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All Journal International Journal of Mechanical Engineering Technologies and Applications (MECHTA) Indonesian Journal of Industrial Engineering & Management International Journal of Industrial Engineering and Engineering Management
Jose, Swaminathan
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Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Improving Thermal Friction Drilling Performance of AISI 304 Stainless Steel Using the Harris Hawk Optimization Method Ogunmola, Bayo Yemisi; Alozie, Nehemiah Sabinus; Adeyinka, Oluwo; Nwankiti , Ugochukwu Sixtus; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 2 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i2.7743

Abstract

Presently, in friction drilling optimization schemes, quick convergence of solutions and simplicity of methods are still challenging. These issues are drawbacks in obtaining the maximum potential benefits from the optimization process. Therefore, this paper applies a new optimization method, Harris Hawk optimization to the thermal drilling process of AISI 304 stainless steel. The algorithm minimizes the axial force, determination error, radial force, and radial error and maximizes the bushing length as the major output of the process. The proposed approach was tested with experimental data obtained from the literature. The obtained results indicate that the optimal production is feasible. An example is given here of the results of the input parameters for the minimum axial force, which is as follows: After 500 iterations, the optimal axial force yields a tool cylindrical region diameter of 5.78593 mm, a friction angle of 60 degrees, a friction contact area ratio of 57.7082, workpiece thickness of 3 mm, feed rate of 140 mm/min and rotational speed of 3002.85 rpm, which can be applied. The results assist engineers in implementing optimal conditions for the drilling process. The outcome of this study strengthens decisions to establish thresholds of values that are less or more than expected thereby providing a basis for comparison, reward, and reprimand for workers. Thus the drilling process can be optimized.
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 Adeboye, Busayo Adedeji, Kasali Aderinmoye Adedeji, Wasiu Oyediran Adekola, Anthony Ozimu Adekoya, Abdulganiyu Adegboyega Adeniran, Mofoluwaso Kehinde Aderibigbe, Samuel Bolaji Adeyemi, Olusola Michael Adeyinka, Oluwo Agada, Alexander Iwodi Alozie, Nehemiah Sabinus Ayanladun, Chris Abiodun Benrajesh, Pandiaraj Emorinken, Samuel Olayinka Francis, Osita Prince Ibitoye, Ayomide Sunday Nwankiti , Ugochukwu Sixtus Nwankiti, Ugochukwu Sixtus Odudare, Samson Oluwaseun Ogunmola, Bayo Yemisi Oke, Sunday Ayoola Okwo, Jeremiah Ugo Olowu, Joseph Kolawole Oluwo, Adeyinka Onitiri, Modupe Adeoye Oyekeye, Manasseh Olusegun Oyelami, Seun Oyetunji, Elkanah Olaosebikan Ozule, Chukwuka Prosper Rajan, John Taiwo, Emmanuel Oluwatobi Udoibe, Ndifreke John Umoh, Akwaeno Uduak