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

Apollo experience report: Command and service module controls and displays subsystem

A review of the command and service module controls and displays subsystem is presented. The subsystem is described, and operational requirements, component history, problems and solutions, and conclusions and recommendations for the subsystem are included

Visual Inspection System To Detect Connector Tilts In Pcbas

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

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

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.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Reverse engineering of printed circuit boards: A conceptual idea

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB).The recent rapid growth in electronics devices, results escalating in the production number of the PCBs.For electronic equipment and appliances which are PCB based, new generations of PCB's are produced to suit the requirements of new products.This development can lead to waste and inefficiency when perfectly serviceable electronic components and appliances have to be scrapped because of the unavailability of spare PCB's from the Original Equipment Manufacturer (OEM) or are already obsolete.This paper proposed a novel framework for reverse engineering of obsolete single layer PCB.Equivalent PCB's which can be used as spares will be reproduced utilizing this new framework.This framework involves several steps, such as Data Acquisition, Image Processing, CAD Editing, PCB Fabrication and Circuitry testing and Analysis.Each stage of the framework and the functionality evaluation of the reproduced PCB will be discussed in detail in following sections

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

A reference architecture for flexibly integrating machine vision within manufacturing

A reference architecture provides an overall framework that may embrace models, methodologies and mechanisms which can support the lifecycle of their target domain. The work described in this thesis makes a contribution to establishing such a generally applicable reference architecture for supporting the lifecycIe of a new generation of integrated machine vision systems. Contemporary machine vision systems consist of a complex combination of mechanical engineering, the hardware and software of an electronic processor, plus optical, sensory and lighting components. "This thesis is concerned with the structure of the software which characterises the system application. The machine vision systems which are currently used within manufacturing industry are difficult to integrate within the information systems required within modem manufacturing enterprises. They are inflexible in all but the execution of a range of similar operations, and their design and implementation is often such that they are difficult to update in the face of the required change inherent within modem manufacturing. The proposed reference architecture provides an overall framework within which a number of supporting models, design methodologies, and implementation mechanisms can combine to provide support for the rapid creation and maintenance of highly structured machine vision applications. These applications comprise modules which can be considered as building blocks of CIM systems. Their integrated interoperation can be enabled by the emerging infrastructural tools which will be required to underpin the next generation of flexibly integrated manufacturing systems. The work described in this thesis concludes that the issues of machine vision applications and the issues of integration of these applications within manufacturing systems are entirely separate. This separation is reflected in the structure of the thesis. PART B details vision application issues while PAIIT C deals with integration. The criteria for next generation integrated machine vision systems, derived in PART A of the thesis, are extensive. In order to address these criteria and propose a complete architecture, a "thin slice" is taken through the areas of vision application, and integration at the lifecycle stages of design, implementation, runtime and maintenance. The thesis describes the reference architecture, demonstrates its use though a proof of concept implementation and evaluates the support offered by the architecture for easing the problems of software change

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