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Rohmat rohmat
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Journal : Jurnal Polimesin

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Intelligent multi-model system for surface roughness prediction in CNC turning of multiple materials Rohmat, Rohmat; Aryntya Firia Ferlania, Dianda; Frediansah Mukminin, Donni
Jurnal Polimesin Vol 24, No 1 (2026): February
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i1.8185

Abstract

Surface roughness is a critical indicator of machining quality that directly affects product performance and service life. However, most existing prediction studies focus on single-material machining and rely on a single predictive model, limiting their effectiveness in real industrial environments where multiple materials are commonly processed. To address this gap, this study proposes an intelligent multimodel system for surface roughness prediction in CNC turning of multiple materials. The experimental investigation was carried out using two commonly applied steels, ST41 and S45C, with 81 machining trials performed for each material. Vibration signals were recorded using a three-axis accelerometer and combined with machining parameters consisting of feed rate, spindle speed, and depth of cut. The acquired signals were analyzed in both time and frequency domains through Fourier transformation, resulting in the extraction of eighteen vibration-related features that were normalized and used as model inputs. Three prediction techniques, namely Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks, were developed and integrated within the proposed system. System performance was evaluated using Mean Absolute Percentage Error (MAPE) and statistically analyzed through one-way ANOVA and Tukey post-hoc tests. The results demonstrate that the ANN model consistently achieved the highest prediction accuracy, with MAPE values of 2.81% for S45C, 4.72% for ST41, and 4.42% for the combined-material dataset, outperforming the Regression and SVR models. These results confirm that the proposed intelligent multimodel system provides a robust, accurate, and practical solution for vibration-based surface roughness prediction in CNC turning of multiple materials.
Co-Authors A.A. Ketut Agung Cahyawan W Abdullah, Ishlahuddin Adeoye, Moses Adeleke Adi, Handaru Indrian Sasmito Agus Sutiyono Agustina Nurhayati Ahra, Muhammad Haikal Alfiyani, Lina Amang Fathurrohman, Amang Anwar, Syaeful Artwodini Muqtadiroh, Feby Aryntya Firia Ferlania, Dianda Ashtaputre, Shrirang AYU NINGSIH Choirudin, Choirudin Dadang S Anshori Dewi, Intan Karunia Djoko Prakosa Dyah Nugraheni, Prasasti Encep Kusumah Erwin Andrianto Fachry Abda El Rahman Fikriawan, Suad Firia Ferlania, Dianda Aryntya Flora Ramona Sigit Prakoeswa Frediansah Mukminin, Donni Ghiffari, Faisal Hendra Purwanto Henny Sri Astuty Hidayat Hidayat Hidayat, Ahmad Wahyu Holish, Amarru Muftie Ida Faridatul Hasanah Isah Cahyani Jamhari, Said Kholid Mawardi Kurniawan, Nurkholis Ma’arifah, Amaliya Miftahul Makhrudy, Khoirul Aman Malik, Muhammad Khoirul Mauridhi Hery Purnomo Miftahul Ulum Milda Istiqomah Mista, Hunaidah Mohamad Jamhari Muhammad Nanang Qosim Muhammad Yusuf Muhyidin, Yusuf Mulyono, Didik Muttaqin, Muhammad Rafi Noor Alis Setiyadi NORIDA CANDA SAKTI Nunung Sitaresmi Nurini Aprilianda Perdana, Muhammad Nabhan Putri, Nanda Anggelina Rara Novika Rahmadhini Rio Achmad Nugroho Rizqa Ahmadi, Rizqa Rizqi, Mohammad Fathur Rozak, Khozani Rufaidah, Anik Sair, Shrafat Ali Saputra, Rio Angga Sholeh, Idris Malikus Siti Zulaikhah Smith, Robert Brian Sri Nurhayati Sugiono, Didik Sulistyaningsih, Lilis Siti Sumaji, Muhammad Anis Susi Nurkholidah Suwarno Suwarno Syariefuddin, Iqbal Syarifudin, Iqbal Tamrin Fathoni Taufiqurrohman Taufiqurrohman Tri Rijanto Uzliawati , Lia Waspodo Tjipto Subroto Wei, David Chuah Cee Wibowo, Gita Vallenti Yahya, Layyina Elwirda Yakob, Asmirati Yayat Suharyat