A non-linear quality improvement model using SVR for manufacturing TFT-LCDs

Li, Der-Chiang; Chen, Wen-Chih; Liu, Chiao-Wen; Lin, Yao-San

doi:10.1007/s10845-010-0440-1

Cited by 30 publications

(7 citation statements)

References 15 publications

(12 reference statements)

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“…Predictive machine learning models have been applied to several industrial problems, such as error detection and diagnosis [29][30][31], quality manufacturing [32][33][34], process monitoring [4,35], and manufacturing automation [36,37]. These machine learning models, which include regression methods, decision trees, support vector machines, and artificial neural networks, have shown better performance compared with conventional methods in various situations.…”

Section: Machine Learning Applications To Industrial Problemsmentioning

confidence: 99%

Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

Lee

Son

2021

Mathematics

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The use of machine learning algorithms to improve productivity and quality and to maximize efficiency in the steel industry has recently become a major trend. In this paper, we propose an algorithm that automates the setup in the cold-rolling process and maximizes productivity by predicting the roll forces and motor loads with multi-layer perceptron networks in addition to balancing the motor loads to increase production speed. The proposed method first constructs multilayer perceptron models with all available information from the components, the hot-rolling process, and the cold-rolling process. Then, the cold-rolling variables related to the normal part set-up are adjusted to balance the motor loads among the rolling stands. To validate the proposed method, we used a data set with 70,533 instances of 128 types of steels with 78 variables, extracted from the actual manufacturing process. The proposed method was found to be superior to the physical prediction model currently used for setups with regard to the prediction accuracy, motor load balancing, and production speed.

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Section: Machine Learning Applications To Industrial Problemsmentioning

confidence: 99%

Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

Lee

Son

2021

Mathematics

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“…Traditionally, many solutions have been proposed to deal with machine learning on small datasets, such as an increase in the sample size (e.g. virtual data and clustering) (Huang & Moraga, 2004;Li et al, 2010;Zhou & Liu, 2006) and dimensionality reduction (feature selection or PCA) (Chandrashekar & Sahin, 2014;Too & Abdullah, 2020). Nonetheless, because they are developed in a cost-insensitive context and take no account of the asymmetry cost, these solutions tend to have a low efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cost-sensitive regression learning on small dataset through intra-cluster product favoured feature selection

Zhao

Zhou

et al. 2021

Connection Science

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Massive regression and forecasting tasks are generally cost-sensitive regression learning problems with asymmetric costs between overprediction and under-prediction. However, existing classic methods, such as clustering and feature selection, are subject to difficulties in dealing with small datasets. As one of the key challenges, it is difficult to statistically validate the importance of features using traditional algorithms (e.g. the Boruta algorithm) owing to insufficient available data. By leveraging the feature information intra-cluster (item group with similar attributes), we propose an intra-cluster product favoured (ICPF) feature selection algorithm to select the information based on the traditional filtering method (specifically the Boruta algorithm in our study). The experimental results show that the ICPF algorithm significantly reduces the number of dimensions of the selected feature set and improves the performance of cost-sensitive regression learning. The misprediction cost decreased by 33.5% (linear-linear cost function) and 32.4% (quadratic-quadratic cost function) after adopting the ICPF algorithm. In addition, the advantage of the ICPF algorithm is robust to other regression models, such as random forest and XGboost.

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“…Statistical quality control [1], a traditional method, has been widely used to assess the quality and performance of manufacturing processes. Based on this method, other techniques have been developed, e.g., linear regression [2,3], nonlinear regression [4], inference learning [5], fuzzy theory [6], and graph theory [7]. These approaches have successfully been applied to manufacturing quality prediction, but only in situations in which the factors (e.g., materials, equipment, and technological parameters) maintain a certain level of stability.…”

Section: Introductionmentioning

confidence: 99%

A Deep Regression Model with Low-Dimensional Feature Extraction for Multi-Parameter Manufacturing Quality Prediction

Deng

Bai

2020

Applied Sciences

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Manufacturing quality prediction can be used to design better parameters at an earlier production stage. However, in complex manufacturing processes, prediction performance is affected by multi-parameter inputs. To address this issue, a deep regression framework based on manifold learning (MDRN) is proposed in this paper. The multi-parameter inputs (i.e., high-dimensional information) were firstly analyzed using manifold learning (ML), which is an effective nonlinear technique for low-dimensional feature extraction that can enhance the representation of multi-parameter inputs and reduce calculation burdens. The features obtained through the ML were then learned by a deep learning architecture (DL). It can learn sufficient features of the pattern between manufacturing quality and the low-dimensional information in an unsupervised framework, which has been proven to be effective in many fields. Finally, the learned features were inputted into the regression network, and manufacturing quality predictions were made. One type (two cases) of machinery parts manufacturing system was investigated in order to estimate the performance of the proposed MDRN with three comparisons. The experiments showed that the MDRN overwhelmed all the peer methods in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. Based on these results, we conclude that integrating the ML technique for dimension reduction and the DL technique for feature extraction can improve multi-parameter manufacturing quality predictions.

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A non-linear quality improvement model using SVR for manufacturing TFT-LCDs

Cited by 30 publications

References 15 publications

Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

Cost-sensitive regression learning on small dataset through intra-cluster product favoured feature selection

A Deep Regression Model with Low-Dimensional Feature Extraction for Multi-Parameter Manufacturing Quality Prediction

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