm (GA) to predict optimal wear rate. This methodology has been termed as ANN-GA method. The results were compared with conventional Taguchi based and regression based GA optimization. A significant reduction in the wear rate could be realized due to optimization using ANN-GA method. This work also examined the corrosion behaviour of AISI 1040 steel exposed to 3.5% NaCl, 3.5% NaOH, 0.5 M H2SO4 and 3.5% NaCl + 0.5 M H2SO4 to simulate various corrosive environments encountered in industrial applications. A nobler corrosion potential was obtained in 3.5% NaOH. Investigations of corroded samples showed pitting corrosion in 3.5% NaCl, 0.5 M H2SO4 as well as combined chloride and sulphate attack. On the other hand, negligible corrosion was observed in 3.5% NaOH.

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Effect of Tool Vibration on Flank Wear and Surface Roughness During High-Speed Machining of 1040 Steel

Cited by 32 publications

References 29 publications

Turning of hardened AISI H13 steel with recently developed S3P-AlTiSiN coated carbide tool using MWCNT mixed nanofluid under minimum quantity lubrication

Turning of hardened AISI H13 steel with recently developed S3P-AlTiSiN coated carbide tool using MWCNT mixed nanofluid under minimum quantity lubrication

Investigation on hard turning temperature under a novel pulsating MQL environment: An experimental and modelling approach

The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

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