Estimation of tool-chip contact length using optimized machine learning in orthogonal cutting
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- @Article{QAZANI:2022:engappai,
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author = "Mohammad Reza Chalak Qazani and
Vahid Pourmostaghimi and Mehdi Moayyedian and Siamak Pedrammehr",
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title = "Estimation of tool-chip contact length using optimized
machine learning in orthogonal cutting",
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journal = "Engineering Applications of Artificial Intelligence",
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volume = "114",
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pages = "105118",
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year = "2022",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2022.105118",
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URL = "https://www.sciencedirect.com/science/article/pii/S0952197622002524",
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keywords = "genetic algorithms, genetic programming, Tool-chip
contact length, Optimization, Adaptive network-based
fuzzy inference system",
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abstract = "Tool-chip contact length has a significant effect on
the various characteristics of metal cutting, including
cutting pressures, chip formation, tool wear, tool
life, and cutting temperatures. It should be added that
there is a direct relationship between the tool-chip
contact length and secondary shear zone thickness in
the metal cutting process. The cutting force and shear
zone temperature decrease by the reduction of tool-chip
contact length. In addition, the tool-chip contact
length affects the tool life and workpiece surface
roughness. Lots of researchers have conducted extensive
research to calculate the tool-chip contact length
using mathematical or machine learning methods. The
main objective of this study is to calculate the
tool-chip contact length using a highly advanced
machine learning method without any time-consuming and
expensive experiments. However, an adaptive
network-based fuzzy inference system (ANFIS) is not
used yet in the prediction of the tool-chip contact
length. In this study, we proposed the ANFIS to predict
the tool-chip contact length for the first time in
orthogonal cutting using depth of cut, feed-rate, and
cutting speed as inputs of the proposed model. As the
second contribution of this study, three
evolutionary-based optimization techniques, including
genetic algorithm, particle swarm optimization, and
grey wolf optimization, as well as global-based
Bayesian optimization, are employed to select the
optimal hyperparameters of the proposed ANFIS model
known as GA-ANFIS, PSO-ANFIS, GWO-ANFIS, and B-ANFIS,
respectively. The proposed methods are designed and
developed in MATLAB software to be compared with the
previous method using genetic programming (GP). The
outcomes of this research demonstrate that the
GWO-ANFIS can decrease the mean square error between
the actual and predicted tool-chip contact length of
15.60percent, 3.67percent, 89.75percent, and
92.17percent in comparison with those of GA-ANFIS,
PSO-ANFIS, B-ANFIS, and GP, respectively. In addition,
the fuzzy logic rule surface of the GWO-ANFIS shows
57.20percent, 30.95percent, and 11.85percent dependency
of tool-chip contact length to cutting speed,
feed-rate, and depth of cut as the inputs of the
orthogonal cutting process, respectively",
- }
Genetic Programming entries for
Mohammad Reza Chalak Qazani
Vahid Pourmostaghimi
Mehdi Moayyedian
Siamak Pedrammehr
Citations