Analisis Perbandingan Pengaruh Penggunaan Vibration Compensation Sensor pada Kualitas Cetak dalam Proses Additive Manufacturing

Khairuddin, Muhammad Luqman (2025) Analisis Perbandingan Pengaruh Penggunaan Vibration Compensation Sensor pada Kualitas Cetak dalam Proses Additive Manufacturing. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Additive Manufacturing (AM), khususnya metode Fused Deposition Modeling (FDM), banyak digunakan karena efisiensinya dalam mencetak objek kompleks. Namun, proses ini rentan terhadap getaran yang dapat menurunkan kualitas cetak, terutama pada aspek kekasaran permukaan (surface roughness). Untuk mengurangi dampak tersebut, dikembangkan Vibration Compensation Sensor (VCS) yang diintegrasikan pada mesin cetak untuk menstabilkan proses pencetakan. Penelitian ini bertujuan menganalisis pengaruh penggunaan VCS terhadap surface roughness dan hardness, serta mengevaluasi akurasi prediksi menggunakan model machine learning. Pengujian dilakukan dengan 3D printer Creality Ender-3 V3 KE menggunakan material PLA+ dan variasi parameter layer height (0,15 – 0,25 mm), print speed (85 – 110 mm/s), dan suhu ekstruder (210 °C). Hasil menunjukkan bahwa VCS tidak berpengaruh signifikan terhadap surface roughness (p > 0,05), namun mampu memperbaiki kerapian lapisan secara visual. Nilai rata-rata kekasaran terbaik terdapat pada layer height 0,15 mm yaitu sebesar 5,98 μm. Sebaliknya, pengaruh VCS terhadap hardness signifikan (p < 0,05), dengan rata-rata meningkat dari 69,20 HD menjadi 72,40 HD. Dalam aspek prediktif, integrasi data sensor ke model regresi polinomial meningkatkan akurasi prediksi. Error prediksi surface roughness turun dari 0,24% menjadi 0,16%, dan hardness dari 0,87% menjadi 0,59%. Dengan demikian, penggunaan VCS tidak hanya berdampak pada kualitas fisik hasil cetak, tetapi juga meningkatkan kinerja model prediksi pembelajaran mesin.
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Additive Manufacturing (AM), particularly the Fused Deposition Modeling (FDM) method, is widely used for its efficiency in printing complex objects. However, the process is prone to vibrations that can negatively affect print quality, especially in terms of surface roughness. To mitigate these effects, a Vibration Compensation Sensor (VCS) was developed and integrated into the printer to stabilize the printing process. This study aims to analyze the effect of VCS on surface roughness and hardness, as well as to evaluate the prediction accuracy using a machine learning model. Experiments were conducted using a Creality Ender-3 V3 KE 3D printer with PLA+ material and variations in layer height (0.15–0.25 mm), print speed (85–110 mm/s), and extruder temperature (210 °C). The results showed that VCS had no statistically significant effect on surface roughness (p > 0.05), but visually improved the uniformity of the printed layers. The best average surface roughness was obtained at a 0.15 mm layer height with a value of 5.98 μm. In contrast, the effect of VCS on hardness was significant (p < 0.05), increasing the average value from 69.20 HD to 72.40 HD. In the predictive aspect, integrating sensor data into a polynomial regression model improved prediction accuracy. The error rate for surface roughness prediction decreased from 0.24% to 0.16%, and for hardness, from 0.87% to 0.59%. Thus, the use of VCS not only enhances the physical quality of printed parts but also improves the performance of machine learning prediction models.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Additive Manufacturing, Fused Deposition Modeling, Vibration Compensation Sensor, Surface Roughness, Hardness, Machine Learning, Additive Manufacturing, Fused Deposition Modeling, Vibration Compensation Sensor, Surface Roughness, Hardness, Machine Learning
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
Divisions:	Faculty of Vocational > Mechanical Industrial Engineering (D4)
Depositing User:	Muhammad Luqman Khairuddin
Date Deposited:	04 Aug 2025 04:54
Last Modified:	04 Aug 2025 04:54
URI:	http://repository.its.ac.id/id/eprint/125123

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