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All Journal International Journal of Industrial Engineering and Engineering Management
Adekola, Anthony Ozimu
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Industrial & Manufacturing Engineering

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Optimization of Process Parameter of Tungsten Inert Gas Welding for Austenitic Stainless Steel using Grey Wolf Optimization Adekola, Anthony Ozimu; Oke, Sunday Ayoola; Nwankiti, Ugochukwu Sixtus
International Journal of Industrial Engineering and Engineering Management Vol. 4 No. 2 (2022)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v4i2.5748

Abstract

Optimization of welding parameters is essential on austenitic stainless steel for industrial applications since they declare the best parameters compared with prioritized constraints. However, available optimization methods, such as the Taguchi method, widely used in this research domain, are weak. Their results are merely comparative and fail to particularly show the specific factor that displays the highest performance in the process. In this paper, the aim is specifically to position the parameters in order of importance and present them in a grey wolf optimization framework. The ultimate tensile strength and yield strength were optimized, and the optimization was conducted using the C++ programming code. Literature data were analyzed for austenitic stainless steel under un-notched/smooth and notched specimen conditions. Empirical models were developed for the ultimate tensile strength and yield strength, among other principal criteria of the material. For the ultimate tensile strength, the best value was obtained at the 100th iteration as 640.75. For the yield strength, the best value of 394.98 was obtained after 100 iterations. A value of 31.07 for the PE was obtained. These results are for the unnotched specimens. However, the PE, NTS, and yield strength values for the notched specimens are 16.32, 780.12, and 494.46, respectively. Based on the findings of this study and compared with other optimization methods, the optimal parameters and outputs predicted using the grey wolf optimization approach were found to produce reliable results. This shows that the grey wolf optimization approach is a good option for predicting the optimal parameters of the tungsten arc welding process by utilizing austenitic stainless steel. The usefulness of this research effort is to help process engineers to implement robust and effective cost decisions in the production of materials based on austenitic stainless steel.
Optimization of Process Parameter of Tungsten Inert Gas Welding for Austenitic Stainless Steel using Grey Wolf Optimization Adekola, Anthony Ozimu; Oke, Sunday Ayoola; Nwankiti, Ugochukwu Sixtus
International Journal of Industrial Engineering and Engineering Management Vol. 4 No. 2 (2022)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v4i2.5748

Abstract

Optimization of welding parameters is essential on austenitic stainless steel for industrial applications since they declare the best parameters compared with prioritized constraints. However, available optimization methods, such as the Taguchi method, widely used in this research domain, are weak. Their results are merely comparative and fail to particularly show the specific factor that displays the highest performance in the process. In this paper, the aim is specifically to position the parameters in order of importance and present them in a grey wolf optimization framework. The ultimate tensile strength and yield strength were optimized, and the optimization was conducted using the C++ programming code. Literature data were analyzed for austenitic stainless steel under un-notched/smooth and notched specimen conditions. Empirical models were developed for the ultimate tensile strength and yield strength, among other principal criteria of the material. For the ultimate tensile strength, the best value was obtained at the 100th iteration as 640.75. For the yield strength, the best value of 394.98 was obtained after 100 iterations. A value of 31.07 for the PE was obtained. These results are for the unnotched specimens. However, the PE, NTS, and yield strength values for the notched specimens are 16.32, 780.12, and 494.46, respectively. Based on the findings of this study and compared with other optimization methods, the optimal parameters and outputs predicted using the grey wolf optimization approach were found to produce reliable results. This shows that the grey wolf optimization approach is a good option for predicting the optimal parameters of the tungsten arc welding process by utilizing austenitic stainless steel. The usefulness of this research effort is to help process engineers to implement robust and effective cost decisions in the production of materials based on austenitic stainless steel.
Coating Adherence Optimization for 67Ni18Cr5Si4B Alloy Powder by High-Velocity Oxygen Fuel Spray Based on the Grey Wolf Algorithm Method Adekola, Anthony Ozimu; Ogunmola, Bayo Yemisi; Onitiri, Modupe Adeoye; Alozie, Nehemiah Sabinus; Oluwo, Adeyinka; Rajan, John; Jose, Swaminathan; Oke, Sunday Ayoola
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 2 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i2.7874

Abstract

Adhesion engineers increasingly use coatings in industrial equipment on gas turbine blades and vanes because of the benefits of protection against thermal stresses, oxidation, and hot corrosion. However, the coating process has suffered sub-optimal value determination, posing a serious threat to the economics of coating. While the prevailing approach of introducing the Taguchi method appears effective in resolving this issue, it sacrifices convergence speed and multiple optimization solutions. Thus, the grey wolf algorithm is proposed to optimize the coating of 67Ni18Cr5Si4B alloy powder process parameters, including powder feed rate, spray velocity, and spray distance. The high-velocity oxygen fuel spray was used, and the objectives were good microhardness, adhesion strength, and porosity. The optimal value to obtain the best coating for each of the responses was given as 85MPa for the adhesion strength, 0.684909% porosity, and 583.04HV microhardness. The present study offers important insights into the optimization thresholds to help the components development process. The quantitative form of this work is new. Fast convergence solutions offered by metaheuristics such as the grey wolf optimization algorithm are rarely found in the literature.
Co-Authors Alozie, Nehemiah Sabinus Jose, Swaminathan Nwankiti, Ugochukwu Sixtus Ogunmola, Bayo Yemisi Oke, Sunday Ayoola Oluwo, Adeyinka Onitiri, Modupe Adeoye Rajan, John