Wear and corrosion characterisation of AISI 1030, AISI 1040 and AISI 1050 steel coated with Shielded Metal Arc Welding (SMAW) and Plasma Transfer Arc (PTA) methods

Öteyaka, Mustafa Özgür; Arslan, Aslı; Çakir, Fatih

doi:10.1007/s12046-021-01661-w

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…6H 2 O apart from iron oxides. 24 A two passive zone was also observed due to evolution of pitting and re-passivation of pitting and so on. A similar observation is made in the present work in Figure 13 .…”

Section: Resultsmentioning

confidence: 97%

“…Electrochemical corrosion testing is equally essential because wearing of parts has both mechanical and chemical aspects. Several electrochemical investigations were conducted to investigate the corrosion behaviour of AISI 1040 steel 24 and particularly austenitic grade steel. 25,26 The corrosion in various media was investigated in a quest to mitigate surface damage, dimensional changes and improve service life of machine components and structures.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 97%

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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Metallurgical characterisation of multi-track Stellite 6 coating on SS316L substrate

Bhoskar

Калыанкар

Deshmukh³

2022

Canadian Metallurgical Quarterly

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Effect of Welding Polarity on Mechanical Properties of Submerged Arc Welded Railway Vehicle Wheels

Goo

Seo

Lee³

2022

Metals

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When a railway vehicle moves on a curved rail, sliding contact between the rail head side and wheel flange causes wear on the wheel flange. Traditionally, a wheel with thinned flange is machined to get a minimum flange thickness specified for structural safety. This operation reduces the rim thickness and shortens the life of the wheel. In the present study, the thinned flanges were hard-faced by submerged arc welding. A welding wire, which has good weldability to the base material of the wheel and does not generate thermal cracking, was developed. The effects of welding polarity on the microstructure, hardness, friction coefficient, and wear characteristics of the welded wheel were studied. The hardness of the wheel welded with reverse polarity was similar to that of welded with straight polarity. The wear rates of the wheel disc welded with reverse polarity and its counterpart rail disc were 11% and 27% lower than those welded with straight polarity. Delamination wear due to subsurface crack propagation and oxidation wear were mixed. The hardness of the rail before the wear test was in the range of 250–300 HV. After the wear test, it soared to 500 HV.

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Wear and corrosion characterisation of AISI 1030, AISI 1040 and AISI 1050 steel coated with Shielded Metal Arc Welding (SMAW) and Plasma Transfer Arc (PTA) methods

Cited by 3 publications

References 30 publications

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

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

Metallurgical characterisation of multi-track Stellite 6 coating on SS316L substrate

Effect of Welding Polarity on Mechanical Properties of Submerged Arc Welded Railway Vehicle Wheels

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