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Redaksi International Journal of Mechanical Engineering Technologies and Applications (MECHTA), Jurusan Teknik Mesin Fakultas Teknik, Universitas Brawijaya Jl. MT. Haryono 167 Malang, Jawa Timur Indonesia 65145
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International Journal of Mechanical Engineering Technologies and Applications (MECHTA)
Published by Universitas Brawijaya
ISSN : -     EISSN : 27223213     DOI : https://doi.org/10.21776/ub.mechta
Core Subject : Science, Engineering,
Automotive Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering
International Journal of Mechanical Engineering Technologies and Applications (MECHTA) is published by Mechanical Engineering Department, Engineering Faculty, Brawijaya University, Malang, East Java, Indonesia. MECHTA is an open-access peer-reviewed journal that mediates the dissemination of academicians, researchers, and practitioners in mechanical engineering. MECHTA accepts submissions from all over the world, especially from Indonesia. MECHTA aims to provide a forum for international academicians, researchers, and practitioners on mechanical engineering to publish the original articles. All accepted articles will be published and will be freely available to all readers with worldwide visibility and coverage. The scope of MECHTA is specific topics issues in mechanical engineering such as design, energy conversion, manufacture, and metallurgy. All articles submitted to this journal can be written in the English Language.
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 22 Documents
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Search results for , issue "Vol. 5 No. 1 (2024)" : 22 Documents clear
INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061 Ihsan, Muhammad Alif; Sumantri, Yeni; Irawan, Yudy Surya
International Journal of Mechanical Engineering Technologies and Applications Vol. 5 No. 1 (2024)
Publisher : Mechanical Engineering Department, Engineering Faculty, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/MECHTA.2024.005.01.10

Abstract

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut.  The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.
CARDIAC BIOMETRICS AND PERCEIVED WORKLOAD REGRESSION ANALYSIS USING RANDOM FOREST REGRESSOR IN COGNITIVE MANUFACTURING TASKS Harmayanti, Afifah; Tama, Ishardita Pambudi; Gapsari, Femiana; Akbar, Zuardin; Juliano, Hans
International Journal of Mechanical Engineering Technologies and Applications Vol. 5 No. 1 (2024)
Publisher : Mechanical Engineering Department, Engineering Faculty, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/MECHTA.2024.005.01.11

Abstract

Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker’s workload in optimal condition. Most common tool of cognitive workload tools are perceived measurement, like NASA – TLX questionnaire. Despite of its sensitivity to capture workload felt by the workers, this subjective measurement was prone to bias. Objective measurement utilizing biometrics data of the human body during working state was useful to eliminate bias. Cardiac biometrics were one of the many that were closely related to mental activity changes. The objective of this study was to understand the relationship of cardiac biometrics to perceived workload as an indicator of cognitive workload analysis. The study utilized four biometrics, heart rate, HRV low frequency power, total frequency power and ratio of low and high frequency power, were used to analyzed a one hour long cognitive based study case. The study case was designed in a manufacturing planning context referring to manufacturing aptitude tests, to induce cognition process on 30 participants. The biometrics and NASA – TLX score result of all the participants, were then calculated as effect size standardization before input into random forest regressor model to analyze relationship between cardiac biometrics and perceived workload. The result found a moderate relationship between the two (r2 = 0.576). Features importance also showed the most impactful feature to the model is the effect size of frequency power ratio. However, it is recommended to always consider evaluating multiple cardiac biometrics in workload analysis to ensure good model performance.

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