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All Journal International Journal of Industrial Engineering and Engineering Management
Modupe Adeoye Onitiri
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria
Industrial & Manufacturing Engineering

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Coating Adherence Optimization for 67Ni18Cr5Si4B Alloy Powder by High-Velocity Oxygen Fuel Spray Based on the Grey Wolf Algorithm Method Anthony Ozimu Adekola; Bayo Yemisi Ogunmola; Modupe Adeoye Onitiri; Nehemiah Sabinus Alozie; Adeyinka Oluwo; John Rajan; Swaminathan Jose; Sunday Ayoola Oke
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.
Combined Application of CRITIC and Back Propagation Neural Network Prediction Method for Vehicle Emission Parametric Evaluation in Logistic Networks and Distribution Systems Sunday Ayoola Oke; Alexander Iwodi Agada; Bayo Yemisi Ogunmola; Henry Ogbemudia Omoregbee; Modupe Adeoye Onitiri; Nehemiah Sabinus Alozie; John Rajan; Swaminathan Jose; Pandiaraj Benrajesh
International Journal of Industrial Engineering and Engineering Management Vol. 8 No. 1 (2026)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v8i1.9607

Abstract

Logistic network and distribution services in the packing industry generate vast amounts of vehicle emissions, which are difficult to control due to the lack of scientific guidance. This paper predicts the emissions from vehicles engaged in logistic networks. The CRITIC (Criteria Importance Through Inter-criteria Correlation) method is integrated with the backpropagation neural network (BPNN) to analyze the input parameters of the emissions process from vehicles plying roads in the packing industry. The packing industry transports goods in packages and delivers them to customers in a road network. The application of the integrated CRITIC-BPNN method is demonstrated by using the dataset of a packing industry in India, obtained from the literature. The results revealed how the packing industry can translate its environmental control strategy into a parametric framework, yielding diverse outputs to assist in selecting optimal decisions. The proposed method exhibits unique characteristics. (1) It emerges as the first decision-making tool for objective criteria-based vehicle emission process control and could serve as a tool for logistics planning. (2) It employs integrated ideas of multi-criteria analysis and neural networks. (3) Objective and optimal emission analysis decisions are advanced through an analysis of multi-criteria tools. By employing the CRITIC method, the conflicting objectives of the emission process are successfully tackled and synchronized objectively to arrive at robust weights used in the back propagation neural network method for the final prediction. Moreover, the neural network method uses intelligence to gather data and translate it into usable forms to predict vehicle emissions.
Co-Authors Adeyinka Oluwo Alexander Iwodi Agada Anthony Ozimu Adekola Bayo Yemisi Ogunmola Henry Ogbemudia Omoregbee John Rajan John Rajan Nehemiah Sabinus Alozie Pandiaraj Benrajesh Sunday Ayoola Oke Swaminathan Jose