DEFECT PROPORTION OF DETECTION INITIAL RATE DETECTION RATE INSPECTOR 3 COMPLEXITY OF TIMES PAN OF PERFORMING o~ ________________________ o~ ______________________ __ -;. INSPECTION TASK -;. VISUAL INSPECTION Fagure 1. Trends in relations between the complexity of inspection tasks, defect detection rates (absolute and relative), and inspection time. Irrespective of the necessities described above, and with the excep tion of specific generic application systems (e.g., bare-board PCB inspection, wafer inspection, solder joint inspection, linewidth measure ment), vision systems are still not found frequently in today's electronics factories. Besides cost, some major reasons for this absence are: 1. The detection robustness or accuracy is still insufficient. 2. The total inspection time is often too high, although this can frequently be attributed to mechanical handling or sensing. 3. There are persistent gaps among process engineers, CAD en gineers, manufacturing engineers, test specialists, and computer vision specialists, as problems dominate the day-to-day interac tions and prevent the establishment of trust. 4. Computer vision specialists sometimes still believe that their contributions are universal, so that adaptation to each real problem becomes tedious, or stumbles over the insufficient availabIlity of multidisciplinary expertise. Whether we like it or not, we must still use appropriate sensors, lighting, and combina tions of algorithms for each class of applications; likewise, we cannot design mechanical handling, illumination, and sensing in isolation from each other.

A discussion of applications of machine vision technology in the semiconductor, electronic, automotive, wood, food, pharmaceutical, printing, and container industries. It describes systems that enable projects to move forward swiftly and efficiently, and focuses on the nuances of the engineering and system integration of machine vision technology.

The research and exploitation of optoelectronic properties in the industrial branch of electronics is becoming more popular each day due to the important role they play in the development of a large variety of sensors, devices, and systems for identifying, measuring, and constructing. While optoelectronics study the applications of electronic devices that source, detect, and transform light, machine vision generates and detects light in order to provide imaging-based automatic inspections and analysis for such applications as automatic object and environmental inspection, process control, and robot/mobile machine guidance in industry. Machine vision is less efficient without optoelectronics, and thus, it is important to investigate the theoretical approaches to different optoelectronic devices available for machine vision as well as current scanning technologies. Examining Optoelectronics in Machine Vision and Applications in Industry 4.0 focuses on the examination of emerging technologies for the design, fabrication, and implementation of optoelectronic sensors, devices, and systems in a machine vision approach to support industrial, commercial, and scientific applications. The book covers topics such as the design, fabrication, and implementation of sensors and devices as well as the development viewpoint of optoelectronic systems and artificial vision techniques using optoelectronic devices. The interaction and informational communication between all these mentioned devices in the complex solution of the same task is the subject of modern challenges in Industry 4.0. Thus, this book supports engineers, technology developers, academicians, researchers, and students who seek machine vision techniques for detection, measurement, and 3D reconstruction.

A discussion of applications of machine vision technology in the semiconductor, electronic, automotive, wood, food, pharmaceutical, printing, and container industries. It describes systems that enable projects to move forward swiftly and efficiently, and focuses on the nuances of the engineering and system integration of machine vision technology.

"This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications. Industry 4.0, the fourth industrial revolution, combines traditional manufacturing with automation and data exchange. Machine vision is used in industry for reliable product inspections, quality control, and data capture solutions. It combines different technologies to provide important information from the acquisition and analysis of images for robot-based inspection and guidance"--