Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding
Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials, including single-crystal silicon, silicon carbide, and gallium arsenide. Surface roughness and subsurface damage depth (SDD) are crucial indicators for evaluating the surface qu...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | International Journal of Extreme Manufacturing |
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| Online Access: | https://doi.org/10.1088/2631-7990/adae67 |
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| _version_ | 1832086548432027648 |
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| author | Shang Gao Haoxiang Wang Han Huang Zhigang Dong Renke Kang |
| author_facet | Shang Gao Haoxiang Wang Han Huang Zhigang Dong Renke Kang |
| author_sort | Shang Gao |
| collection | DOAJ |
| description | Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials, including single-crystal silicon, silicon carbide, and gallium arsenide. Surface roughness and subsurface damage depth (SDD) are crucial indicators for evaluating the surface quality of these materials after grinding. Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions. This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials. The surface roughness model uniquely incorporates the material’s elastic recovery properties, revealing the significant impact of these properties on prediction accuracy. The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece, as well as the mechanisms governing stress-induced damage evolution. The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut (GDC). Additionally, we have developed an analytical relationship between the GDC and grinding process parameters. This, in turn, enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters, which cannot be achieved by previous models. The models were validated through systematic experiments on three different semiconductor materials, demonstrating excellent agreement with experimental data, with prediction errors of 6.3% for surface roughness and 6.9% for SDD. Additionally, this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials. These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials. |
| format | Article |
| id | doaj-art-ee2a93a9c3bc4c10a6555d1a1b6b925c |
| institution | Kabale University |
| issn | 2631-7990 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | International Journal of Extreme Manufacturing |
| spelling | doaj-art-ee2a93a9c3bc4c10a6555d1a1b6b925c2025-02-06T13:32:05ZengIOP PublishingInternational Journal of Extreme Manufacturing2631-79902025-01-017303510310.1088/2631-7990/adae67Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grindingShang Gao0https://orcid.org/0000-0001-7447-3140Haoxiang Wang1Han Huang2Zhigang Dong3Renke Kang4State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology , Dalian 116024, People’s Republic of ChinaState Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology , Dalian 116024, People’s Republic of ChinaSchool of Advanced Manufacturing, Sun Yat-sen University , Shenzhen, People’s Republic of ChinaState Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology , Dalian 116024, People’s Republic of ChinaState Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology , Dalian 116024, People’s Republic of ChinaWorkpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials, including single-crystal silicon, silicon carbide, and gallium arsenide. Surface roughness and subsurface damage depth (SDD) are crucial indicators for evaluating the surface quality of these materials after grinding. Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions. This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials. The surface roughness model uniquely incorporates the material’s elastic recovery properties, revealing the significant impact of these properties on prediction accuracy. The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece, as well as the mechanisms governing stress-induced damage evolution. The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut (GDC). Additionally, we have developed an analytical relationship between the GDC and grinding process parameters. This, in turn, enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters, which cannot be achieved by previous models. The models were validated through systematic experiments on three different semiconductor materials, demonstrating excellent agreement with experimental data, with prediction errors of 6.3% for surface roughness and 6.9% for SDD. Additionally, this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials. These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.https://doi.org/10.1088/2631-7990/adae67surface qualitygrindingpredictive modelssemiconductor materialssurface roughnesssubsurface damage depth |
| spellingShingle | Shang Gao Haoxiang Wang Han Huang Zhigang Dong Renke Kang Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding International Journal of Extreme Manufacturing surface quality grinding predictive models semiconductor materials surface roughness subsurface damage depth |
| title | Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding |
| title_full | Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding |
| title_fullStr | Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding |
| title_full_unstemmed | Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding |
| title_short | Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding |
| title_sort | predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding |
| topic | surface quality grinding predictive models semiconductor materials surface roughness subsurface damage depth |
| url | https://doi.org/10.1088/2631-7990/adae67 |
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