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Ogochukwu Chinedum, Chukwunedum
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Electrical & Electronics Engineering Energy Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Physics Veterinary

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Multi-Criteria Performance Evaluation and Optimization of Composite Particleboard Materials: A Grey Relational Analysis Approach Ogochukwu Chinedum, Chukwunedum; Chidozie Chukwuemeka, Nwobi-Okoye; Ekwueme, Godspower Onyekachukwu; Daniel Chinazom, Anizoba
Journal of Engineering and Science Vol. 4 No. 1 (2025): June
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jes.v4i1.290

Abstract

This study presents a comprehensive multi-criteria decision analysis (MCDA) of fifteen composite particleboard materials based on their fundamental physical and mechanical properties. The evaluation utilized Grey Relational Analysis (GRA) to systematically rank particleboard compositions according to five critical performance parameters: density (D), water absorption (WA), thickness swelling (TS), modulus of rupture (MOR), and modulus of elasticity (MOE). The Grey Relational Grade (GRG) methodology revealed significant performance variations among different particleboard compositions. The analysis identified sawdust waste reinforced with plastic-based resin (waste styrofoam) as the optimal composition, achieving the highest GRG value of 0.8143 (81.43%), indicating superior overall performance characteristics. Conversely, cement-bonded particleboard manufactured from pine (Pinus caribaea M.) sawdust and coconut husk/coir (Cocos nucifera L.) demonstrated the lowest performance with a GRG value of 0.4279 (42.79%). The research methodology employed systematic normalization procedures and grey relational coefficient calculations to establish comprehensive performance rankings. Results indicate that material composition and binder selection significantly influence particleboard performance characteristics, with plastic-based resins demonstrating superior mechanical properties compared to traditional formaldehyde-based binders. This investigation provides a quantitative framework for optimizing composite particleboard manufacturing processes and material selection strategies. The findings contribute to sustainable materials engineering by identifying high-performance alternatives utilizing waste materials, thereby supporting circular economy principles in the wood products industry. The established ranking system serves as a decision-support tool for manufacturers seeking to optimize particleboard compositions for specific applications while maintaining cost-effectiveness and environmental sustainability
Multi-Criteria Performance Evaluation and Optimization of Composite Particleboard Materials: A Grey Relational Analysis Approach Ogochukwu Chinedum, Chukwunedum; Chidozie Chukwuemeka, Nwobi-Okoye; Ekwueme, Godspower Onyekachukwu; Daniel Chinazom, Anizoba
Journal of Engineering and Science Vol. 4 No. 1 (2025): June
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jes.v4i1.290

Abstract

This study presents a comprehensive multi-criteria decision analysis (MCDA) of fifteen composite particleboard materials based on their fundamental physical and mechanical properties. The evaluation utilized Grey Relational Analysis (GRA) to systematically rank particleboard compositions according to five critical performance parameters: density (D), water absorption (WA), thickness swelling (TS), modulus of rupture (MOR), and modulus of elasticity (MOE). The Grey Relational Grade (GRG) methodology revealed significant performance variations among different particleboard compositions. The analysis identified sawdust waste reinforced with plastic-based resin (waste styrofoam) as the optimal composition, achieving the highest GRG value of 0.8143 (81.43%), indicating superior overall performance characteristics. Conversely, cement-bonded particleboard manufactured from pine (Pinus caribaea M.) sawdust and coconut husk/coir (Cocos nucifera L.) demonstrated the lowest performance with a GRG value of 0.4279 (42.79%). The research methodology employed systematic normalization procedures and grey relational coefficient calculations to establish comprehensive performance rankings. Results indicate that material composition and binder selection significantly influence particleboard performance characteristics, with plastic-based resins demonstrating superior mechanical properties compared to traditional formaldehyde-based binders. This investigation provides a quantitative framework for optimizing composite particleboard manufacturing processes and material selection strategies. The findings contribute to sustainable materials engineering by identifying high-performance alternatives utilizing waste materials, thereby supporting circular economy principles in the wood products industry. The established ranking system serves as a decision-support tool for manufacturers seeking to optimize particleboard compositions for specific applications while maintaining cost-effectiveness and environmental sustainability
PREDICTING CUSTOMER LOYALTY IN AUTOMOTIVE SERVICES: EVIDENCE FROM MACHINE LEARNING ON SATISFACTION AND SERVICE COSTS IN NIGERIA Onyekachukwu Ekwueme, Godspower; Chukwuemeka Godwin, Harold; Chukwu Callistus Nkemjika; Ogochukwu Chinedum, Chukwunedum
SciencePlus Vol. 1 No. 2 (2025): SciencePlus
Publisher : Barkah Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Customer loyalty in Nigeria’s automotive service sector has become volatile due to digital competition, variable pricing, and shifting satisfaction patterns. Traditional regression models fail to capture the nonlinear links between satisfaction, cost, and loyalty. This study used machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), to predict loyalty using customer satisfaction, service cost, and behavioral indicators from Anaval Mechanic Workshop (January–December 2023). Model performance was evaluated using accuracy and Area Under the Curve (AUC). XGBoost performed best (AUC = 0.985; accuracy = 97.1%), followed by RF (AUC = 0.962) and SVM (AUC = 0.485). Findings confirm satisfaction, cost, and uncertainty as key loyalty drivers, highlighting XGBoost’s superiority in modeling complex satisfaction–cost dynamics
Co-Authors Chidozie Chukwuemeka, Nwobi-Okoye Chukwu Callistus Nkemjika Chukwuemeka Godwin, Harold Daniel Chinazom, Anizoba Ekwueme, Godspower Onyekachukwu Onyekachukwu Ekwueme, Godspower