Researchers at Germany’s Fraunhofer Institute for Material and Beam Technology IWS have developed SURFinpro, an AI-assisted inspection system designed specifically for roll-to-roll processing. The system utilizes four cameras and four lasers to capture real-time images of material surfaces, identify minute defects, and accurately mark their locations. In roll-to-roll production lines, this automated inspection system can replace traditional manual checks, thereby improving production efficiency and reducing material waste. The system is developed with the goal of lowering manufacturing costs…

Major Advancements in Imaging Technology Expected in the Automation Field.

Machine vision is expected to see wider adoption across industries, offering efficient solutions for production and manufacturing. Imaging technology is anticipated to become more intelligent by integrating AI and deep learning, enabling higher levels of automation and precise inspection. This evolution will enhance production line efficiency, reduce costs, and bring significant improvements in quality control. Looking ahead, the automation industry will place more emphasis on sustainability and intelligence, driving innovation and application of machine vision technology across various sectors.

Automation technicians, operators, engineers, and managers typically cannot rely solely on the latest trends. While it is important to evaluate promising new technologies, ultimate success lies in practical application and meeting the needs of the process or production environment. In the application of vision and imaging technologies, several key topics deserve attention.

Overcoming Challenges of “Ease of Use” and “No-Code” Solutions:

While the market promotes ease of use and no-code solutions, developing user-friendly vision systems remains a challenge. Obstacles include the complexity of imaging and image acquisition design, as well as the unique requirements of each application. General-purpose solutions often have limited effectiveness in specific use cases, so the key to success lies in focusing on well-defined tasks.

As components become more advanced and complex, 3D imaging has matured from an emerging technology into a standard feature in automated imaging products. With various component options available on the market, 3D imaging is now an expected capability in Vision-Guided Robotics (VGR). Recent trends involve expanding 3D capabilities through application-specific solutions to make 3D VGR easier to use. For example, bin-picking applications are now offered as standalone solutions, while others, such as palletizing and depalletizing, are available as “packaged” systems. This approach to 3D VGR is likely to drive broader adoption of these complementary technologies, although their functions must still align with project requirements.

In automated inspection, artificial intelligence almost exclusively refers to deep learning. As a valuable tool for image segmentation and classification, its key advantage lies in learning through software rather than fixed algorithms—especially useful for recognizing subjective features. However, deep learning is not a universal solution for all machine vision applications. Successful products often use hybrid approaches. Lessons learned from market observations include: deep learning cannot compensate for poor lighting or optical design, it requires high skill levels to implement, and it is not a one-size-fits-all solution when used in isolation.

Overall, the trends in the field of vision and imaging include ease of use, 3D imaging, and deep learning. While these trends may be applied in specific applications, proven and reliable tools remain key to practical implementation and long-term success.

Researchers at Germany’s Fraunhofer Institute for Material and Beam Technology IWS have developed SURFinpro, an AI-assisted inspection system designed specifically for roll-to-roll processing. The system utilizes four cameras and four lasers to capture real-time images of material surfaces, identify minute defects, and accurately mark their locations. In roll-to-roll production lines, this automated inspection system can replace traditional manual checks, thereby improving production efficiency and reducing material waste. The system is developed with the goal of lowering manufacturing costs…

Major Advancements in Imaging Technology Expected in the Automation Field.

Machine vision is expected to see wider adoption across industries, offering efficient solutions for production and manufacturing. Imaging technology is anticipated to become more intelligent by integrating AI and deep learning, enabling higher levels of automation and precise inspection. This evolution will enhance production line efficiency, reduce costs, and bring significant improvements in quality control. Looking ahead, the automation industry will place more emphasis on sustainability and intelligence, driving innovation and application of machine vision technology across various sectors.

Automation technicians, operators, engineers, and managers typically cannot rely solely on the latest trends. While it is important to evaluate promising new technologies, ultimate success lies in practical application and meeting the needs of the process or production environment. In the application of vision and imaging technologies, several key topics deserve attention.

Overcoming Challenges of “Ease of Use” and “No-Code” Solutions:

While the market promotes ease of use and no-code solutions, developing user-friendly vision systems remains a challenge. Obstacles include the complexity of imaging and image acquisition design, as well as the unique requirements of each application. General-purpose solutions often have limited effectiveness in specific use cases, so the key to success lies in focusing on well-defined tasks.

As components become more advanced and complex, 3D imaging has matured from an emerging technology into a standard feature in automated imaging products. With various component options available on the market, 3D imaging is now an expected capability in Vision-Guided Robotics (VGR). Recent trends involve expanding 3D capabilities through application-specific solutions to make 3D VGR easier to use. For example, bin-picking applications are now offered as standalone solutions, while others, such as palletizing and depalletizing, are available as “packaged” systems. This approach to 3D VGR is likely to drive broader adoption of these complementary technologies, although their functions must still align with project requirements.

In automated inspection, artificial intelligence almost exclusively refers to deep learning. As a valuable tool for image segmentation and classification, its key advantage lies in learning through software rather than fixed algorithms—especially useful for recognizing subjective features. However, deep learning is not a universal solution for all machine vision applications. Successful products often use hybrid approaches. Lessons learned from market observations include: deep learning cannot compensate for poor lighting or optical design, it requires high skill levels to implement, and it is not a one-size-fits-all solution when used in isolation.

Overall, the trends in the field of vision and imaging include ease of use, 3D imaging, and deep learning. While these trends may be applied in specific applications, proven and reliable tools remain key to practical implementation and long-term success.

Researchers at Germany’s Fraunhofer Institute for Material and Beam Technology IWS have developed SURFinpro, an AI-assisted inspection system designed specifically for roll-to-roll processing. The system utilizes four cameras and four lasers to capture real-time images of material surfaces, identify minute defects, and accurately mark their locations. In roll-to-roll production lines, this automated inspection system can replace traditional manual checks, thereby improving production efficiency and reducing material waste. The system is developed with the goal of lowering manufacturing costs…

Major Advancements in Imaging Technology Expected in the Automation Field.

Machine vision is expected to see wider adoption across industries, offering efficient solutions for production and manufacturing. Imaging technology is anticipated to become more intelligent by integrating AI and deep learning, enabling higher levels of automation and precise inspection. This evolution will enhance production line efficiency, reduce costs, and bring significant improvements in quality control. Looking ahead, the automation industry will place more emphasis on sustainability and intelligence, driving innovation and application of machine vision technology across various sectors.

Automation technicians, operators, engineers, and managers typically cannot rely solely on the latest trends. While it is important to evaluate promising new technologies, ultimate success lies in practical application and meeting the needs of the process or production environment. In the application of vision and imaging technologies, several key topics deserve attention.

Overcoming Challenges of “Ease of Use” and “No-Code” Solutions:

While the market promotes ease of use and no-code solutions, developing user-friendly vision systems remains a challenge. Obstacles include the complexity of imaging and image acquisition design, as well as the unique requirements of each application. General-purpose solutions often have limited effectiveness in specific use cases, so the key to success lies in focusing on well-defined tasks.

As components become more advanced and complex, 3D imaging has matured from an emerging technology into a standard feature in automated imaging products. With various component options available on the market, 3D imaging is now an expected capability in Vision-Guided Robotics (VGR). Recent trends involve expanding 3D capabilities through application-specific solutions to make 3D VGR easier to use. For example, bin-picking applications are now offered as standalone solutions, while others, such as palletizing and depalletizing, are available as “packaged” systems. This approach to 3D VGR is likely to drive broader adoption of these complementary technologies, although their functions must still align with project requirements.

In automated inspection, artificial intelligence almost exclusively refers to deep learning. As a valuable tool for image segmentation and classification, its key advantage lies in learning through software rather than fixed algorithms—especially useful for recognizing subjective features. However, deep learning is not a universal solution for all machine vision applications. Successful products often use hybrid approaches. Lessons learned from market observations include: deep learning cannot compensate for poor lighting or optical design, it requires high skill levels to implement, and it is not a one-size-fits-all solution when used in isolation.

Overall, the trends in the field of vision and imaging include ease of use, 3D imaging, and deep learning. While these trends may be applied in specific applications, proven and reliable tools remain key to practical implementation and long-term success.