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

Business Intelligence in Industry 4.0: State of the art and research opportunities

Data collection and analysis have been at the core of business intelligence (BI) for many years, but traditional BI must be adapted for the large volume of data coming from Industry 4.0 (I4.0) technologies. They generate large amounts of data that need to be processed and used in decision-making to generate value for the companies. Value generation of I4.0 through data analysis and integration into strategic and operational activities is still a new research topic. This study uses a systematic literature review with two objectives in mind: understanding value creation through BI in the context of I4.0 and identifying the main research contributions and gaps. Results show most studies focus on real-time applications and integration of voluminous and unstructured data. For business research, more is needed on business model transformation, methodologies to manage the technological implementation, and frameworks to guide human resources training

Design and Development of Robotic Part Assembly System under Vision Guidance

Robots are widely used for part assembly across manufacturing industries to attain high productivity through automation. The automated mechanical part assembly system contributes a major share in production process. An appropriate vision guided robotic assembly system further minimizes the lead time and improve quality of the end product by suitable object detection methods and robot control strategies. An approach is made for the development of robotic part assembly system with the aid of industrial vision system. This approach is accomplished mainly in three phases. The first phase of research is mainly focused on feature extraction and object detection techniques. A hybrid edge detection method is developed by combining both fuzzy inference rule and wavelet transformation. The performance of this edge detector is quantitatively analysed and compared with widely used edge detectors like Canny, Sobel, Prewitt, mathematical morphology based, Robert, Laplacian of Gaussian and wavelet transformation based. A comparative study is performed for choosing a suitable corner detection method. The corner detection technique used in the study are curvature scale space, Wang-Brady and Harris method. The successful implementation of vision guided robotic system is dependent on the system configuration like eye-in-hand or eye-to-hand. In this configuration, there may be a case that the captured images of the parts is corrupted by geometric transformation such as scaling, rotation, translation and blurring due to camera or robot motion. Considering such issue, an image reconstruction method is proposed by using orthogonal Zernike moment invariants. The suggested method uses a selection process of moment order to reconstruct the affected image. This enables the object detection method efficient. In the second phase, the proposed system is developed by integrating the vision system and robot system. The proposed feature extraction and object detection methods are tested and found efficient for the purpose. In the third stage, robot navigation based on visual feedback are proposed. In the control scheme, general moment invariants, Legendre moment and Zernike moment invariants are used. The selection of best combination of visual features are performed by measuring the hamming distance between all possible combinations of visual features. This results in finding the best combination that makes the image based visual servoing control efficient. An indirect method is employed in determining the moment invariants for Legendre moment and Zernike moment. These moments are used as they are robust to noise. The control laws, based on these three global feature of image, perform efficiently to navigate the robot in the desire environment

Application of artificial intelligence techniques to the smart control of sheet metal forming processes

The present research work aims at evaluating the economical feasibility and the technological viability of implementing intelligent control systems in complex industrial manufacturing processes; in this case forming processes. Forming processes are manufacturing processes that use force and pressure in order to modify the shape of a material part until getting the final product. The wide range of non-linear factors (material properties, tool geometry, machine parameters and lubrication variables) that determine the final quality of the parts manufactured by these processes makes them to be inherently quite unstable. Thus, the control made by human operators is still essential nowadays. On the other hand, although human operators have demonstrated to be a very successful strategy when controlling this type of processes, the actual market evolution towards the fabrication of more complex parts, made of lower formability materials at higher production rates is decreasing their capacity of reaction when solving the daily problems. Therefore, the development of new automatic and global control systems based, not on traditional control techniques and mathematical models but on the control strategy that has been successfully used for many years, the control through the experience and knowledge, is now even more necessary. In the present research work, two intelligent control systems based on AI techniques have been developed and evaluated. The main purpose of these intelligent control systems is to identify the process failures at forming processes and to propose the right solutions that should lead to their solution, all this in a quick and reliable way. Following this strategy, the solution of the process failures is considerably simplified because, after any process failure of defective part detection, human operators find a report where an explanation of the incidence, as well as its causes and the way to solve it, are displayed. This has the inherent advantage of decreasing the length of the downtimes at the manufacturing facilities and thus increasing the number of parts produced. Together with the previously described core of the global control systems, two monitoring systems have been developed and implemented in a forming facility too. The purpose of these monitoring systems is to work as the senses of the intelligent control systems. The first one, an artificial vision system, is aimed at evaluating the quality of the produced parts by carrying out a 100% quality control at the end of the forming process. This will assure the right quality of all the products shipped to the customer. The second one, a sensors based process monitoring system, is aimed at detecting any process failure at the forming facility by means of force and acoustic emissions measurements. This will reduce the internal defective and will assure the security of the forming facility. Both systems are in charge of detecting any process failure and defective part and of reporting about them to the intelligent control system. Since the aim of the research work was to evaluate the feasibility of implementing global intelligent control systems in the industry, all the developments and results achieved through the present research work have been carried out in an industrial environment. The research work is principally divided into three main parts; 1) the development and implementation of the sensors based process monitoring system, 2) the development and implementation of the AV monitoring system and 3) the development of the intelligent control systems. At the end, a summary of all the results and conclusions achieved through the development of the previous mentioned systems is given too.Ikerkuntza lan honen helburua sistema adimendunak fabrikazio prozesu konplexuak kontrolatzeko erabiltzearen bideragarritasuna aztertzea da, bai ekonomikoki eta teknologikoki. Kasu honetan, konformazio prozesuetan inplementatutako sistema adimenduak ikertu dira. Konformazio prozesuak, amaierako produktua lortzeko, hasierako materialari esfortzu edo presioen bidez forma geometrikoa aldatzean datzate. Konformaturiko piezen amaierako kalitatea finkatzen duten aldagai ez-linealen ugaritasun zabalak (materialen propietateak, lanabesen geometriak, makinen parametroak eta/edo lubrifikazioa) prozesu hauek ezegonkorrak izatea ondorioztatzen du. Hori dela medio, gaur egun ere, prozesu hauen kontrola giza-langile bidez egiten da. Langileak prozesu hauek modu eraginkorrean kontrolatzeko gai direla erakutsi du esperientziak. Dena den, deformagarritasun txikiagoko materialez eginiko pieza konplexuagoak kadentzia altuagoetan fabrikatzeko gaur egungo joerak, langileek ezustekoen aurrean erantzuteko duten gaitasuna gutxitu du. Ondorioz, prozesua gainbegiratu eta kontrolatzen duten sistema automatiko eta adimendu berrien garapena beharrezkoa bihurtu da. Sistema hauek ez daude kontrol teknika tradizional edo eredu matematikoetan oinarrituak. Sistema hauen kontrola ezagutza eta esperientzian oinarriturik dago, zeinak azken urteetan emaitza onak eman dituen. Ikerkuntza lan honetan adimen artifizial tekniketan oinarrituriko bi kontrol sistema adimendun garatu eta baloratu dira. Sistema hauen helburu nagusia konformazio prozesuetan emaniko akatsak identifikatu eta automatikoki ebazpenproposamenak aurkeztea da, modu azkar eta sendoan. Estrategia hau jarraituz, prozesuko akatsen ebazpena errazten da, pieza akastunak atzematean edo makinaren geldialdi baten aurrean, sistemak langilea eman beharreko pausuak azaltzen dizkion txosten batez hornituko baitu. Makinaren geldialdiaren murriztea eta ondorioz, produktibitatea igotzea da honen abantaila nagusia, akatsen identifikazioa berehalakoa baita. Kontrol sistema garatzeaz gain, puntzonaketa instalakuntza batean bi monitorizazio sistema martxan jarri dira. Bi monitorizazio sistema hauen helburua prozesuaren informazioa jaso eta kontrol sistemari bidaltzea da. Lehenengoa ikuspen artifizialeko sistema bat da, zeinaren helburua ekoiztutako piezen %100aren kalitatea aztertzea den. Honenbestez, bezeroei bidalitako piezen kalitate egokia bermatzen da. Bigarrena sentsoreetan oinarrituriko prozesuen monitorizazio sistema bat da. Bere helburua prozesuan emaniko edozein akats antzematea da. Honek akastun piezen kantitatea gutxitzen du eta instalakuntzak prozesuen ezegonkortasunetatik babesten ditu. Ondorioz, bi sistemen helburua prozesuan izandako arazo edo pieza akastunak antzematea eta kontrol sistemari hauen berri ematea da. Lan honen helburua aurrez aipaturiko sistemen gaitasuna industri ingurunean ebaluatzea denez, aurkezturiko garapen eta emaitzak enpresa batean burutu dira. Hiru atal nagusi bereiz daitezke lan honetan: 1) sentsoreetan oinarrituriko monitorizazio sistema baten garapen eta inplementazioa; 2) ikuskapen artifizialeko sistemaren garapen eta inplementazioa; eta 3) adimendun kontrolean oinarrituriko sistemen garapena.El presente trabajo de investigación tiene como objetivo evaluar en qué condiciones es económicamente viable y tecnológicamente factible la implementación de sistemas inteligentes de control en procesos de fabricación complejos; en este caso procesos de conformado. Los procesos de conformado son procesos de fabricación basados en la aplicación de esfuerzos o presiones sobre componentes con el objetivo de modificar su forma geométrica hasta conseguir un producto final. El gran abanico de variables no lineales (propiedades de materiales, geometría de herramientas, parámetros de máquinas y/o lubricación) que determinan la calidad final de las piezas conformadas hacen que estos procesos sean inherentemente inestables. Por ello, aun hoy en día, el control de estos procesos se realiza mediante operarios humanos. Por otro lado, aunque la experiencia ha demostrado que los operarios son capaces de controlar estos procesos de manera eficiente, la actual tendencia hacia la fabricación de piezas más complejas, fabricadas en materiales menos deformables y todo ello a cadencias de fabricación mayores, ha hecho que la capacidad de los operarios para reaccionar ante imprevistos se haya visto mermada. Por lo tanto, el desarrollo de nuevos sistemas automáticos e inteligentes de supervisión y control basados, no en técnicas tradicionales de control o en modelos matemáticos, sino en la estrategia de control que ha dado buenos resultados a lo largo de los años, el control basado en la experiencia y el conocimiento, es cada vez más necesario. En el presente trabajo de investigación, se han desarrollado y evaluado dos sistemas inteligentes de control basados en técnicas de inteligencia artificial. El principal objetivo de estos sistemas es ser capaces de identificar los fallos de proceso en procesos de conformado así como de plantear, automáticamente, las instrucciones para su resolución, todo ello de una manera rápida y robusta. Siguiendo esta estrategia, la resolución de los fallos de proceso se simplifica ya que, tras una parada de máquina o la detección de piezas defectuosas, el sistema proporciona al operario un informe donde se detallan las acciones a llevar a cabo. Esto tiene como ventaja una reducción en los tiempos de parada de máquina (y por lo tanto aumento en la cantidad de piezas producidas) ya que la identificación de los fallos es inmediata. Junto con el núcleo del sistema global de control, se han desarrollado e implementando en una instalación de corte progresivo dos sistemas de monitorización. El objetivo de estos dos sistemas de monitorización es recoger información sobre el proceso y enviársela al sistema de control. El primero, un sistema de visión artificial, tiene como objetivo analizar la calidad del 100% de las piezas fabricadas. Esto asegura la correcta calidad de todas las piezas enviadas a los clientes. El segundo, un sistema de monitorización de procesos basado en sensores, tiene como objetivo la detección de cualquier fallo de proceso. Esto reduce el defectivo interno y protege a las instalaciones frente a anomalías de proceso. Por lo tanto, ambos sistemas tienen como misión la detección de cualquier anomalía de proceso o pieza defectiva así como informar al sistema de control sobre las mismas. Puesto que el objetivo de este trabajo es evaluar la capacidad de los sistemas anteriormente citados en el entorno industrial, todos los desarrollos y resultados obtenidos a lo largo del mismo se han llevado a cabo en una empresa. El trabajo se puede dividir en tres partes: 1) el desarrollo e implementación del sistema de monitorización basado en sensores, 2) el desarrollo e implementación del sistema de visión artificial y 3) el desarrollo de los sistemas de control inteligentes

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways