Ilhamsyah, Moch Akbar (2025) Pengaruh Variasi Diameter Nozzle,Printing Speed, Dan Layer Height Terhadap Prediksi Nilai Surface Roughness Dan Hardness Menggunakan Machine Learning Pada Fdm 3d Printer Dengan Material PLA+. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Di zaman yang serba modern seperti saat ini, teknologi banyak ditemukan serta dikembangkan yang berfungsi tak lain lagi untuk membantu pekerjaan manusia. Salah satu contoh teknologi tersebut yaitu Additive Manufacturing (AM) atau biasa dipanggil pencetakan tiga dimensi. Dalam proses pencetakan tiga dimensi terdapat beberapa parameter yang mempengaruhi bentuk geometri serta sifat mekanik yang dihasilkan. Parameter proses pencetakan tiga dimensi tersebut diantaranya adalah diameter nozzle, printing speed, layer height, dll. Selain parameter yang telah disebutkan, material filamen yang digunakan sebagai filler juga memiliki peran penting pada proses pencetakan tiga dimensi. Setiap material filamen memiliki sifat mekanik serta karakterisik yang berbeda-beda. Hal tersebut memungkinkan terjadinya perbedaan fisik serta sifat mekanik untuk setiap material filamen yang berbeda.
Penelitian ini menganalisi pengaruh variasi pencetakan yaitu diameter nozzle, printing speed, dan layer height menggunakan material PLA+ terhadap hasil uji surface roughness dan hardness pada benda hasil cetak. Selama proses pencetakan, hasil digunakan untuk model machine learning (ML) untuk dapat memprediksi kualitas hasil cetak (Surface roughness dan Hardness) yang hasilnya dapat diakses menggunakan pengguna antarmuka sehingga lebih mudah digunakan.
Dari hasil penelitian diketahui bahwa setting parameter nozzle diameter memiliki pengaruh signifikan terhadap surface roughness. Pada setting variasi nozzle diameter 0,8 mm disimpulkan dapat menurunkan nilai surface roughness secara signifikan yaitu pada rerata 3,78 µm. Berdasarkan pearson correlation, parameter pencetakan nozzle diameter memiliki 0,564 koefisien korelasi linier positif dengan hardness. Lalu untuk model ML, R² Test untuk prediksi surface roughness menghasilkan nilai 0,94. Sedangkan R² Test untuk prediksi hardness menghasilkan nilai 0,71.
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In modern times like today, many technologies have been discovered and developed that function nothing but to help human work. One example of such technology is Additive Manufacturing (AM) or commonly called three-dimensional printing. In the three-dimensional printing process, there are several parameters that affect the geometric shape and mechanical properties produced. The parameters of the three-dimensional printing process include nozzle diameter, printing speed, layer height, etc. In addition to the parameters mentioned, the filament material used as filler also has an important role in the three-dimensional printing process. Each filament material has different mechanical properties and characteristics. This allows for differences in physical and mechanical properties for each different filament material.
This research analyzes the effect of printing variations, namely nozzle diameter, printing speed, and layer height using PLA + material on the results of surface roughness and hardness tests on printed objects. During the printing process, the results are used for machine learning (ML) models to be able to predict the quality of the print (Surface roughness and Hardness) whose results can be accessed using a user interface so that it is easier to use.
From the research results, it is known that setting the nozzle diameter parameter has a significant effect on surface roughness. In the setting of the nozzle diameter variation of 0.8 mm, it is concluded that it can significantly reduce the surface roughness value at an average of 3.78 µm. Based on Pearson correlation, the nozzle diameter printing parameter has 0.564 positive linear correlation coefficient with hardness. Then for the ML model, the R² Test for predicting surface roughness resulted in a value of 0.94. While the R² Test for hardness prediction resulted in a value of 0.71
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Additive Manufacturing (AM), Fused Deposition Modeling (FDM), Diameter Nozzle, Printing speed, Layer height, Surface Roughness, Hardness, Machine learning, PLA+ |
| Subjects: | Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. T Technology > TA Engineering (General). Civil engineering (General) > TA418.42 Hardness properties and tests. Hardness--Testing. T Technology > TJ Mechanical engineering and machinery > TJ561 Surface T Technology > TP Chemical technology > TP1140 Polymers T Technology > TS Manufactures > TS161 Materials management. |
| Divisions: | Faculty of Vocational > Mechanical Industrial Engineering (D4) |
| Depositing User: | Moch. Akbar Ilhamsyah |
| Date Deposited: | 04 Aug 2025 04:46 |
| Last Modified: | 04 Aug 2025 04:46 |
| URI: | http://repository.its.ac.id/id/eprint/125055 |
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