Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

One of the common problems of organizations with turn-key projects is the high scrap rate. There exist such traditional methods as Lean Six Sigma (LSS) and DMAIC tools that analyze causes and suggest solutions. New emerging intelligent technologies should influence these methods and tools as they affect many areas of our life. The purpose of this paper is to present the innovative Small Mixed Batches (SMB). The standard set of LSS tools is extended by intelligent technologies such as artificial neural networks (ANN) and machine learning. The proposed method uses the data-driven quality strategy to improve the turning process at the bakery machine manufacturer. The case study shows the step-by-step DMAIC procedure of critical to quality (CTQ) characteristics improvement. Findings from the data analysis lead to a change of measurement instrument, training of operators, and lathe machine set-up correction. However, the scrap rate did not decrease significantly. Therefore the advanced mathematical model based on ANN was built. This model predicts the CTQ characteristics from the inspection certificate of the input material. The prediction model is a part of a newly designed process control scheme using machine learning algorithms to reduce the variability even for input material with different properties from new suppliers. Further research will be focused on the validation of the proposed control scheme, and acquired experiences will be used to support business sustainability.

Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer

Monitoring manufacturing process variation remains challenging, especially within a rapid and automated manufacturing environment. Problematic and unstable processes may produce distinct time series patterns that could be associated with assignable causes for diagnosis purpose. Various machine learning classification techniques such as artificial neural network (ANN), classification and regression tree (CART), and fuzzy inference system have been proposed to enhance the capability of traditional Shewhart control chart for process monitoring and diagnosis. ANN classifiers are often opaque to the user with limited interpretability on the classification procedures. However, fuzzy inference system and CART are more transparent, and the internal steps are more comprehensible to users. There have been limited works comparing these two techniques in the control chart pattern recognition (CCPR) domain. As such, the aim of this paper is to demonstrate the development of fuzzy heuristics and CART technique for CCPR and compare their classification performance. The results show the heuristics Mamdani fuzzy classifier performed well in classification accuracy (95.76%) but slightly lower compared to CART classifier (98.58%). This study opens opportunities for deeper investigation and provides a useful revisit to promote more studies into explainable artificial intelligence (XAI).

https://www.mdpi.com/2073-8994/13/1/110/pdf

Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

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Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Abstract Hydrological extremes occupy a large spatial extent, with a temporal sequence, both of which can be influenced by a range of climatological and geographical phenomena. Understanding the key information in the spatial and temporal domain is essential to make accurate forecasts. The capabilities of deep learning methods can be applied in such instances due to their enhanced ability in learning complex relationships. Given its success in other domains, this study presents a framework that features a long short-term memory deep learning model for spatio temporal hydrological extreme forecasting in the South Pacific region. The data consists of satellite rainfall estimates and sea surface temperature (SST) anomalies. We use the satellite rainfall estimate to calculate the effective drought index (EDI), an indicator of hydrological extreme events. The framework is developed to forecast monthly EDI using three different approaches: (i) univariate (ii) multivariate with neighbouring spatial points (iii) multivariate with neighbouring spatial points and the eigenvector values of SST. Additionally, better identification of extreme wet events is noted with the inclusion of the eigenvector values of SST. By establishing the framework for the multivariate approach in two forms, it is evident that the model accuracy is contingent on understanding the dominant feature which influences precipitation regimes in the Pacific. The framework can be used to better understand linear and non-linear relationships within multi-dimensional data in other study regions, and provide long-term climate outlooks. 

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Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model

Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

Statistical Process Control with Intelligence Based on the Deep Learning Model

Control charts are an important tool for statistical process control (SPC). SPC has the characteristics of fluctuation and asymmetry in the symmetrical coordinate system. It is a graph with control limits used to analyze and judge whether the process is in a stable state. Its fast and accurate identification is of great significance to the actual production. The existing control chart pattern recognition (CCPR) method can only recognize a control chart with fixed window size, but cannot adjust with different window sizes according to the actual production needs. In order to solve these problems and improve the quality management effect in the manufacturing process, a new CCPR method is proposed based on convolutional neural network (CNN) and information fusion. After undergoing feature learning, CNN is used to extract the best feature set from the control chart, while at the same time, expert features (including one shape features and four statistical features) are fused to complete the CCPR. In this paper, the control charts of 10 different window sizes are generated by the Monte Carlo simulation method, and various data patterns are drawn into images, then the CCPR model is set up. Finally, simulation experiments and a real example are addressed to validate its feasibility and effectiveness. The results of simulation experiments demonstrate that the recognition method based on CNN can be used for pattern recognition for different window size control charts, and its recognition accuracy is higher than the traditional ones. In addition, the recognition method based on information fusion performs much better. The result of a real example shows that the method has potential application in solving the pattern recognition problem of control charts with different window sizes.

https://www.mdpi.com/2073-8994/12/9/1472/pdf

Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion

Pd nanocrystals modified Ni foam electrode (Pd NCs/Ni) with Pd loading of 12.5 μg cm−2 was prepared via electrodeposition and exhibited higher mass activity in the electrochemical hydrodechlorination. With the assistance of polyvinylpyrrolidone and bromide ion, Pd nanocrystals with abundant catalytic sites were uniformly anchored on both the inner and surfaces of the Ni foam. Six chlorophenols were rapidly dechlorinated to phenol using Pd NCs/Ni with high conversion (> 98%) and selectivity (∼ 100%). Pd NCs/Ni showed broad applicability to various reaction conditions and maintained stable activity over 12 consecutive cycles. The electrochemical hydrodechlorination follows the indirect dechlorination mechanism. Hydrogenolysis between adsorbed H and chlorophenols is the rate-controlling step. Low coordination Pd atoms on the faces of Pd (220) and Pd (311) are the main catalytic sites. The study provides a valuable insight of high performance and low cost Pd modified electrode and its catalytic mechanism in electrochemical hydrodechlorination. 

Ultra-low-loading Pd nanocrystals modified Ni foam electrode for efficient electrochemical hydrodechlorination


 There are a number of challenges associated with personalized medicines. For example, many models revolve around the question of whether or not the in vitro models capture the relevant in vivo pathobiology and drug response information that may impact a patient's response to a therapy. Most in vitro oncology drug screening techniques are based on two-dimensional (2D) cell culture, which, to date, has failed to accurately imitate the physiological microenvironments due to the inherent limitations, such as the lack of interactions between intracellular and extracellular environments, or modifications in three-dimensional cell morphology, polarity, and division. These disadvantages prompted the development of models which would better simulate the microenvironment of tumor cells. Three-dimensional (3D) culture systems are becoming increasingly popular due to their ability to mimic natural cell characteristics and tissue-like structures compared with traditional 2D monolayer cell culture. Currently, multicellular spheroid and organoid cell culture are two popular 3D culture methods to evaluate drug activity. Spheroid cell culture utilizes ultra-low attachment plate to allow cells grow in spheroid format. Organoid cell culture is accomplished by growing cells in extracellular matrix scaffolds (such as Matrigel). In this study, we attempted to compare different drug screening platforms, including patient-derived xenograft (PDX) and two 3D cell cultures (spheroid and organoid). Compound sensitivity, genomics, pathology and expression of clinical marker were profiled. The purpose of this study is to identify an optimal model as a drug testing platform and a bridge between in vitro studies and animal experiments for identifying novel therapeutic targets.
 Citation Format: Chao Zhang, Qinghua Wu, Lei Jin, Deyin Chen, Xuzhen Tang, Hui Qi, Wenjie Gao, Qunsheng Ji. Comprehensive evaluation of three-dimensional (3D) culture systems as compound screening platforms with primary cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6015.

Abstract 6015: Comprehensive evaluation of three-dimensional (3D) culture systems as compound screening platforms with primary cancer cell lines

This paper is aiming at the problem that the existing anomaly discrimination methods of control chart can not realize the discrimination of complex anomaly data and the low level of intelligence. It is exploring the performance of control chart pattern recognition based on deep learning. It briefly introduces 1DCNN, LSTM and BiLSTM then adopts a neural network model based on 1DCNN+BiLSTM. The Monte Carlo method is used to generate the simulation data of the control chart, and different abnormal data are generated by changing the parameters for simulation experiments. According to the effect of control chart pattern recognition under different abnormal data, the training samples are optimized and the parameters of the network model are determined. The results show that the proposed method performs better in recognition.

Deyin Chen

Papers

Statistical Process Control with Intelligence Based on the Deep Learning Model

Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion

Ultra-low-loading Pd nanocrystals modified Ni foam electrode for efficient electrochemical hydrodechlorination

Abstract 6015: Comprehensive evaluation of three-dimensional (3D) culture systems as compound screening platforms with primary cancer cell lines

Research on Pattern Recognition Performance of Control Chart Based on Deep Learning