Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO)
| Autor: | M. Khoirul Effendi, Bobby Oedy Pramoedyo Soepangkat, Bambang Pramujati, Rachmadi Norcahyo |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Materials science
Computer Networks and Communications 020209 energy Thrust 02 engineering and technology Drilling process Biomaterials 0202 electrical engineering electronic engineering information engineering Torque Multi-response optimization BPNN-PSO CFRP Civil and Structural Engineering Fluid Flow and Transfer Processes Carbon fiber reinforced polymer Drill business.industry Mechanical Engineering 020208 electrical & electronic engineering Delamination Metals and Alloys Particle swarm optimization Drilling Factorial experiment Structural engineering Electronic Optical and Magnetic Materials Hardware and Architecture lcsh:TA1-2040 business lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | Engineering Science and Technology, an International Journal, Vol 23, Iss 3, Pp 700-713 (2020) |
| ISSN: | 2215-0986 |
| Popis: | An integrated approach has been applied to predict and optimize multi-performance-characteristics, being optimum thrust force (FTh), torque (M), hole entry delamination (FDen) and hole exit delamination (FDex), in the drilling process of carbon fiber reinforced polymer (CFRP). The drilling operation was performed by using a full factorial design of experiments with two different drill geometry (DG), three diverse levels of spindle speed (n), and feeding speed (Vf). The quality characteristics of FTh, M, FDen, and FDex were smaller the better. Back propagation neural network (BPNN) was first performed to model the drilling process and to predict the optimum drilling responses. Particle swarm optimization (PSO) was executed to attain the best combination of drilling parameters levels that would give optimum performance. The influences of drill geometry, speeds of spindle, and feeding speed on the responses were examined by using the response graphs. In addition, the scanning electron microscope (SEM) photos of the drilled hole are also provided to show the difference of the hole quality before and after optimization. The outcome of the confirmation experiment disclosed that the integration of BPNN and PSO managed to substantially predicted and enhanced the multi-performance characteristics accurately. |
| Databáze: | OpenAIRE |
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