Machine vision is a popular tool of the manufacturing industry. Coupled with AI, it has a wide range of use cases. For instance, it can perform an automated visual inspection on objects that are manufactured in thousands every day. Machine vision uses deep learning-based training algorithms to identify and understand the requirements of any particular task assigned to it. These systems are easy to train and implement. They are also reliable, robust, and stable. Machine vision systems are low-cost and high-accuracy devices that can withstand the mechanical and thermal stresses of the industry. Deployment and maintenance of machine vision systems, therefore, is simple. When done regularly, it is a short and quick process.
Deployment of a machine vision system
- Installation of the different modules – Installation of the optical system, feeding system, processor, and output system to complete the cycle of communication is done. Sometimes advanced data storage and image analysis tools are added based on the requirements. Light settings are also done appropriately for best performance.
- Customization of the algorithm – The software installed in the system is customized according to the needs of the manufacturer to fulfill the business demands. Data is entered into the system for training. Analysis of the data prepares the system for performing in the industrial environment.
- Inspection of equipment – The machine vision model performs the task as per the configuration. There is a multitude of tasks that are performed, like recognition of shapes, detection of objects, pattern matching, measurement, and calibration.
- Improvement of performance – Machine vision can improve as it is exposed to more and more data. It has a continuous feedback mechanism that is a part of the algorithm. This enables the model to learn as it is working. That ability minimizes the intervention from humans.
What are some critical conditions that need to be maintained for high performance?
- Choose the right light – The correct light and the correct color and the correct illumination technique are three factors that affect the image capturing conditions of the machine vision system. Depending on your requirements you can increase or reduce contrast. If the lighting conditions are not correct, your entire project might perform poorly.
- Check software compatibility – New software keeps getting added to the machine vision system as per the requirements of the task to be performed. You must check the compatibility of new software programs with your original algorithm for smooth functioning.
- Plan for the future – Before you begin making major modifications to your machine vision model, pave the path for easy re-programming and modeling which might be required in the future.
- Use versatile equipment – Whenever you begin modeling your machine vision system or modifying it, consider hardware that is versatile and can be used in different ways. The ability to program the parts will be worth the additional cost of purchasing those parts.
- Match the environment – The hardware and software and the environment must be complementary to one another. For example, the optics should match a desired field of view. Robust illumination must be put in place. Reliable software libraries must be installed and tested so that complexity is kept at bay.
- Be prepared for upgrades – Maintenance of a machine vision system means preparing the system for continuous upgrades and modifications as new and better technology comes into the market. Upgrades improve the efficiency and accuracy of your system without slowing them down.
Machine vision maintenance needs –
- Maintenance as a training tool –
There are two types of maintenance, preventative and repairs. This type of maintenance is limited to hardware. Hardware failures are rare in machine vision systems. The issues come from material handling systems, which sometimes require reprogramming. Most of the equipment used in machine vision is solid state. This means there are very few moving parts and thus few points of failure. Cameras are workable for eight to ten years after they have been manufactured. Any hardware equipment that passes the tests of manufacturing can easily last a long time. User errors may cause equipment malfunctioning, but sensitive electronics are usually out of the reach of the user.
- Repurposing and reprogramming –
The system can last longer than the product, which means changes in designs, packaging, and the product itself needs to be reprogrammed into the algorithm. A sampling-based quality control technology can help manage this sort of maintenance. A confidence score is assigned to the machine learning algorithm from what is called a “golden set”. It consists of all kinds of defects and their handling. From this set, a baseline confidence meter is formulated. Whenever a machine vision system verifies a product, it assigns itself a confidence score. Using that confidence score, a human operator can certify if the algorithm performed the task correctly. When machine vision systems start making mistakes, it is time to reprogram them.
- Documentation updates –
As a part of the maintenance services to customers, the documentation and user manuals need to be constantly updated to serve customers. These manuals act as a guide for the users to configure and set up the machine vision model. Any changes to software or hardware should be updated immediately to keep the user informed about anything important regarding the machine vision system.
Thus, maintenance of machine vision systems is not a tedious job, but it must be done periodically to prevent the system and the user from getting overwhelmed with updates. A well-performing machine vision model is marked by frequent upgrades, constant confidence score checks, and up-to-date user guides. While making any changes to the machine vision system, you should keep in mind any possible future changes that can be handled better by adding extra modifications at the present moment. This helps in smoother transitions, minimal downtime, and occasional changes without compromising the efficiency and effectiveness of the machine vision system.