Application of ridge regression and factor analysis in design and production of alloy wheels

Ye, Zhi‐Sheng; Li, Jianguo; Zhang, Mengru

doi:10.1080/02664763.2013.872233

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…To check the linearity of the data in this investigation and to separate the classes, few well-known ML regression approaches like Ridge regression (Ye et al, 2014), Random Forest (Methkal et al, 2022) and J48 (Madhusudana et al, 2018) prediction models were used. ML is frequently used in a variety of disciplines to resolve challenging issues that are not easily addressed using conventional methods (Bustillo et al, 2018).…”

Section: Machining Parameters On Flank Wearmentioning

confidence: 99%

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

et al. 2023

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Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

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Section: Machining Parameters On Flank Wearmentioning

confidence: 99%

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

et al. 2023

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“…At first, features of multicollinearity are considered to provide redundant information regarding the target property (i.e. increase dataset's complexity and reduce the result's interpretability) [39]. Here, environmental features with significant correlation (by calculating Pearson correlation coefficient) were grouped into one cluster and they were considered to be multicollinearity features [29].…”

Section: Selection Of the Dominating Featuresmentioning

confidence: 99%

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

Yan

Diao

Lang

et al. 2020

Science and Technology of Advanced Materials

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The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (R 2 values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.

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“…Five of independent variables' VIF are higher than 10 excluding energy structure and foreign trade degree. The VIF above 10 is a reliable indicator of multicollinearity and the higher VIF means a more serious multicollinearity [44]. Thus, the regression results obtained by OLS are meaningless and unreliable to be accepted to analyze Hebei's carbon emissions.…”

Section: ′     ′ (11)mentioning

confidence: 99%

Energy-related CO₂emissions in Hebei province: Driven factors and policy implications

Wen¹,

Liu

2016

Environmental Engineering Research

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The purpose of this study is to identify the driven factors affecting the changes in energy-related CO2 emissions in Hebei Province of China from 1995 to 2013. This study confirmed that energy-related CO2 emissions are correlated with the population, urbanization level, economic development degree, industry structure, foreign trade degree, technology level and energy proportion through an improved STIRPAT model. A reasonable and more reliable outcome of STIRPAT model can be obtained with the introducing of the Ridge Regression, which shows that population is the most important factor for CO2 emissions in Hebei with the coefficient 2.4528. Rely on these discussions about affect abilities of each driven factors, we conclude several proposals to arrive targets for reductions in Hebei's energy-related CO2 emissions. The method improved and relative policy advance improved pointing at empirical results also can be applied by other province to make study about driven factors of the growth of carbon emissions.

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Application of ridge regression and factor analysis in design and production of alloy wheels

Cited by 11 publications

References 21 publications

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

Energy-related CO₂emissions in Hebei province: Driven factors and policy implications

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Application of ridge regression and factor analysis in design and production of alloy wheels

Cited by 11 publications

References 21 publications

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

Energy-related CO2emissions in Hebei province: Driven factors and policy implications

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Product

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Energy-related CO₂emissions in Hebei province: Driven factors and policy implications