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Different deep convolution neural network (DCNN) models have been proposed for wafer map pattern identification and classification tasks in previous studies. However, factors such as the effect of input image resolution on the classification performance of the proposed models and class imbalance in the training set after splitting the data into training and test sets have not been considered in the previous studies. This study proposes a DCNN model with residual blocks, called Opt-ResDCNN model, for wafer map defect pattern identification and classification by considering 26 * 26 input image resolutions and class imbalance issues during the model training. The proposed model is compared with the previously published defect pattern recognition and classification models in terms of accuracy, precision, recall, and F1 score for 26 * 26 input image size. Using a publicly available wafer map dataset (WM-811K), the proposed method can obtain an average accuracy, precision, recall, and F1 score results of 99.672%, 99.664%, 99.695%, 99.692%, respectively for the 26 * 26 input image resolution.

Lithium-ion (Li-ion) batteries are considered to be one of the ideal energy sources for automotive and electronic products due to their size, high levels of charge, higher energy density, and low maintenance. When Li-ion batteries are used in harsh environments or subjected to poor charging habits, etc., their degradation will be accelerated. Thus, online state of health (SOH) estimation becomes a hot research topic. In this study, normalized capacity is considered as SOH for the estimation and calculation of remaining useful lifetime (RUL). A multi-step look-ahead forecast-based deep learning model is proposed to obtain SOH estimates. A total of six batteries, including three as source datasets and three as target datasets, are used to validate the deep learning model with a transfer learning approach. Transferability measures are used to identify source and target domains by accounting for cell-to-cell differences in datasets. With regard to the SOH estimation, the root mean square errors (RMSEs) of the three target batteries are 0.0070, 0.0085, and 0.0082, respectively. Concerning RUL prediction performance, the relative errors of the three target batteries are obtained as 2.82%, 1.70%, and 0.98%, respectively. In addition, all 95% prediction intervals of RUL on the three target batteries include the end-of-life (EOL) value (=0.8). These results indicate that our method can be applied to battery SOH estimation and RUL prediction.

Sustainability in operations and supply chains is becoming more popular among academics and practitioners through Sustainable Supplier Selection (SSS). In addition to balancing economic, social, and environmental factors, the awareness of the United Nations Sustainable Development Goals 2030 has affected the selection of long-term suppliers, ensuring green operations and sustainable supply chains. The criteria for SSS have multiple dimensions and are interdependent; this mimics the real-world scenario rather than assuming independently from an analytic hierarchy process. We use the multi-criteria decision-making (MCDM) model, combining DEMATEL-based on ANP (called DANP) with VIKOR, to solve the SSS problem. The DANP method is used to model and assess the interdependent relationships between criteria. Then, ranking the available alternatives and selecting the best one can be accomplished using the VIKOR method. We consider the electronic manufacturing industry in Taiwan as an empirical case. This study, in addition to selecting the best sustainable supplier, demonstrates the use of influential network relationship maps to analyze and improve the gaps in each dimension and criterion.

Different deep convolution neural network (DCNN) models have been proposed for wafer map pattern identification and classification tasks in previous studies. However, factors such as the effect of input image resolution on the classification performance of the proposed models and class imbalance in the training set after splitting the data into training and test sets have not been considered in the previous studies. This study proposes a DCNN model with residual blocks, called Opt-ResDCNN model, for wafer map defect pattern identification and classification by considering 26 * 26 input image resolutions and class imbalance issues during the model training. The proposed model is compared with the previously published defect pattern recognition and classification models in terms of accuracy, precision, recall, and F1 score for 26 * 26 input image size. Using a publicly available wafer map dataset (WM-811K), the proposed method can obtain an average accuracy, precision, recall, and F1 score results of 99.672%, 99.664%, 99.695%, 99.692%, respectively for the 26 * 26 input image resolution.