Prediction Model and Experimental Verification of Surface Roughness of Single Crystal Diamond Chemical Mechanical Polishing Based on Archimedes Optimization Algorithm
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| Title: | Prediction Model and Experimental Verification of Surface Roughness of Single Crystal Diamond Chemical Mechanical Polishing Based on Archimedes Optimization Algorithm |
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| Authors: | Zhaoze Li, Xiaoguang Guo, Guanghui Fan, Yueming Deng, Renke Kang, Xuefei Wang |
| Source: | Micromachines, Vol 16, Iss 10, p 1121 (2025) |
| Publisher Information: | MDPI AG, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Mechanical engineering and machinery |
| Subject Terms: | single crystal diamond, chemical mechanical polishing, Archimedes optimization algorithm, roughness prediction model, Mechanical engineering and machinery, TJ1-1570 |
| Description: | Chemical mechanical polishing (CMP) is a critical technique for fabricating ultra-smooth and high-quality surfaces of single crystal diamond (SCD), where processing parameters profoundly influence polishing performance. To achieve superior diamond surface finishes, this study first investigates the effects of key process parameters, including oxidant concentration, catalyst type, and abrasive particle size, on surface quality through single-factor experiments. Subsequently, an Archimedes optimization algorithm (AOA)-based prediction model for diamond CMP surface roughness (Sa) is developed and validated experimentally. Results reveal that high-concentration oxidants, fine-particle abrasives, and dual-catalyst polishing systems synergistically enhance surface quality. The AOA-based prediction model demonstrates a root-mean-square error (RMSE) of 0.006 and a correlation coefficient (R) of 0.98 between the predicted and experimental Sa values. Under the conditions of a dual-catalyst type, 35% oxidant concentration, and 500 nm abrasive particle size, the model predicts a surface roughness of 0.128 nm, with an experimental value of 0.125 nm and a relative error of less than 3%. These findings highlight the capability of the model to accurately forecast surface roughness across diverse process parameters, offering a novel predictive framework for precision CMP of SCD. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2072-666X |
| Relation: | https://www.mdpi.com/2072-666X/16/10/1121; https://doaj.org/toc/2072-666X |
| DOI: | 10.3390/mi16101121 |
| Access URL: | https://doaj.org/article/e5e9f1de331e4dd4b49a62027565b3ba |
| Accession Number: | edsdoj.5e9f1de331e4dd4b49a62027565b3ba |
| Database: | Directory of Open Access Journals |
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