{"id":12954,"date":"2026-10-09T14:31:44","date_gmt":"2026-10-09T06:31:44","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12954"},"modified":"2026-10-17T05:16:05","modified_gmt":"2026-10-16T21:16:05","slug":"jase-202701-36-014","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-014","title":{"rendered":"CGAN-KSR: Surface Roughness Prediction in CNC Milling from Small Samples"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Thanh-Nhan Nguyen1<\/sup>, Dinh-Khang Bach1<\/sup>, Thai-Viet Dang2<\/sup>, and Anh-Dung Nguyen1<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Department of Mechanical Engineering, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Vietnam<\/p>\n\n\n\n

2<\/sup>Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Vietnam<\/p>\n<\/div>\n\n\n\n

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Received: June 19, 2026
Accepted: August 25, 2026
Publication Date: October 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Architecture and training workflow of the CGAN model for surface roughness prediction.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.014<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Surface roughness (Ra<\/sub>) prediction in CNC milling is impeded by small experimental datasets that cover the cutting-parameter space sparsely and unevenly. This paper proposes a hybrid CGAN-KSR framework integrating conditional data augmentation with nonparametric smoothing under a leakage-free evaluation protocol. A Conditional GAN (CGAN) is trained on eight CNC milling experiments on Y8 carbon tool steel to learn the conditional distribution of Ra<\/sub> given the cutting parameters. Within each cross-validation fold the trained CGAN generates 20 condition-consistent synthetic samples at unobserved grid conditions, and a Kernel Smoothing Regression (KSR) model is fitted to the 27 combined samples. KSR is adopted not for predictive superiority but because it delivers a continuous, smooth response surface from which a process window can be read directly. Under Leave-One-Out Cross-Validation, augmentation reduces MSE from 0.363 to 0.142 (60.8%) and RMSE from 0.603 to 0.377 \u03bcm (37.4%) relative to a KSR baseline trained solely on experimental data. A percentile bootstrap 95% confidence interval for the paired MSE difference, [0.023, 0.443], is consistent with a directional improvement rather than a strongly powered or definitive statistical result, and the sign test does not reach significance at n = 8 (6 of 8 folds, P = 0.145). Ablation experiments show the gain is specific to conditional generation rather than sample volume, and is reproduced by augmented SVR and Gaussian Process models. Crucially, augmentation degrades uncertainty calibration: the 95% predictive intervals of an augmented Gaussian Process cover only 25% of held-out cases. The study therefore demonstrates feasibility, under the single material, tool and parameter range investigated, rather than general applicability.<\/p>\n\n\n\n

Keywords: Surface roughness prediction; CNC milling; Conditional GAN (CGAN); Data augmentation; Kernel smoothing regression; Small-sample learning<\/em><\/p>\n\n\n\n

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