Experimental investigation of cutting parameters using nano lubrication on turning AISI 1040 steel

Kanth, V. Krishna; Sreeramulu, D.; Srikiran, S.; Kumar, M. Pradeep; Jagdeesh, K.E.; Govindh, B.

doi:10.1016/j.matpr.2019.06.323

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Economic analysis for the application of nano cutting fluids is done to assess the viability of these fluids in the industry. Kanth et al [5] investigated the use of a mixture of nano crystalline graphite and sunflower oil as an alternative for cutting fluids for an improved surface roughness finish in a turning process.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

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Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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“…[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. Although research began with the use of generalised empirical equation systems based on expanded Taylor's equations to determine appropriate process conditions.…”

Section: Introductionmentioning

confidence: 99%

“…4–7 Using ML approaches has resulted in a significant improvement in the quality of the machining process for AISI 1040 steel. 8,9…”

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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“…The authors compared the mentioned system with conventional and dry machining conditions and showed the superiority of the MQL system by examining the surface roughness, tool wear, cutting force, and chip morphology. Kanth et al [31] experimentally investigated the usage of nanolubrication with MQL during the turning of AISI 1040 steel. The surface roughness and temperature were included as the performance measures and optimum oil and powder mixtures were obtained for maximal machinability.…”

Section: Introductionmentioning

confidence: 99%

The Effects of MQL and Dry Environments on Tool Wear, Cutting Temperature, and Power Consumption during End Milling of AISI 1040 Steel

Salur

Kuntoğlu

Aslan

et al. 2021

Metals

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Minimum quantity lubrication (MQL) is a sustainable method that has been efficiently applied to achieve machinability improvements with various materials in recent years, such as hardened steels, superalloys, soft metals, and composites. This study is the first to focus on the performance evaluation of MQL and dry milling environments with AISI 1040 steel. The tool wear, cutting temperature, and power consumption were considered as the quality responses while cutting speed, feed rate and machining environment are taken as input parameters. The effects of the influential factors are analyzed using analysis of variance (ANOVA) and bar charts. Additionally, Taguchi signal-to-noise (S/N) ratios are utilized in order to determine the optimum parameters for the best quality responses. The results show that the MQL system provides better performance compared to dry milling by reducing the tool wear, cutting temperature, and power consumption. According to the ANOVA results, the cutting environment affects the cutting temperature (37%) and power consumption (94%), while cutting speed has importance effects on the tool wear (74%). A lower cutting speed (100 m/min) and feed rate (0.10 mm/rev) should be selected under MQL conditions to ensure minimum tool wear and power consumption; however, a higher feed rate (0.15 mm/rev) needs to be selected along with a low cutting speed and MQL conditions to ensure better temperatures. A comparative evaluation is carried out on the tool wear, cutting temperature, and power consumption under MQL and dry environments. This investigation is expected to contribute to the current literature, highlighting the superiority of sustainable methods in the milling of industrially important materials.

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Experimental investigation of cutting parameters using nano lubrication on turning AISI 1040 steel

Cited by 10 publications

References 7 publications

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

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

The Effects of MQL and Dry Environments on Tool Wear, Cutting Temperature, and Power Consumption during End Milling of AISI 1040 Steel

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