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Mining Safety and Sustainability I
| Mining Safety and Sustainability I |
| Autore | Dong Longjun |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (348 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
mining water hazard rock formations 3D test device 3Dlaser scanning additional displacement of slope mining slip air volume reconstruction AMD axial compression BP neural network cemented paste backfill chemical corrosion classification model comprehensivediagnosis of health curing conditions cyclic impact decision model deep-sea sediment drilling depth drilling state identification algorithm dynamic weight ecological pollution feature extraction filling-aided design freeze-thaw cycles ground damage hydroponic experiment independent cut set intelligent recognition loess donga mathematical strength model mechanical behavior mechanical properties microseismic monitoring mine ventilation network MIP monitoring-while-drilling method neural network numerical simulation overburden failure phytoremediation pipe transportation system test pressure loss probability integration process parameters quantitative evaluation random forest algorithm rheology rock mechanics safety factor SEM shallow buried thick seam similaritysimulation test strength degradation sulfate superimposed calculation support vector machine surface subsidence surface subsidence law surface subsidence process tailings tailings dam thick aeolian sand time-dependent cohesion top-coal caving mining tracked miner traction force vibration signals wetland plants wind speed sensors distribution |
| ISBN | 3-0365-4688-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469303321 |
Dong Longjun
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Recent Advances and Applications of Machine Learning in Metal Forming Processes
| Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| Autore | Prates Pedro |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (210 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Mining technology & engineering |
| Soggetto non controllato |
sheet metal forming
uncertainty analysis metamodeling machine learning hot rolling strip edge defects intelligent recognition convolutional neural networks deep-drawing kriging metamodeling multi-objective optimization FE (Finite Element) AutoForm robust analysis defect prediction mechanical properties prediction high-dimensional data feature selection maximum information coefficient complex network clustering ring rolling process energy estimation metal forming thermo-mechanical FEM analysis artificial neural network aluminum alloy mechanical property UTS topological optimization artificial neural networks (ANN) machine learning (ML) press-brake bending air-bending three-point bending test sheet metal buckling instability oil canning artificial intelligence convolution neural network hot rolled strip steel defect classification generative adversarial network attention mechanism deep learning mechanical constitutive model finite element analysis plasticity parameter identification full-field measurements |
| ISBN | 3-0365-5772-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637782503321 |
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Prates Pedro
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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