Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 electronic resource (348 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
top-coal caving mining
process parameters decision model BP neural network similaritysimulation test time-dependent cohesion traction force deep-sea sediment tracked miner rheology cemented paste backfill curing conditions mechanical properties mathematical strength model AMD phytoremediation sulfate hydroponic experiment wetland plants ecological pollution tailings dam safety factor quantitative evaluation dynamic weight comprehensivediagnosis of health rock formations surface subsidence law surface subsidence process 3D test device 3Dlaser scanning mine ventilation network wind speed sensors distribution air volume reconstruction independent cut set surface subsidence probability integration loess donga superimposed calculation additional displacement of slope mining slip mining water hazard microseismic monitoring intelligent recognition feature extraction support vector machine classification model freeze–thaw cycles tailings mechanical behavior SEM MIP thick aeolian sand shallow buried thick seam overburden failure ground damage numerical simulation rock mechanics cyclic impact chemical corrosion axial compression strength degradation pipe transportation system test pressure loss random forest algorithm filling-aided design vibration signals neural network drilling state identification algorithm drilling depth monitoring-while-drilling method |
| ISBN | 3-0365-4688-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469303321 |
Dong Longjun
|
||
| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
|
Prates Pedro
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||