Reducing Coating Defects by Implementing Auto Dripping System on The Finishing Machine
Abstract
Finishing Process is one of the most important processes in the manufacture of resistors, because it is the process of forming the resistor body by coating the resistor with a coating liquid. However, in this process there are still many cases of product defects, one of the main causes is the uncontrolled viscosity of the coating liquid causing the formation of body resistors is not good, or what is called Body deform and body thin defects. Auto Dripping is a useful tool for automatically dripping methanol liquid which aims to control the viscosity of the coating liquid in the finishing process. Because Auto Dripping is a tool that has just been implemented and does not yet have the right parameter settings, it is necessary to do trials to find the right parameters so that Auto Dripping functions properly. In Auto Dripping there are two settings that can be controlled, namely Stroke Speed and Stroke Length, these two settings affect the output of methanol liquid that comes out. In this study using SPC tools to analyze the parameters being tested, namely the Control Chart which is used to monitor the stability of a process and study process changes from time to time. then, in this study also used the Process Capability Analysis method. This method is very effective in viewing the performance of a process, is used to measure process capability by comparing its distribution with the distribution of specifications accurately and provides a clearer picture. After carrying out this project, the authors concluded that the implementation of the Auto Dripping set up parameter has proven to be quite influential in reducing the case of coating defects caused by uncontrolled viscosity, namely by maintaining the stability of the coating viscosity so that it can produce resistors that meet specifications. From the data above, the production of resistors has been running stably with a period of 5 weeks, with 100 resistor samples being measured. With an average value of 2.82 mm. As for the Cpk value after implementing the new parameter setup, which is 3.35, this is a high result and meets the standard, which is ≥1.5.
Downloads
References
R. S. Hutapea, S. N. Dewi, and C. M. Lasambouw, “Quality Costs in Improving the Efficiency Production Costs,” Proceedings of the International Conference on Applied Science and Technology on Social Science (ICAST-SS 2020), vol. 544, May 2021, doi: 10.2991/ASSEHR.K.210424.105.
“Fundamentals of Quality Control and Improvement, 5th Edition | Wiley.” Accessed: Dec. 22, 2023. [Online]. Available: https://www.wiley.com/en-us/Fundamentals+of+Quality+Control+and+Improvement,+5th+Edition-p-9781119692331
“Product Liability Desk Reference, A Fifty-State Compendium, 2024 Edition | Wolters Kluwer Legal & Regulatory.” Accessed: Dec. 22, 2023. [Online]. Available: https://law-store.wolterskluwer.com/s/product/product-liability-desk-referencea-fifty-state-compendium3mo-subvitallaw-3r/01t0f00000NY7avAAD
A. Goyal, R. Agrawal, and C. R. Saha, “Quality management for sustainable manufacturing: Moving from number to impact of defects,” J Clean Prod, vol. 241, p. 118348, Dec. 2019, doi: 10.1016/J.JCLEPRO.2019.118348.
V. Shah, “Handbook of Plastics Testing and Failure Analysis: Third Edition,” Handbook of Plastics Testing and Failure Analysis: Third Edition, pp. 1–634, Jun. 2006, doi: 10.1002/0470100427.
A. K. Chakraborty and M. Chatterjee, “Handbook of Multivariate Process Capability Indices,” Handbook of Multivariate Process Capability Indices, Jan. 2021, doi: 10.1201/9780429298349.
D. C. Montgomery, “Introduction to Statistical Quality Control, 8th Edition | Wiley,” Wiley Online Library, p. 786, 2019, Accessed: Dec. 22, 2023. [Online]. Available: https://www.wiley.com/en-us/Introduction+to+Statistical+Quality+Control%2C+8th+Edition-p-9781119399308
L. S. Manurung and H. Sudrajad, “DESIGN AND BUILD UP THE STIRRER VISCOMETER,” Jurnal Geliga Sains: Jurnal Pendidikan Fisika, vol. 6, no. 2, pp. 98–104, Feb. 2019, doi: 10.31258/JGS.6.2.98-104.
N. A. Golilarz et al., “A new automatic method for control chart patterns recognition based on convnet and harris hawks meta heuristic optimization algorithm,” IEEE Access, vol. 7, pp. 149398–149405, 2019, doi: 10.1109/ACCESS.2019.2945596.
G. Arcidiacono and A. Pieroni, “The revolution Lean Six Sigma 4.0,” Int J Adv Sci Eng Inf Technol, vol. 8, no. 1, pp. 141–149, 2018, doi: 10.18517/IJASEIT.8.1.4593.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Open Access Policy
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.