Machine vision is an integral part of today’s smart factory. And with advancements in AI and ML, it’s changing the way manufacturers work. Article By Keyence.
Machine Vision has become a vital component across a broad range of industries, from smart cities to process and factory automation. And with deep learning and Machine Learning (ML) embedded in these platforms the possibilities are endless.
As smart manufacturing takes root and industry 4.0 becomes a reality, machine vision will play an increasingly prominent role in industry. And with AI, Cloud, and technologies such as 5G, the manufacturing sector looks set to transform in the coming years.
However, while automation is an important application area for machine vision, it also has a role in areas where there is a high level of manual involvement.
1.Aiding Manual Assembly
The capabilities of manufacturers continue to increase through the use of machine vision. One area where the technology is showing promise is in aiding manual assembly.
Under normal operations, manufacturer’s manual inspect products for quality related issues, with another individual checking for deficits. If a product is picked up as defective, it is usually picked up by the manufacturer’s quality check. If a defective product is missed, then it makes its way to the end-user and ultimately returns to the vendor.
This can result in a bad reputation for the manufacturer impacting their bottomline. One solution to reduce quality issues and ultimately safeguard the manufacturer’s reputation and bottomline is to utilise machine vision in the inspection process.
To implement a machine vision solution a set of assembly instructions are loaded into the camera, which the operator can then follow from a monitor.
When the operator goes through the assembly their actions are compared with the embedded set of instructions. If the operator’s actions are different from the embedded image then the operator is informed and errors are reduced, saving time and money for the company.
2. Manual Assembly Process Integration
While the previous example works well for a single product, the process itself can be extended to encompass a firm’s entire control system.
This would result in a more robust system that could be used to assist the manufacturer in assembly while ultimately using the same principle as the previous use case mentioned.
Manufacturing data could then be requested from a central database allowing them to assembly the product. This would also tie in with efficient and effective training, providing a feedback loop by appointing an operator an ID which is assigned to a particular training competency. This also ensures that an operator that signs into assemble a particular product is qualified and competent enough to complete the task.
3. Automated Inspection
This type of inspection is widespread and can be seen across a broad range of industries. With this, the camera is integrated into the process, which is connected to a reject mechanism.
This is often seen in mass production where products are inspected at high speed and accepted or rejected based on the embedded set of instructions within the inspection device.
In this case, the processing can be done on the inspection device itself, ie: the camera, or it could be part of a larger PC-based system, which often includes many cameras or multiple inspection stations.
When utilising this approach, the system needs to be designed with space and the surrounding environment in mind to ensure optimal performance from the system.
4. Process Control
While employing machine vision in the quality control process can reduce errors, using it alongside statistical process control it has the potential to not just check critical measurements but also identify trends and ultimately course correct the process.
This allows corrections to be made to the process before problems occur in the product. This relates to Industry 4.0, which is concerned about optimising the process and providing feedback from a sensor network and providing an environment that is intelligent and optimised to the goals of the organisation.
Where To Now
Machine vision is used across a range of applications providing imaging-based automatic inspection and analysis for applications such as automatic inspection, process control, and robot guidance.
These methods only provide an overview of some possible applications for machine vision in reducing errors in the production process. In light of this, we can expect significant advancements in the versatility and capability of machine vision as the devices become more intelligent through AI and new methods of information extraction.
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