Optimasi Brushing Pole Machine Untuk Meningkatkan Produktivitas Produksi Menggunakan Metode Particle Swarm Optimization (PSO)

Ardianto, Mohammad Fadillah (2024) Optimasi Brushing Pole Machine Untuk Meningkatkan Produktivitas Produksi Menggunakan Metode Particle Swarm Optimization (PSO). Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Dalam industri perakitan baterai, proses brushing pole merupakan tahapan penting yang mempengaruhi kualitas dan efisiensi produksi. Sistem manual yang digunakan selama ini memerlukan waktu yamg cukup lama yaitu rata-rata 22,9 detik dan melibatkan 2 intervensi operator yaitu, operator finishing dan operator pole terminal welding (PWT). Sehingga rentan terhadap kesalahan manusia dan inkonsistensi hasil. Oleh karena itu, diperlukan sebuah sistem yang dapat mengotomatisasi proses ini, mengurangi waktu yang dibutuhkan, dan meningkatkan produktivitas serta kualitas produk akhir. Penelitian ini menggunakan metode Particle Swarm Optimization (PSO) untuk mengembangkan dan mengoptimalkan sistem Auto Brushing Machine. PSO dipilih karena kemampuannya dalam menemukan solusi optimal dengan cepat dan efisien. Dalam penelitian ini, parameter-parameter penting yang mempengaruhi proses brushing seperti waktu brushing dan tinggi adjuster dioptimalkan untuk mendapatkan performa terbaik dari mesin brushing. Data eksperimen dikumpulkan dan dianalisis untuk membandingkan kinerja antara sistem manual dan sistem baru yang menggunakan PSO. Hasil penelitian menunjukkan bahwa implementasi PSO dalam sistem Auto Brushing Machine berhasil mengurangi total waktu proses brushing dari 22,9 detik menjadi 7,5 detik, atau penurunan sebesar 67,24%.
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In the battery assembly industry, the brushing pole process is a crucial stage that affects the quality and efficiency of production. The manual system currently in use requires a significant amount of time and involves extensive operator intervention, making it prone to human error and inconsistent results. Therefore, a system is needed that can automate this process, reduce the required time, and enhance productivity and the quality of the final product. This study employs the Particle Swarm Optimization (PSO) method to develop and optimize the Auto Brushing Machine system. PSO was chosen for its ability to quickly and efficiently find optimal solutions through the simulation of swarm behavior. In this research, critical parameters such as brushing time and adjuster height were optimized to achieve the best performance of the brushing machine. Experimental data were collected and analyzed to compare the performance between the manual system and the new system using PSO. The results indicate that the implementation of PSO in the Auto Brushing Machine system successfully reduced the total brushing process time from 22.9 second to 7.5 second, a reduction of 67.24%.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Efisiensi, Particle Swarm Optimization, Otomatisasi, Efisiensi Produksi, Sistem Brushing Otomatis, Brushing Pole, Production Efficiency, Automatic Brushing System
Subjects:	T Technology > T Technology (General) > T57.6 Operations research--Mathematics. Goal programming T Technology > T Technology (General) > T57.84 Heuristic algorithms. T Technology > T Technology (General) > T58.8 Productivity. Efficiency
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	MOHAMMAD FADILLAH ARDIANTO
Date Deposited:	02 Sep 2024 01:45
Last Modified:	02 Sep 2024 01:45
URI:	http://repository.its.ac.id/id/eprint/115578

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