Garuda - Garba Rujukan Digital

Optimizing the Parameters of Carbon Fiber Reinforced Plastic Composite Drilling Process Using Signal-to-noise Ratio-based Grey Wolf Optimization Algorithm Taiwo, Emmanuel Oluwatobi; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.7691

This study aims to develop an optimization scheme that contributes to the production of carbon fiber-reinforced plastics using the grey wolf optimization approach. Different from other optimization schemes such as the Taguchi method, which takes some time to compute and use, this grey wolf optimization approach introduced a fast convergence scheme to reduce computation time thereby making its implementation in the factory very interesting. Data used for the analysis was obtained from a doctoral thesis via an experimental approach. Four responses were considered in this work, namely the torque, delamination at entry and exit, eccentricity and thrust force. A spreadsheet was used to implement the computational procedure of the grey wolf optimization algorithm. In using the wolves, at the initial level, the starting point was a zero where hunting had not begun and the prey had just entered the park, which is within the territory of the grey wolves. With this in mind, real life is mimicked and such data gathered would aid precise decision-making. The results revealed the feasibility of the approach and convergence was obtained at the tenth iteration with the best fitness value at 9020785071. It is expected that the findings from this work will be useful as a method for planning in production planning and policy development for the carbon fiber-reinforced plastic industry. This study is a noteworthy contribution to the production development of CFRPs where the grey wolf algorithm is used to analyze the problem. In addition, evidence of the responses determining the quality of drilled products is provided.

Optimizing to Minimize Thrust Force in Drilling Carbon Fiber Reinforced Plastic Composites with HSS Drill Bit Using Taguchi-Pareto Particle Swarm Optimization Method Taiwo, Emmanuel Oluwatobi; Oke, Sunday Ayoola
International Journal of Industrial Engineering and Engineering Management Vol. 4 No. 1 (2022)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v4i1.5081

In this study, a robust method of Taguchi-Pareto (TP) coupled with particle swarm optimization (PSO) is proposed to minimize the thrust force in the drilling of carbon fiber reinforced plastic composites. Taguchi-Pareto is used against Taguchi (T) to emphasize the prioritization scheme essential for deploying the resources to parameters. Besides, and differently from earlier studies, particle swarm optimization is integrated with the Taguchi-Pareto to optimize the structure further. A further result is placed in the fitness function of the PSO to cultivate the velocity and position vectors. In the TP-PSO, the Pareto scheme is introduced to prioritize the factors based on the 80-20-rule. The Taguchi method yielded a feasible optimal parametric setting. The TPSO and TPPSO attained minimum thrust force in four and seven iterations, respectively. Furthermore, the PSO, TPPSO, and TPSO hold the first, second, and third positions, respectively. Results suggest that the proposed robust TPPSO offers an important indicator of optimization of the thrust force while drilling carbon fiber reinforced plastic composites using existing datasets. The usefulness of this effort is to help drilling operators and process engineers undertake energy-saving decisions.

Optimizing to Minimize Thrust Force in Drilling Carbon Fiber Reinforced Plastic Composites with HSS Drill Bit Using Taguchi-Pareto Particle Swarm Optimization Method Taiwo, Emmanuel Oluwatobi; Oke, Sunday Ayoola
International Journal of Industrial Engineering and Engineering Management Vol. 4 No. 1 (2022)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v4i1.5081

In this study, a robust method of Taguchi-Pareto (TP) coupled with particle swarm optimization (PSO) is proposed to minimize the thrust force in the drilling of carbon fiber reinforced plastic composites. Taguchi-Pareto is used against Taguchi (T) to emphasize the prioritization scheme essential for deploying the resources to parameters. Besides, and differently from earlier studies, particle swarm optimization is integrated with the Taguchi-Pareto to optimize the structure further. A further result is placed in the fitness function of the PSO to cultivate the velocity and position vectors. In the TP-PSO, the Pareto scheme is introduced to prioritize the factors based on the 80-20-rule. The Taguchi method yielded a feasible optimal parametric setting. The TPSO and TPPSO attained minimum thrust force in four and seven iterations, respectively. Furthermore, the PSO, TPPSO, and TPSO hold the first, second, and third positions, respectively. Results suggest that the proposed robust TPPSO offers an important indicator of optimization of the thrust force while drilling carbon fiber reinforced plastic composites using existing datasets. The usefulness of this effort is to help drilling operators and process engineers undertake energy-saving decisions.

Optimizing the Parameters of Carbon Fiber Reinforced Plastic Composite Drilling Process Using Signal-to-noise Ratio-based Grey Wolf Optimization Algorithm Taiwo, Emmanuel Oluwatobi; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.7691

This study aims to develop an optimization scheme that contributes to the production of carbon fiber-reinforced plastics using the grey wolf optimization approach. Different from other optimization schemes such as the Taguchi method, which takes some time to compute and use, this grey wolf optimization approach introduced a fast convergence scheme to reduce computation time thereby making its implementation in the factory very interesting. Data used for the analysis was obtained from a doctoral thesis via an experimental approach. Four responses were considered in this work, namely the torque, delamination at entry and exit, eccentricity and thrust force. A spreadsheet was used to implement the computational procedure of the grey wolf optimization algorithm. In using the wolves, at the initial level, the starting point was a zero where hunting had not begun and the prey had just entered the park, which is within the territory of the grey wolves. With this in mind, real life is mimicked and such data gathered would aid precise decision-making. The results revealed the feasibility of the approach and convergence was obtained at the tenth iteration with the best fitness value at 9020785071. It is expected that the findings from this work will be useful as a method for planning in production planning and policy development for the carbon fiber-reinforced plastic industry. This study is a noteworthy contribution to the production development of CFRPs where the grey wolf algorithm is used to analyze the problem. In addition, evidence of the responses determining the quality of drilled products is provided.

Title

Found 4 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 4 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search