STEMMER IMAGING

In the manufacturing sector, there are huge numbers of products that are assembled manually, relying on the skill of the operator to ‘get it right’. These products are often visually inspected by another member of staff as part of the QC process. There are two outcomes for any faulty product/components that are produced: they are either identified at the QC stage and rejected, or they find their way through to the end customer, where they are likely to be returned as sub-standard.

Either way, unless the product can be re-worked there could be a lot of waste and a potential cloud over the reputation of the manufacturer. Even if the rejected component can be reworked, this incurs additional costs for the manufacturer.

The approach outlined above is highly effective in ensuring the correct manual assembly of a product, but is essentially a stand-alone system. It is possible to take this a step further by integrating this type of manual assembly process into a company’s overall control system.

This would allow a more sophisticated vision system to be used to assist with the manual assembly, offering a greater range of measurement and inspection tools, while using the same principle of highlighting any assembly errors on the display monitor. Assembly instructions and manufacturing data could then be downloaded to the system from a central database as required.

This approach would also allow various safeguards to be introduced such as linking an operator ID to training competency so that the system could check whether an operator logging in to begin a particular assembly was trained for that product.

Similarly all inspection data including images could be transferred back to the database to provide a complete audit trail for every component assembled. The availability of more sophisticated vision tools also allows the system to accommodate new requirements as new products are brought on stream.

Automated inspection systems are used in QC applications in an enormous range of industries and processes. Whilst configurations can vary enormously, the basic premise is that the vision system is integrated into the process, where it is linked to a reject mechanism.

Products or components are inspected, often at high speed, and accepted or rejected on the basis of the measurements made. Vision systems can vary from a single-point self-contained smart camera, where all of the processing and measurement is carried out in the camera itself and a pass/fail result sent back to the reject mechanism, to PC-based systems that may feature multiple cameras and/or multiple inspection stations.

Key to the success of this approach is the ability to integrate the vision system into the process, taking into account space and other environmental considerations. Vision systems can be retrofitted into existing processes, designed from the outset into new ones, and with the emergence of embedded vision systems, are increasingly being incorporated into OEM equipment.

The use of automated vision as a QC tool significantly reduces the possibility of ‘out of spec’ product reaching an end user, but by using it in conjunction with statistical process control and feedback methods it can not only check critical measurements but also analyse trends in these measurements and make changes to the process.

In this way, interventions can be made to adjust the process before any out-of-tolerance product is produced. There is therefore a logical extension from this into Industry 4.0 where the objectives are to optimize the process using big data analytics based on the feedback from many different types of sensors that are monitoring the process. These, of course, will include simple and smart vision sensors as well as more sophisticated vision subsystems or systems.

Measurements fall into 3 categories: 1D, 2D and 3D.

• 1D measurements are typically used to obtain the positions, distances, or angles of edges.

• 2D measurements provide a host of measurements including area, shape, perimeter, centre of gravity, the quality of surface appearance, edge based measurements and the presence and location of features. Pattern matching of an object against a template is also an important part of the 2D armoury. Reading and checking of characters and text, and decoding 1D or 2D codes is another key activity.

• 3D measurement methods provide add height information, allowing the measurement of volume, shape, and surface quality such as indentations, scratches and dents as well as 3D shape matching. Materials produced in continuous rolls (web) or sheet, such as paper, textiles, film, foil, plastics, metals, glass, or coatings are generally inspected using continuous line scan vision systems to detect and identify defects.

Vision plays an important role in end of line inspection by reading unique identifiers in the form of 1D or 2D codes, alphanumerics or even braille for tracking and tracing applications in industries as diverse as aerospace, automotive, food, healthcare and pharmaceutical. Human readable on-pack data, such as batch, lot numbers, best before or expiry dates are also critical for products such as food, pharmaceutical, medical devices and cosmetics.

Machine vision technology encompasses all of the component parts of a machine vision system such as cameras, optics, lenses, frame grabbers, computers, software, cables etc. Most important is the expertise to be able to select the most appropriate components and create a solution for the specific application.

As one of Europe’s leading machine vision technology providers, STEMMER IMAGING offers world-leading machine vision products, extensive knowledge and experience to its customers. In this way, tailored solutions can be delivered, from configured components to vertical application sub-systems for systems integrators or the development of customer-specific solutions for OEMs.

This is increasingly important when considering the development of vision systems embedded into other equipment and manufacturing processes. Many of the leading machine vision libraries and toolkits can now be ported to small, embedded processing boards, usually based on ARM architecture, offering a lower cost for higher volume applications.

Combining these processing capabilities with low cost cameras, including board level cameras, means that vision systems could be incorporated into a wide variety of products and processes with comparatively small cost overheads which were previously not viable.

In addition the exploitation of deep learning and machine learning techniques in vision applications are opening up more possibilities for organic ad varying products which can also run on inexpensive embedded systems, making extremely cost-effective systems possible.