Energy consumption modeling and prediction of the milling process: A multistage perspective

2016 ◽  
Vol 232 (11) ◽  
pp. 1973-1985 ◽  
Author(s):  
Chaoyong Zhang ◽  
Zhiheng Zhou ◽  
Guangdong Tian ◽  
Yang Xie ◽  
Wenwen Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Energy Consumption ◽  
Gene Expression Programming ◽  
Milling Process ◽  
Quadratic Model ◽  
Neighborhood Search ◽  
Accurate Estimation ◽  
Modeling And Prediction ◽  
Prediction Approach ◽  
Energy Consumption Modeling

In order to provide an accurate estimation of energy consumption, this work proposes a novel energy consumption modeling and prediction approach for a milling process from a multistage perspective. Based on its work stages, each stage’s energy consumption model is established by sliding filter, multiple linear regression, and improved gene expression programming (variable neighborhood search–based gene expression programming) methods and then the total energy consumption is predicted through their combination. A case study is given to illustrate the proposed model and its effectiveness. Compared with the full quadratic model, which can fully consider the interaction between cutting factors, the proposed method can achieve the higher accuracy to predict the energy consumption of the milling process.

scholarly journals Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 472 ◽  
Author(s):  
Amir Mahdiyar ◽  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Ahmadreza Hedayat ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gene Expression Programming ◽  
Sensitivity Analyses ◽  
Accurate Estimation ◽  
Blasting Operation ◽  
Simulation Techniques ◽  
Input Variables ◽  
The Impact

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

scholarly journals Gene-expression programming to predict scour at a bridge abutment

2011 ◽  
Vol 14 (2) ◽  
pp. 324-331 ◽  
Author(s):  
H. Md. Azamathulla
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Gene Expression Programming ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Laboratory Measurements ◽  
Ann Modeling ◽  
Bridge Abutment ◽  
Alternative Approach ◽  
Artificial Neural Network Ann

The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modeling in predicting the scour depth at an abutment.

scholarly journals An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming

2012 ◽  
Vol 7 (2) ◽  
pp. 155892501200700 ◽  
Author(s):  
Abdolrasool Moghassem ◽  
Alireza Fallahpour ◽  
Mohsen Shanbeh
Keyword(s):  
Gene Expression ◽  
Breaking Strength ◽  
Gene Expression Programming ◽  
Ann Model ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Spun Yarns ◽  
Artificial Neural Network Ann ◽  
High Degree ◽  
Prediction Approach

Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm.

Comparison of minimum quantity lubrication and wet milling based on energy consumption modeling

2021 ◽  
pp. 095440892110144
Author(s):  
Masuod Bayat ◽  
Mohammad Mahdi Abootorabi
Keyword(s):  
Energy Consumption ◽  
Manufacturing Industry ◽  
Minimum Quantity Lubrication ◽  
Milling Process ◽  
Numerical Control ◽  
Machining Process ◽  
Wet Milling ◽  
Total Energy Consumption ◽  
Minimum Quantity ◽  
Energy Consumption Modeling

Estimating the energy consumed by machining process is substantial because it has a large share of environmental effects in the manufacturing industry. In this paper, a generic energy consumption model was developed for milling processes that is able to be applied in all milling machine tools. Energy consumption of each segment was estimated according to power characteristics and parameters extracted from numerical control (NC) codes, then the total energy consumption was estimated by adding energy consumption of the machine components. Energy consumption of milling process was measured and compared in conventional (wet) and minimum quantity lubrication (MQL) conditions. The developed method was verified by comparing the estimated values of energy consumption with experimental results. Various studies have suggested different types of energy consumption modeling with machining, however; only a few studies have focused on the use of these modeling techniques. Thus, the MQL method has been rarely compared with the wet milling in terms of energy consumption. In the proposed model, energy consumption for workpiece adjustment, accounting for a major part of the costs in machining economics was considered for the first time. The results showed that the proposed method is efficient and practical for predicting energy consumption, with the possibility of occurring 5% error. Analysis of the results revealed that using the MQL method in milling process leads to 33% lower power consumption than wet milling and therefore, the MQL method can reduce the cost of production.

Sign in / Sign up

Export Citation Format

Share Document