REAL-TIME STRUCTURAL ANALYSIS BASED ON MACHINE LEARNING FOR CUSTOM PRODUCT DESIGN: A CASE STUDY OF ORTHOPEDIC FIXATOR PRODUCT
-
Aji Digdoyo(1)
Departement Mechanical Engineering, Jayabaya University
-
Adhitio Satyo Bayangkari Karno(2)
Department of Information System, Faculty of Engineering, Gunadarma University
-
Widi Hastomo(3*)
ITB Ahmad Dahlan Jakarta
-
Agita Tunjungsari(4)
Department of Psychology; Faculty of Psychology Science, Gunadarma University
-
Nada Kamilia(5)
Department of Information System, STMIK Jakarta
-
Indra Sari Kusuma Wardhana(6)
Department of Information System, STMIK Jakarta
-
Nia Yuningsih(7)
Department of Information System, Faculty of Engineering, Gunadarma University
(*) Corresponding Author
Abstrak
ABSTRAK
Kustomisasi massal berkaitan dengan peningkatan keseimbangan antara kebutuhan perusahaan yang difokuskan pada pelanggan pada kondisi fleksibilitas dan efisiensi produksi. Penyesuaian produk yang sesuai kebutuhan pelanggan dapat meningkatkan daya saing perusahaan. Namun, proses dan penyesuaian produksi yang khusus memerlukan waktu lebih dan biaya yang tidak efisien. Pemodelan produk parametrik merupakan teknik yang cukup populer untuk menangani masalah ini. Namun, masih memiliki tantangan terkait biaya yang tinggi pada harga perangkat lunak serta tenaga kerja yang mempunyai keahlian khusus pada bidang kualitas kontrol. Selain itu, desain khusus produk tidak dapat diuji secara cepat, sehingga berdampak pada waktu produksi yang cukup lama. Penelitian ini mengusulkan metode Machine Learning (ML) yang bertujuan agar mendapatkan struktur waktu yang cepat untuk menganalisis hasil produksi fixator ortopedi. Proses penelitian ini membutuhkan kumpulan data latih dengan atribut produk, karakteristik fisik, kualitas, teknik ML yang dipilih, dan penentuan set hyperparameter yang tepat. Hasil optimasi diperoleh dengan menggunakan metode gradient boosting dengan nilai . Dengan hasil tersebut, perangkat fixator ortopedi dapat digunakan dalam studi kasus pengembangan model pembelajaran mesin ini.
##plugins.generic.usageStats.downloads##
Referensi
A. Salonika and H. Marko, “New Industrial Sustainable Growth: 3D and 4D Printing,” in Trends and Opportunities of Rapid Prototyping Technologies, R. Păcurar, Ed. Rijeka: IntechOpen, 2022. doi: 10.5772/intechopen.104728.
B. Carnero, C. Bao-Varela, A. I. Gómez-Varela, and M. T. Flores-Arias, “Internal Microchannel Manufacturing Using Stereolithographic 3D Printing,” R. Păcurar, Ed. Rijeka: IntechOpen, 2022, p. Ch. 5. doi: 10.5772/intechopen.102751.
R. Bernardini, Numerical Problem Encryption for High-Performance Computing Applications. In Modern Cryptography-Current Challenges and Solutions. 2019.
G. Baranauskas, “Digitalization Impact on Transformations of Mass Customization Concept: Conceptual Modelling of Online Customization Frameworks.,” 2020.
S. Niehoff, M. Matthess, C. Zwar, S. Kunkel, T. Guan, L. Chen, B. Xue, D. Grudzien, E. P. de Lima, G. Beier., “Sustainability related impacts of digitalisation on cooperation in global value chains: An exploratory study comparing companies in China, Brazil and Germany,” J. Clean. Prod., vol. 379, p. 134606, 2022, doi: 10.1016/j.jclepro.2022.134606.
A. Szymonik, Logistics and Supply Chain Management. 2012.
C. Alexandru, “Virtual Prototyping Platform for Designing Mechanical and Mechatronic Systems. In Product Design,” IntechOpen, 2020.
Z. Wei and J. Du, “Heat and Mass Transfer of Additive Manufacturing Processes for Metals,” A. Iranzo, Ed. Rijeka: IntechOpen, 2019, p. Ch. 10. doi: 10.5772/intechopen.84889.
F. McCann, G. Ridolfi, E. Karjadi, H. Demmink, and H. Boyd, “Numerical Analysis of Hot Polymer-Coated Steel Pipeline Joints in Bending,” in Finite Element Method, R. Păcurar, Ed. Rijeka: IntechOpen, 2018. doi: 10.5772/intechopen.72262.
E. Günay, “Micromechanical Analysis of Polymer Fiber Composites under Tensile Loading by Finite Element Method,” in Perusal of the Finite Element Method, R. Petrova, Ed. Rijeka: IntechOpen, 2016. doi: 10.5772/65002.
I. A. Khan and S. M. Hashemi, “On Finite Element Vibration Analysis of Carbon Nanotubes,” R. Petrova, Ed. Rijeka: IntechOpen, 2016, p. Ch. 3. doi: 10.5772/65358.
N. Nathani and G. C. Manna, “A Quality of Service Based Model for Supporting Mobile Secondary Users in Cognitive Radio Technology,” S. S. Moghaddam, Ed. Rijeka: IntechOpen, 2018, p. Ch. 2. doi: 10.5772/intechopen.80072.
J. M. Pereira, O. A. Postolache, and P. S. Girao, “Analog to Digital Conversion Methods for Smart Sensing Systems,” M. K. Sharma, Ed. Rijeka: IntechOpen, 2010, p. Ch. 24. doi: 10.5772/8727.
W. Hastomo, A. S. Bayangkari Karno, N. Kalbuana, A. Meiriki, and Sutarno, “Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia,” J. Phys. Conf. Ser., vol. 1933, no. 1, p. 012050, 2021, doi: 10.1088/1742-6596/1933/1/012050.
W. Hastomo, N. Aini, A. Satyo, B. Karno, and L. M. R. Rere, “Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management,” vol. 11, no. 2, pp. 131–139, 2022, doi: https://doi.org/10.22146/jnteti.v11i2.2586.
N. Korunovi and M. Zdravković, “Real-time structural analysis assistance in customized product design,” ICIST 2019 Proceedings, 1, vol. 1, pp. 217–220, 2019. [Online]. Available at: www.eventiotic.com/eventiotic/files/Papers/URL/1e5f3a5e-0d0d-4077-b56a-d53ff742b595.pdf
M. Zdravković and N. Korunović, “Novel methodology for real-time structural analysis assistance in custom product design,” Mech. Eng., 2021, doi: 10.22190/FUME200828008Z.
J. Zhou, H. Lin, H. Jin, S. Li, Z. Yan, and S. Huang, “Cooperative prediction method of gas emission from mining face based on feature selection and machine learning,” Int. J. Coal Sci. Technol., vol. 9, no. 1, p. 51, 2022, doi: 10.1007/s40789-022-00519-8.
W. Hastomo, A. S. Karno, S. Sutarno, D. Arif, & Moreta, E., and S. Sudjiran, “Mengatasi Ketimpangan Data Deep Neural Network dengan Pelipatan Fitur Data Klasifikasi Spektroskopi Darah,” J. Ilm., vol. 8, no. 2, pp. 579–591, 2022, doi: 10.35326/pencerah.v8i2.2251.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
B. Anandan and M. Manikandan, “Machine learning approach with various regression models for predicting the ultimate tensile strength of the friction stir welded AA 2050-T8 joints by the K-Fold cross-validation method,” Mater. Today Commun., vol. 34, p. 105286, 2023, doi: 10.1016/j.mtcomm.2022.105286.
X. Zhang and C.-A. Liu, “Model averaging prediction by K-fold cross-validation,” J. Econom., 2022, doi: 10.1016/j.jeconom.2022.04.007.
H. L. Vu, K. T. W. Ng, A. Richter, and C. An, “Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation,” J. Environ. Manage., vol. 311, p. 114869, 2022, doi: 10.1016/j.jenvman.2022.114869.
P. J. Bugryniec, A. Yeardley, A. Jain, N. Price, S. Vernuccio, and S. F. Brown, “Gaussian-Process based inference of electrolyte decomposition reaction networks in Li-ion battery failure,” in 32 European Symposium on Computer Aided Process Engineering, vol. 51, L. Montastruc and S. B. T.-C. A. C. E. Negny, Eds. Elsevier, 2022, pp. 157–162. doi: 10.1016/B978-0-323-95879-0.50027-8.
##submission.copyrightStatement##
##submission.license.cc.by4.footer##Penulis yang mengirimkan naskah harus memahami dan menyetujui bahwa jika diterima untuk diterbitkan, penulis memiliki hak cipta dan memberikan jurnal hak publikasi pertama dengan karya yang dilisensikan secara bersamaan di bawah Creative Commons Attribution (CC-BY) 4.0 License yang memungkinkan orang lain untuk berbagi karya dengan pengakuan kepenulisan karya dan publikasi awal dalam jurnal ini.
