Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.
<p>Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852022208547782656 |
|---|---|
| author | Fatma Alamri (20855088) |
| author2 | Imad Barsoum (17178454) Shrinivas Bojanampati (20855091) Maher Maalouf (6318215) |
| author2_role | author author author |
| author_facet | Fatma Alamri (20855088) Imad Barsoum (17178454) Shrinivas Bojanampati (20855091) Maher Maalouf (6318215) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fatma Alamri (20855088) Imad Barsoum (17178454) Shrinivas Bojanampati (20855091) Maher Maalouf (6318215) |
| dc.date.none.fl_str_mv | 2025-03-10T17:43:53Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0316600.t006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Optimal_tuning_parameters_and_accuracy_of_the_relative_density_surface_roughness_and_hardness_models_/28568991 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified xlink "> despite mean squared error lt ;&# 8201 gt ;&# 8201 feature importance analysis alsi10mg samples produced 10 &# 181 support vector regression kernel ridge regression additive manufactured samples g ., porosity data including porosity open additive manufacturing scan speed region predicting relative density optimal laser power predict part quality additive manufacturing scan speed relative density laser power lasso regression accurately predict widespread adoption various industries study identified study aims still hindered results presented random forest process parameters poor quality layer thickness improve repeatability hatch distance evaluated based current work computational results additional measurements 120 hv |
| dc.title.none.fl_str_mv | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_567c8cd4ef3ce85de0f99ded79880a85 |
| identifier_str_mv | 10.1371/journal.pone.0316600.t006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28568991 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.Fatma Alamri (20855088)Imad Barsoum (17178454)Shrinivas Bojanampati (20855091)Maher Maalouf (6318215)SociologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedxlink "> despitemean squared errorlt ;&# 8201gt ;&# 8201feature importance analysisalsi10mg samples produced10 &# 181support vector regressionkernel ridge regressionadditive manufactured samplesg ., porositydata including porosityopen additive manufacturingscan speed regionpredicting relative densityoptimal laser powerpredict part qualityadditive manufacturingscan speedrelative densitylaser powerlasso regressionaccurately predictwidespread adoptionvarious industriesstudy identifiedstudy aimsstill hinderedresults presentedrandom forestprocess parameterspoor qualitylayer thicknessimprove repeatabilityhatch distanceevaluated basedcurrent workcomputational resultsadditional measurements120 hv<p>Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.</p>2025-03-10T17:43:53ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0316600.t006https://figshare.com/articles/dataset/Optimal_tuning_parameters_and_accuracy_of_the_relative_density_surface_roughness_and_hardness_models_/28568991CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285689912025-03-10T17:43:53Z |
| spellingShingle | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. Fatma Alamri (20855088) Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified xlink "> despite mean squared error lt ;&# 8201 gt ;&# 8201 feature importance analysis alsi10mg samples produced 10 &# 181 support vector regression kernel ridge regression additive manufactured samples g ., porosity data including porosity open additive manufacturing scan speed region predicting relative density optimal laser power predict part quality additive manufacturing scan speed relative density laser power lasso regression accurately predict widespread adoption various industries study identified study aims still hindered results presented random forest process parameters poor quality layer thickness improve repeatability hatch distance evaluated based current work computational results additional measurements 120 hv |
| status_str | publishedVersion |
| title | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| title_full | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| title_fullStr | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| title_full_unstemmed | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| title_short | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| title_sort | Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models. |
| topic | Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified xlink "> despite mean squared error lt ;&# 8201 gt ;&# 8201 feature importance analysis alsi10mg samples produced 10 &# 181 support vector regression kernel ridge regression additive manufactured samples g ., porosity data including porosity open additive manufacturing scan speed region predicting relative density optimal laser power predict part quality additive manufacturing scan speed relative density laser power lasso regression accurately predict widespread adoption various industries study identified study aims still hindered results presented random forest process parameters poor quality layer thickness improve repeatability hatch distance evaluated based current work computational results additional measurements 120 hv |