FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP
In order to analyze and predict the fatigue life of the shearer’s cutting planet carrier,the shearer’s rigid-flexible coupling model was established,the planet carrier’s dynamic characteristics was studied,and the planet carrier’s maximum stress value was obtained as the input. The planet carrier’s...
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Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.04.030 |
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| _version_ | 1841535842858827776 |
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| author | ZHAO LiJuan ZHANG Bo ZHANG Wen |
| author_facet | ZHAO LiJuan ZHANG Bo ZHANG Wen |
| author_sort | ZHAO LiJuan |
| collection | DOAJ |
| description | In order to analyze and predict the fatigue life of the shearer’s cutting planet carrier,the shearer’s rigid-flexible coupling model was established,the planet carrier’s dynamic characteristics was studied,and the planet carrier’s maximum stress value was obtained as the input. The planet carrier’s fatigue life was obtained by using n Soft,and the planet carrier’s fatigue life,under different working conditions was predicted by using BP,PSO-BP and improved PSO-BP. The results show that under the condition of solidity coefficient of 8. 4,depth of cut-off 600 mm,rotational speed of 90 r/min and traction speed of 2. 5 m/min,the maximum stress is 554. 78 MPa,the planet carrier’s stress concentration area is at the spline receding groove,and the fatigue life is 5. 507 2×106. Among the three neural network models,BP,PSO-BP and the improved PSO-BP,the predicted fatigue life’s maximum relative error is 4. 35%,2. 52% and 0. 90%,and the iteration ’s number is 23,8 and 6 times,respectively. The improved PSO-BPNN model improves the prediction accuracy and also improves the iterative speed. Combining MATLAB、ANSYS、ADAMS、n Soft and improved PSO-BPNN,this paper provides a method for predicting the fatigue life of key parts of industrial and mining equipment. |
| format | Article |
| id | doaj-art-2e11e05fbad24854987b1517b4127aa9 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-2e11e05fbad24854987b1517b4127aa92025-01-15T02:25:52ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-014397798130611608FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BPZHAO LiJuanZHANG BoZHANG WenIn order to analyze and predict the fatigue life of the shearer’s cutting planet carrier,the shearer’s rigid-flexible coupling model was established,the planet carrier’s dynamic characteristics was studied,and the planet carrier’s maximum stress value was obtained as the input. The planet carrier’s fatigue life was obtained by using n Soft,and the planet carrier’s fatigue life,under different working conditions was predicted by using BP,PSO-BP and improved PSO-BP. The results show that under the condition of solidity coefficient of 8. 4,depth of cut-off 600 mm,rotational speed of 90 r/min and traction speed of 2. 5 m/min,the maximum stress is 554. 78 MPa,the planet carrier’s stress concentration area is at the spline receding groove,and the fatigue life is 5. 507 2×106. Among the three neural network models,BP,PSO-BP and the improved PSO-BP,the predicted fatigue life’s maximum relative error is 4. 35%,2. 52% and 0. 90%,and the iteration ’s number is 23,8 and 6 times,respectively. The improved PSO-BPNN model improves the prediction accuracy and also improves the iterative speed. Combining MATLAB、ANSYS、ADAMS、n Soft and improved PSO-BPNN,this paper provides a method for predicting the fatigue life of key parts of industrial and mining equipment.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.04.030ShearerPlanet carrierFatigue lifeAdaptive variationParticle swarm optimization algorithmBP neural network |
| spellingShingle | ZHAO LiJuan ZHANG Bo ZHANG Wen FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP Jixie qiangdu Shearer Planet carrier Fatigue life Adaptive variation Particle swarm optimization algorithm BP neural network |
| title | FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP |
| title_full | FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP |
| title_fullStr | FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP |
| title_full_unstemmed | FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP |
| title_short | FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP |
| title_sort | fatigue life analysis and prediction of planet carrier in cutting part of shearer based on improved pso bp |
| topic | Shearer Planet carrier Fatigue life Adaptive variation Particle swarm optimization algorithm BP neural network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.04.030 |
| work_keys_str_mv | AT zhaolijuan fatiguelifeanalysisandpredictionofplanetcarrierincuttingpartofshearerbasedonimprovedpsobp AT zhangbo fatiguelifeanalysisandpredictionofplanetcarrierincuttingpartofshearerbasedonimprovedpsobp AT zhangwen fatiguelifeanalysisandpredictionofplanetcarrierincuttingpartofshearerbasedonimprovedpsobp |