Integration Increases Machine Vision Value Proposition
The global race toward smart manufacturing is driving the use of advanced automation technologies such as machine vision (MV). MV has become a key technology in both manufacturing and quality control; however, MV is rapidly becoming a crucial building block for Industrie 4.0-enabled smart factory infrastructure. MV is an essential element for smart factory infrastructure, due to its characteristics such as efficient communicating network and the intelligent exchange of information among sensors, devices, and machines. MV systems have demonstrated their cost effectiveness in inspection, measurement, scanning, and object detection in manufacturing to improve consistency, productivity, and overall quality.
MV systems provide object recognition capabilities with varying degrees of accuracy and robustness. Lighting, camera resolution, vision algorithms, and workpiece orientation are all factors which impact the accuracy and robustness of an MV solution. However, an underlying limitation is the application programming which is generally developed to handle a limited number of cases when performing a visual inspection or vision guidance application. Today’s MV systems do not have the ability to train when variation in the workpiece is not expected by the application program.
Machine Learning and Machine Vision
Machine learning is now being used to augment MV systems to incorporate adaptability into deployed systems. Integration of machine learning with MV is of growing importance for enabling MV systems to adapt autonomously to manufacturing variations. This increases the value proposition of MV by improving operational efficiencies, accelerating deployment of production processes, and enhancing production optimization.
Integration of machine learning with MV technologies is already demonstrating that it can enhance industrial automation and inspection processes by generating big data. Big data analysis is critical for improving production operations as manufacturers make the transition to the smart connected factory and extend the Industrial Internet of Things ecosystems across factories, plants, and supply chains. Advanced analytics powered by both cloud based and edge based machine learning algorithms can help analyze large volumes of MV production records to identify patterns that differentiate components that pass inspection from those that fail. Machine learning algorithms augment programmed MV systems by updating identification algorithms directly on the vision system without human intervention. These types of closed-loop systems will drive real and actionable continuous process improvement and are beneficial in application areas such as electronics, food & beverage, and automotive where the machine learning algorithms are used to identify minuscule defects invisible to the naked eye and enable automated removal of the defective parts.
Robotics Drives Adoption of MV
As MV systems become more efficient and robust, one of the key growth markets that drives adoption of MV is robotics. The demand for robotics has diversified across a broader range of industries. Robotic solutions address the seemingly insatiable demand in manufacturing today to increase flexibility, improve productivity, and employ human operators only in tasks where they can add the most value. The use of MV systems is widely sought in robotic guidance applications. MV systems are employed in a robot for guidance and object recognition. Single-camera vision guidance solutions are emerging on the market specifically for collaborative robots. As the increasing demand for 3D MV systems at a lower entry point cost, will accelerate the adoption of robotic solutions in a broader range of applications. The demand for robotic solutions is expected to accelerate in the years to come, further proliferating the sale of MV systems during the forecast period.
The new ARC report “Machine Vision Systems” provides strategic market information and includes quantitative assessments and forecasts of MV systems as well as discussions of world regions, revenue categories, industries, applications, sales channels, and customer types.
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