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All Journal Jurnal Teknik Mesin IPTEK The Journal of Engineering JMES The International Journal of Mechanical Engineering and Sciences Sewagati
Mohammad Khoirul Effendi
Institut Teknologi Sepuluh Nopember
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer Kirana Alif Fatika; Mohammad Khoirul Effendi
JMES: The International Journal of Mechanical Engineering and Sciences Vol 5 No 2 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.10382

Abstract

This experiment focused on the drilling process of Glass Fiber Reinforced Polymer (GFRP) composites. The data was obtained from an experiment carried out by Production Engineering Laboratory, Mechanical Engineering Department, Faculty of Industrial Technology and Systems Engineering, Institut Sepuluh Nopember Surabaya in 2019. The experiment was done with an artificial intelligence method called Backpropagation Neural Network (BPNN) as an approach to predict the response parameters (thrust force, torque, hole roundness, and hole surface roughness). The parameter inputs are drill point geometry, drill point angle, feed rate, and spindle speed. Hence the prediction would be used to gain the minimum input parameters by applying metaheuristic methods called Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO). Then the result from both methods was compared to determine which method gained the better optimization values. Since BPNN-DE and BPNN-TLBO with type X drill point geometry was considerably better than type S drill point geometry, type X drill point geometry could be used to optimize the drilling process of GFRP.
The Comparisson between LBP and SQI Methods in the Surface Roughness Measurement Using ESPI Method Mohammad Khoirul Effendi; Agus Sigit Pramono; Pandu Pratama; Rifki Wardana
IPTEK The Journal of Engineering Vol. 2 No. 1 (2015)
Publisher : Institut Teknologi Sepuluh Nopember

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Abstract

Surface roughness measurement using direct contact methods raises several issues, for examples stylus wear and size limitation problems. Furthermore non-contact methods are proposed to solve these problems. One of them is Electronic Speckle Pattern Interferometry (ESPI), which use Helium-Neon (He - Ne) as laser light source. A speckle pattern is produced by scattered light on the surface of the measuring object due to the interference of laser beams, and it will be captured by Charge Coupled Device (CCD) camera. Afterwards Linear Binary Pattern (LBP) and Self Quotient Image (SQI) methods are used to reduce illumination effect in the captured image. The average gray-level from the previous process will be converted into a surface roughness value by gray level to surface roughness conversion formulation. It is obtained from correlation value between gray level and a set of standard roughness. The standard roughness value range is start from 0.05 micrometer to 12.5 micrometer. It is measured from five different final machining process, which are flat lapping, grinding, horizontal milling, and vertical milling. As verification, the results of ESPI method will be compared with the result of direct contact tools using Mitutoyo Surftest 301 and 401.
Co-Authors Agus Sigit Pramono Aida Annisa Amin Daman Ari Surya Yulianto Arif Wahjudi Arif Wahjudi Bambang Pramujati Bambang Sudarmanta Bara Atmaja Bara Atmaja Dedi Budi Purwanto Dinny Harnany Djatmiko Ichsani Fungky Dyan Pratiwi Hanif Pribadi Herman Sasongko Kirana Alif Fatika Kirana Alif Fatika Pandu Pratama Rhema Adi Magiza Wicaksana Rhema Adi Magiza Wicaksana Rifki Wardana Ryan Filbert Wijaya Sampurno Sampurno Sampurno Suhardjono Suhardjono Suhardjono Suhardjono Triyogi Yuwono Yusuf Kaelani