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

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

Genetic Algorithm for Solving the Integrated Production-Distribution-Direct Transportation Planning

This paper proposes a model of integrated production, distribution and transportation planning for 4-echelon supply chain system that consists of a manufacturer using a continuous production process, a distribution center, distributors and retailers. By means of time-dependent demand at all retailers and direct transportation from one echelon to its successive echelons, the purpose of this paper is to determine production/replenishment and transportation policies at manufacturer, distribution center, distributors and retailers in order to minimize annually total system cost. Due to the proposed model is classified as a mixed integer non-linear programming so it is almost impossible to solve the model using the exact optimization methods and a lot of time is needed when the enumeration methods is applied to solve only a small scale problem. In this paper, we apply the genetic algorithm for solving the model. Using integer encoding for constructing the chromosome, the best solution is going to be searched. Compared with enumeration method, the difference of the result is only 0.0594% with the consumption time is only 0.5609% time that enumeration methods need

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Proceedings / 6th International Symposium of Industrial Engineering - SIE 2015, 24th-25th September, 2015, Belgrade

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi

Proceedings / 6th International Symposium of Industrial Engineering - SIE 2015, 24th-25th September, 2015, Belgrade

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi

Micro-costing study of rituximab subcutaneous injection versus intravenous infusion in dutch setting

Background: Rituximab for subcutaneous (SC) administration has recently been approved for use in common forms of diffuse large B-cell lymphoma (DLBCL). This form of rituximab is supplied in ready-to-use vials that do not require individual dose adjustment. It is expected that SC-injection will shorten the treatment time per administration of rituximab in comparison with currently available intravenous (IV) infusion. Aims: The goal of this study is to identify and compare all direct costs of IV and SC rituximab given to the DLBCL patients in the Netherlands. Methods: Using a prospective, observational, bottom up, micro-costing study we collected primary data on the direct medical costs of the preparation, administration and acquisition of rituximab. Drug costs and spillage, labor costs, material costs and remaining daycare costs were identified using standardized forms, structured using guideline prices and compared for the IV and SC forms of rituximab. Results: Measurements were done on 53 administrations (33 IV and 20 SC). The mean total costs of the IV infusion were €2174, and €1907 for the SC injection. The estimated difference of €267 per administration was mainly due to spillage costs and differences in chair time, related daycare costs and drug costs. Summary and Conclusions: Rituximab administered in the form of SC injection is less costly than its IV form. Taking into account their equal effectiveness, favorable pharmacoeconomic profile of SC rituximab can result in significant savings when transferred to the total DLBCL population in the Netherlands

Trial efficacy vs real world effectiveness in first line treatment of multiple myeloma

Background: Large randomized clinical trials (RCT) are the foundation of the registration of newly developed drugs. A potential problem with RCTs is that the inclusion/exclusion criteria will make the population different from the actual population treated in real life. Hence, it is important to understand how the results from the RCT can be generalized to a general population. Aims: The primary aim of the present study was to assess the generalizability of the large 1st line RCTs in Multiple Myeloma (MM) to the Nordic setting and to understand potential difference and magnitude in outcomes between RCTs and patients treated in standard care in the Nordics. Methods: A retrospective analysis was performed on an incident cohort of 2960 MM-patients from 24 hospitals in Denmark, Finland, Norway and Sweden. The database contained information on patient baseline characteristics, treatments and outcomes. Data from relevant 1st line MM RCTs was selected from the treatment MP (Waage, A., et al., Blood. 2010], MPT (Waage, A., et al., Blood. 2010) and VMP (San Miguel, J.F., et al., N Engl J Med, 2008) and baseline characteristics were compared to newly diagnosed Nordic MM treated patients. Potential difference in response and overall survival (OS) was estimated by adjusting the RWE population to the RCT population using matching adjusted indirect comparisons. Patients were matched on age (median approximated to mean), gender, calcium, beta2-microglobulin and ISS score 3. These variables were selected because they were reported in all trials and have previously been identified as having prognostic value. Results: Patients in the Nordic database treated with MP (n=880) had a response rate of (PD, NR, PR, VGPR, ≥nCR) of (13%, 39%, 38%, 6%, 4%). After matching (n=347), the response rate was slightly worse (12%, 43%, 36%, 6%, 3%). This can be compared to the response rate from the RCT of (7%, 53%, 33%, 3%, 4%). OS for Nordic MP treated patients was 2.67 years (2.25-3.17). After matching the OS was 3.37 years (2.86-3.96) and this can be compared to the trial with OS 2.40 years (2.23-2.66). Patients treated with MPT (n=283) in the Nordic countries had a response rate of (5%, 14%, 52%, 20%, 9%). After matching (n=179) the response rate was slightly changed to (6%, 20%, 50%, 13% 11%). The corresponding RCT response results were 14%, 29%, 34%, 10%, and 13% respectively. OS for Nordic MPT treated patients was 4.15 years (3.73- 4.74). After matching the OS was 4.28 years (3.98-NA) years and compared to 2.42 years (2.08-3.17) OS observed in the corresponding trial. Patients treated with VMP (n=59) in the Nordic countries had a response rate of (4%, 5%, 40%, 18%, 33%). After matching (n=31) the response rate was improved to (8%, 11%, 28%, 8%, 45%). This corresponding response rates shown in the trial are 1%, 23%, 33%, 8%, and 33% respectively. OS for Nordic MP treated patients was 4.86 years (3.79-NA). After matching the OS was 4.86 years (4.86-NA) and this can be compared to the trial with OS 4.70 years. Summary and Conclusions: Surprisingly Nordic treated MM patients do very well compared to, and even better than, patients treated in RCTs. Since the OS for all tested treatments improves after matching to the RCT baseline characteristics, patients recruited to the RCTs seems to be a bit better than ordinary Nordic patents. The database used in the present study, and the used method, can be valuable for generalizing the results to the Nordic setting and estimating potential difference for future RCTs and Nordic MM treated patients. Future research should include different data cuts to see whether the analyses are biased by differences subsequent treatments applied in RCTs and clinical practice

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability