Industry 4.0: Mining Physical Defects in Production of Surface-Mount Devices

With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices. We show how to build a classification model based on random forests that efficiently detects defect products with a high degree of precision. In fact, the results of our preliminary experimental analysis indicate that our approach is able to correctly determine defects in a simulated production environment of surface-mount devices with a MCC score of 0.96. We investigate the feasibility of utilizing this approach in realistic settings. We believe that our approach will help to advance the production of surface-mount devices

Automated robotic inspection system for electronic manufacturing

An automated robotic inspection system for electronic manufacturing has been developed to identify pin defects of IC packages mounted on printed circuit boards using surface mount technology. The automated robotic inspection system consists of two robots, a computer, a CCD camera with frame gabber for image acquisition, and a customized windows program using neural network for on-line defect identification. Gray scale images of the pins on IC packages are acquired using ambient light. The images are filtered and formatted to appropriate size, so that Matlab neural network tool could be used. The images are used to train neural networks using Matlab\u27s Bayesian Regularization module. Optimal network was found to be a single-layer network with three outputs for each IC investigated. The weights and biases of each of the ICs investigated and the matrices of gray scale values for the IC images are saved as text files. A customized windows program uses these text files for on-line defect identification. The defect identification for the networks was found to be 100 percent for the two ICs investigated. The analysis and integration of an automated robotic inspection system for on-line monitoring of electronic manufacturing using neural networks is presented in this work

Application Of Machine Vision On Solder Joint Inspection Using Orthogonal And Oblique Views

Machine vision has been widely deployed in many industrial applications. However, the use of machine vision to perform solder joint inspection of electronic assembly has yet to reach the desired maturity level. Solder joint surfaces are minute, curved and the shapes tend to vary greatly with the soldering conditions. These characteristics have posted a challenging task to develop an effective machine vision with acceptable level of classification accuracy. This research aims to investigate a new methodology of inspecting solder joint through analysis of the combined image from two viewing directions; one from orthogonal view while the other from oblique view. The concept based on the physics of surface tension, contact angle and slant angle of solder joint fillet

Fine-grained Classification of Solder Joints with {\alpha}-skew Jensen-Shannon Divergence

Solder joint inspection (SJI) is a critical process in the production of printed circuit boards (PCB). Detection of solder errors during SJI is quite challenging as the solder joints have very small sizes and can take various shapes. In this study, we first show that solders have low feature diversity, and that the SJI can be carried out as a fine-grained image classification task which focuses on hard-to-distinguish object classes. To improve the fine-grained classification accuracy, penalizing confident model predictions by maximizing entropy was found useful in the literature. Inline with this information, we propose using the {\alpha}-skew Jensen-Shannon divergence ({\alpha}-JS) for penalizing the confidence in model predictions. We compare the {\alpha}-JS regularization with both existing entropyregularization based methods and the methods based on attention mechanism, segmentation techniques, transformer models, and specific loss functions for fine-grained image classification tasks. We show that the proposed approach achieves the highest F1-score and competitive accuracy for different models in the finegrained solder joint classification task. Finally, we visualize the activation maps and show that with entropy-regularization, more precise class-discriminative regions are localized, which are also more resilient to noise. Code will be made available here upon acceptance.Comment: Submitted to IEEE Transactions on Components, Packaging and Manufacturing Technolog

Visual Inspection System To Detect Connector Tilts In PCBAs [TS156. V844 2005 f rb] [Microfiche 7845].

Sistem pemeriksaan visual automatic memainkan peranan penting dalam bahagian tapisan kualiti di industri eletronik. AVI’s are playing important roles in quality inspection in the electronic industry

The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly

Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PD

Understanding, Modeling and Predicting Hidden Solder Joint Shape Using Active Thermography

Characterizing hidden solder joint shapes is essential for electronics reliability. Active thermography is a methodology to identify hidden defects inside an object by means of surface abnormal thermal response after applying a heat flux. This research focused on understanding, modeling, and predicting hidden solder joint shapes. An experimental model based on active thermography was used to understand how the solder joint shapes affect the surface thermal response (grand average cooling rate or GACR) of electronic multi cover PCB assemblies. Next, a numerical model simulated the active thermography technique, investigated technique limitations and extended technique applicability to characterize hidden solder joint shapes. Finally, a prediction model determined the optimum active thermography conditions to achieve an adequate hidden solder joint shape characterization. The experimental model determined that solder joint shape plays a higher role for visible than for hidden solder joints in the GACR; however, a MANOVA analysis proved that hidden solder joint shapes are significantly different when describe by the GACR. An artificial neural networks classifier proved that the distances between experimental solder joint shapes GACR must be larger than 0.12 to achieve 85% of accuracy classifying. The numerical model achieved minimum agreements of 95.27% and 86.64%, with the experimental temperatures and GACRs at the center of the PCB assembly top cover, respectively. The parametric analysis proved that solder joint shape discriminability is directly proportional to heat flux, but inversely proportional to covers number and heating time. In addition, the parametric analysis determined that active thermography is limited to five covers to discriminate among hidden solder joint shapes. A prediction model was developed based on the parametric numerical data to determine the appropriate amount of energy to discriminate among solder joint shapes for up to five covers. The degree of agreement between the prediction model and the experimental model was determined to be within a 90.6% for one and two covers. The prediction model is limited to only three solder joints, but these research principles can be applied to generate more realistic prediction models for large scale electronic assemblies like ball grid array assemblies having as much as 600 solder joints