Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Mean Cutting Force Prediction of Conical Picks Using Ensemble Learning Paradigm
https://orcid.org/0000-0002-1519-6304
Abstract The conical pick is the most essential tool of excavation machinery such as roadheaders, continuous miners, and shearers for breaking rock in mining and civil engineering operations. For rock cuttability, however, the geometry of conical picks and mechanical parameters of rocks are the most important factors. This study aims to construct an optimized data-driven predictive model to establish a quantitative correlation between strength of rock, geometry of tool, and cutting action data with the mean cutting force (CF). For this purpose, 157 datasets of 47 different materials including rocks, ores, coals, and artificial rocks with uniaxial compressive strength ($${\sigma }_{c}$$), tensile strength ($${\sigma }_{t}$$), cone angle ($$\theta$$), attack angle ($$\gamma$$), cutting depth ($$d$$), and mean CF (MCF) are accumulated from the literature. Then, extreme gradient boosting (XGBoost) model is constructed by fine-tuning hyperparameters using grid search, random search, genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). Based on performance indices that are calculated for each model, i.e., coefficient of determination ($${R}^{2}$$), root mean square error (RMSE), and mean absolute percentage error (MAE) for the best performed model, i.e., DE-XGboost are $${R}^{2}=0.812$$, RMSE = 2256.90 $$N$$, and MAE = 1313.66 $$N$$ for training stage and $${R}^{2}=0.875$$, RMSE = 2104.86 $$N$$, and MAE = 1140.42 $$N$$ for testing stage, respectively. The findings also suggest that using a metaheuristic algorithm to fine-tune the hyperparameters of the XGBoost model can increase prediction accuracy. In the last step, three model interpretation methods viz., the permutation-based variable importance, H-statistic-based variable interaction, and accumulated local effects are applied to sensitivity analysis of the input parameters to predict MCF, providing key insights to model and researchers. The ALE plot demonstrated a complex non-linear relationship between predictors and the response variable. It is revealed that parameters $$d$$ and $$\theta$$ have the highest and lowest impact on the MCF, respectively. Finally, the successful implementation of this approach provides a solid platform for future studies and can be an alternative to complicated conventional and theoretical methods.
Highlights A predictive model to relate the strength of rock, geometry of tool, and cutting action data to mean cutting force is established.Extreme gradient boosting method is optimized by meta‑heuristic algorithms.Differential evolution outperformed grid search, random search, genetic algorithm, and particle swarm optimization algorithms in tunning processApplying adaptive sampling with a minimum resampling number of five and fivefold CV with three repetitions to validate the results.Application of model interpretation methods to assess the influence, interaction, and relation of parameters with the mean cutting force response.
Mean Cutting Force Prediction of Conical Picks Using Ensemble Learning Paradigm
https://orcid.org/0000-0002-1519-6304
Abstract The conical pick is the most essential tool of excavation machinery such as roadheaders, continuous miners, and shearers for breaking rock in mining and civil engineering operations. For rock cuttability, however, the geometry of conical picks and mechanical parameters of rocks are the most important factors. This study aims to construct an optimized data-driven predictive model to establish a quantitative correlation between strength of rock, geometry of tool, and cutting action data with the mean cutting force (CF). For this purpose, 157 datasets of 47 different materials including rocks, ores, coals, and artificial rocks with uniaxial compressive strength ($${\sigma }_{c}$$), tensile strength ($${\sigma }_{t}$$), cone angle ($$\theta$$), attack angle ($$\gamma$$), cutting depth ($$d$$), and mean CF (MCF) are accumulated from the literature. Then, extreme gradient boosting (XGBoost) model is constructed by fine-tuning hyperparameters using grid search, random search, genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). Based on performance indices that are calculated for each model, i.e., coefficient of determination ($${R}^{2}$$), root mean square error (RMSE), and mean absolute percentage error (MAE) for the best performed model, i.e., DE-XGboost are $${R}^{2}=0.812$$, RMSE = 2256.90 $$N$$, and MAE = 1313.66 $$N$$ for training stage and $${R}^{2}=0.875$$, RMSE = 2104.86 $$N$$, and MAE = 1140.42 $$N$$ for testing stage, respectively. The findings also suggest that using a metaheuristic algorithm to fine-tune the hyperparameters of the XGBoost model can increase prediction accuracy. In the last step, three model interpretation methods viz., the permutation-based variable importance, H-statistic-based variable interaction, and accumulated local effects are applied to sensitivity analysis of the input parameters to predict MCF, providing key insights to model and researchers. The ALE plot demonstrated a complex non-linear relationship between predictors and the response variable. It is revealed that parameters $$d$$ and $$\theta$$ have the highest and lowest impact on the MCF, respectively. Finally, the successful implementation of this approach provides a solid platform for future studies and can be an alternative to complicated conventional and theoretical methods.
Highlights A predictive model to relate the strength of rock, geometry of tool, and cutting action data to mean cutting force is established.Extreme gradient boosting method is optimized by meta‑heuristic algorithms.Differential evolution outperformed grid search, random search, genetic algorithm, and particle swarm optimization algorithms in tunning processApplying adaptive sampling with a minimum resampling number of five and fivefold CV with three repetitions to validate the results.Application of model interpretation methods to assess the influence, interaction, and relation of parameters with the mean cutting force response.
Mean Cutting Force Prediction of Conical Picks Using Ensemble Learning Paradigm
https://orcid.org/0000-0002-1519-6304
Abstract The conical pick is the most essential tool of excavation machinery such as roadheaders, continuous miners, and shearers for breaking rock in mining and civil engineering operations. For rock cuttability, however, the geometry of conical picks and mechanical parameters of rocks are the most important factors. This study aims to construct an optimized data-driven predictive model to establish a quantitative correlation between strength of rock, geometry of tool, and cutting action data with the mean cutting force (CF). For this purpose, 157 datasets of 47 different materials including rocks, ores, coals, and artificial rocks with uniaxial compressive strength ($${\sigma }_{c}$$), tensile strength ($${\sigma }_{t}$$), cone angle ($$\theta$$), attack angle ($$\gamma$$), cutting depth ($$d$$), and mean CF (MCF) are accumulated from the literature. Then, extreme gradient boosting (XGBoost) model is constructed by fine-tuning hyperparameters using grid search, random search, genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). Based on performance indices that are calculated for each model, i.e., coefficient of determination ($${R}^{2}$$), root mean square error (RMSE), and mean absolute percentage error (MAE) for the best performed model, i.e., DE-XGboost are $${R}^{2}=0.812$$, RMSE = 2256.90 $$N$$, and MAE = 1313.66 $$N$$ for training stage and $${R}^{2}=0.875$$, RMSE = 2104.86 $$N$$, and MAE = 1140.42 $$N$$ for testing stage, respectively. The findings also suggest that using a metaheuristic algorithm to fine-tune the hyperparameters of the XGBoost model can increase prediction accuracy. In the last step, three model interpretation methods viz., the permutation-based variable importance, H-statistic-based variable interaction, and accumulated local effects are applied to sensitivity analysis of the input parameters to predict MCF, providing key insights to model and researchers. The ALE plot demonstrated a complex non-linear relationship between predictors and the response variable. It is revealed that parameters $$d$$ and $$\theta$$ have the highest and lowest impact on the MCF, respectively. Finally, the successful implementation of this approach provides a solid platform for future studies and can be an alternative to complicated conventional and theoretical methods.
Highlights A predictive model to relate the strength of rock, geometry of tool, and cutting action data to mean cutting force is established.Extreme gradient boosting method is optimized by meta‑heuristic algorithms.Differential evolution outperformed grid search, random search, genetic algorithm, and particle swarm optimization algorithms in tunning processApplying adaptive sampling with a minimum resampling number of five and fivefold CV with three repetitions to validate the results.Application of model interpretation methods to assess the influence, interaction, and relation of parameters with the mean cutting force response.
Mean Cutting Force Prediction of Conical Picks Using Ensemble Learning Paradigm
Fathipour-Azar, Hadi (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
38.58
Geomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB41
A Theoretical Model for Estimating the Peak Cutting Force of Conical Picks
| British Library Online Contents | 2018
A model describing rock cutting with conical picks
| Online Contents | 1985
A model describing rock cutting with conical picks
| Springer Verlag | 1985
Characterization of Wear Mechanisms in Distorted Conical Picks After Coal Cutting
| British Library Online Contents | 2016
Characterization of Wear Mechanisms in Distorted Conical Picks After Coal Cutting
| Online Contents | 2015