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This study aims to identify and maintain optimal stencil printer settings in dynamic surface mount technology (SMT) assembly lines to control the solder paste volume transfer efficiency (TE) and increase the yield rates of surface mount assembly (SMA) lines. Stencil printing process (SPP) is a crucial procedure that determines the first pass yields in SMA lines. Stencil printing speed (PS) and printing pressure (PP) are two critical SPP parameters in the manufacturing process; identifying the optimal printer settings in complex fabrication environment is cost efficient but challenging. To search for the global optimal PS and PP values in real time with minimal computational efforts, a dynamic optimization SPP optimization system is proposed based on a novel guided evolutionary search (GES) strategy. The GES algorithm is flexible to adjust the feasible solution region based on real-time optimization results to maximize the probability of finding optimal solutions and minimize the computational burden. As a major extension on the classical evolutionary search, the GES minimizes the possibility to fall in the local optimal due to insufficiencies in the prediction accuracy and guarantees global optimality within the dynamically varying printing environment.

This article aims to propose a predictive abnormality detection model in the stencil printing process (SPP). The SPP is the main contributor to surface mounting technology (SMT) soldering defects. The prediction of abnormal conditions is necessary to enhance the first-pass yield and reduce the reworking costs of the printed circuit board (PCB) assembly line. In this research, a novel multiphase intelligent abnormality prognosis (IAP) framework is proposed. The model comprises two phases: the abnormality detection phase and the abnormality prediction phase. The first phase is to develop the random forest-based exponential weighted moving average (RF-based EWMA) control chart. The goal is to properly monitor the highly autocorrelated SPP process and effectively recognize the existing patterns. In the second phase, the accurate prediction of anomalies within the SPP before they arise is achieved. The integration of adaptive boosting (AdaBoost) predictive modeling and a moving recognition window approach is proposed. To discriminate the different patterns from each other, features are extracted using the sliding window, and then, the AdaBoost model is adopted to predict the occurrence of abnormal patterns in the SPP. The experimental results confirm the effectiveness and reliability of the proposed framework in early and accurate prediction of abnormal patterns within the SPP process to prevent solder paste printing defects and reduce the high reworking costs for large-scale production.

This article aims to develop a dynamic optimization model performing real-time control of a stencil printing process (SPP) by maintaining the optimal printer parameter settings. In a surface mount technology (SMT) assembly line, stencil printing is a major process that affects the yield of printed circuit boards (PCBs). During printing, environmental changes may induce the PCB’s printing results to deviate from initial optimal outcomes. To consistently improve the system performance, a real-time adaptation of the printer settings is an effective and cost-efficient approach. This research proposes a hybrid online optimization model by using online learning to predict real-time SPP volumes and an evolutionary search (ES) technique to determine the optimal settings. The prediction model investigates the printing volumes’ transfer efficiency (TE) in averages and standard deviations (SDs) with relevant features. From the model selection of the online-based learning, the multi-layer online sequential extreme learning machine (MOSELM) shows outstanding prediction performance with $R^{2}$ values of 97% for volume averages and 81% for SDs. From the real implicational results, the system achieves a $C_{pk}=2.8$ , outperforming other advanced models. The proposed framework exhibits a good balance between accuracy and retraining efficiency, promising effective SMT assembly dynamic control.

This research proposes a stencil cleaning decision-making model in surface mount technology. Stencil cleaning is a critical process that influences the quality and efficiency of printing circuit boards. Stencil cleaning operation depends on various process variables, such as printing speed, printing pressure, and aperture shape. The objective of this research is to develop an intelligent model to guide stencil cleaning decision-making to reduce process defects. The stencil cleaning process is considered as a sequential detection problem in this study. Based on quality measures of printed historical boards, such as solder paste volume and the number of defects, a novel feature space is proposed by considering both short-term and long-term process trend. A gradient boosting model is applied to make the stencil cleaning decision. To validate the effectiveness of the proposed model, different scenarios are designed in the experimental test. State-of-art data mining models are also compared to the proposed cleaning decision-making model. Experimental results show that the proposed boosting-based intelligent model outperforms other models and can effectively provide the cleaning suggestion even the board design is changed in the future.

This research aims to develop a dynamic prediction model to assist real-time decision making of a stencil printing process by maintaining high prediction accuracy of the printing process. In a Surface Mount Technology (SMT) assembly line, the stencil printing process (SPP) accounts for more than 50% of the defectiveness of printed circuit boards (PCBs). During the printing process, environmental changes such as humidity or wear of blades may induce the PCBs printing results to deviate from the target volume. Thus, real-time adjustment of the printer settings (e.g., printing parameters, clean cycles, etc.) based on prediction of printing volumes is critical to maintain a high printing performance. However, research has been limited in real time SPP control, which is partially due to the difficulties in predicting the paste volumes with high accuracy and time efficiency. To tackle the challenges, this research proposes novel online learning models for real-time SPP status prediction. The prediction model is implemented by selecting advanced online learning models to estimate the printing volumes in averages and standard deviations (SDs) considering different pad sizes with different clean ages, directions, printer parameters, etc. The model performances are evaluated in Root Mean Square Error (RMS E), R2, etc. From comparison, the Support Vector Regressor (SVR) shows outstanding prediction performance with R2 values of 92% and 81% for volume averages and SDs. This research emphasizes the potential of using online learning as a preliminary process for effective real-scenario SMT assembly dynamic control.

This research proposes a novel 3D Convolutional Neural Network (CNN) to perform organ tissue segmentation from volumetric 3D medical images. Accurate and efficient segmentation on the 3D medical image of human organ is a critical step towards disease diagnosis. For volumetric 3D medical image segmentation tasks, the effectiveness of conventional 2D CNNs are reduced due to loss of spatial information. To overcome the obstacles, a 3D CNN that implements the convolution and pooling processes in a 3D space is applied as a substitution to the patch division scheme of 2D CNNs. By using 3D CNN, the image becomes scalable in the spatial direction, allowing accurate image detection with different frame sizes. The 3D CNN implements a cube-by-cube scanning strategy, followed by 3D transformation for each cube in terms of convolving and pooling. The segmentation results of 3D CNN is tested on a 3D OCT image dataset of human thyroid. Experimental results demonstrate that the 3D CNN approach obtains outstanding consistency and accuracy.

This research studies a dilated depthwise separable convolution neural network (DSCN) model to identify human tissue types from 3D medical images. 3D medical image classification is a challenging task due to the unpredictable noise and indistinct tissue behaviors of the image content. The objective of this research is to improve typical supervised deep learning model accuracy by using dilated convolution and depthwise separable network approaches on 3D medical image classification tasks. A depthwise separable architecture is used to improve parameter utilization efficiency. Dilated convolutions are applied to systematically aggregate multiscale contextual information and provide a large receptive field with a small number of trainable weights. The performance of the constructed model is tested to perform a multi-class human tissue classification on 3D Optical Coherence Tomography (OCT) images. Experimental results are compared with typical deep learning classification models. The results show that the DSCN model outperforms other models for all the tissue classification tasks. The proposed DSCN model can be a potential approach for the 3D image-based diagnostic tasks in both healthcare and manufacturing field.

The simplified slice-plain-strain method and the incorporating incremental superposition theory were adopted for the cumulative effective strain (CES) of elliptical cross-section spiral equal-channel extrusion (ECSEE) process. The ECSEE deformation was divided into two basic deformation modes: round-ellipse/ellipse-round cross-section transitional channel deformation and elliptical cross-section torsion transitional channel deformation, through tracking a particle of the cross section. The change laws for the combined CES of the particle with the channel length and the combined effective strain (ES) distribution were obtained by MATLAB software programming, and the results were compared with these via Deform-3D finite element method (FEM). The results show that the ECSEE accumulation torsion strain is greater than that of other forms, and the shear deformation is dominant. The blank cross-section ES presents the gradient decreasing trend from the periphery to the center. The FEM results also verify the accuracy of analytical solution.

The elliptical cross-section spiral equal-channel extrusion (ECSEE) process is simulated by using Deform-3D finite element software. The ratio m of major-axis to minor-axis length for ellipse-cross-section, the torsion angle φ , the round-ellipse cross-section transitional channel L 1 , the elliptical rotation cross-section transitional channel L 2 and the ellipse-round cross-section transitional channel L 3 are destined for the extrusion process parameters. The average effective strain e ave on cross-section of blank, the deformation uniformity coefficient α and the value of maximum damage δ max are chosen to be the optimize indexes, and the virtual orthogonal experiment of L 16 (4 5 ) is designed. The correlation degree of the process factors affecting e ave , α and δ max is analyzed by the numerical simulation results using the weights and grey association model. The process parameters are optimized by introducing the grey situation decision theory and the ECSEE optimal combination of process parameters is obtained: φ of 120°, m of 1.55, L 1 of 7 mm, L 2 of 10 mm, and L 3 of 10 mm. Simulation and experimental results show that the material can be refined with the optimized structural parameters of die. Therefore, the optimization results are satisfactory.

Ultrafine-grained (UFG) metal materials processed by severe plastic deformation (SPD) have attracted the great interest. This overview introduces some attractive SPD techniques. Special attention is paid to two new deformation techniques named Elliptical Cross-section Spiral Channel Extrusion with Equal-area (ECSEE) and Elliptical Cross-section Spiral Channel Drawing with Equal-area (ECSDE). The mechanism and microstructure transformation characteristics of materials in SPD, current problems and ongoing research are also discussed in detail.