Applying machine vision in manufacturing involves using advanced cameras and algorithms to allow specialized equipment to detect things humans would otherwise miss. As manufacturers aggressively pursue Industry 4.0 progress, many decision-makers realize their strategies must have machine vision as a foundational element. Here are some fascinating examples of what the technology can do.
Integrate With Various Supporting Technologies
When manufacturers assess technologies to potentially bring into their factories, they often research how well the new products would fit with the high-tech equipment they already have. Fortunately, machine-vision specialists know this and build their items to suit.
Consider the example of a smart workbench recently developed through a collaborative research effort. It includes 2D and 3D machine vision, a mixed reality headset, connected tools, a seven-axis robot and more. One potential application involves a factory worker using the smart workbench for inspections or assemblies and following a step-by-step process. Similarly, they might be in a facility filled with physical objects where they can retrieve digital documents and view them through the headset.
Machine vision comes into play because it can check and confirm positional issues, even on highly complex assemblies. People can also use the workbench with a pick-to-light robotic system, allowing them to leave the assembly tasks to a machine.
Another workbench feature is radio frequency identification capabilities that show which person is using it. Relatedly, the bench height automatically adjusts after determining someone’s identity. These capabilities support security and quality control.
The workbench might alert a supervisor that someone’s trying to assemble something without adequate training. The employee-identification aspect could also help managers learn which workers usually engage in tasks at the workbench and the total time they spend doing so per day or week.
No matter how people decide to use machine vision in manufacturing, they must determine how well the solutions they’ve shortlisted would work with the technologies they already have. Alternatively, they could invest in machine vision options like the one described here, so the chosen product includes machine vision and other advanced innovations.
Combat Labor-Related Challenges
Many manufacturers must get creative as they face worsening labor shortages. Fortunately, machine vision is such a diverse technology that it can allow them to overcome the associated obstacles and restrictions.
In one example, a machine-vision company developed a system that ensures boxed chocolates are in the right tray positions. Manufacturers using pick-and-place robots to handle the candies risk them bouncing out of their compartments or being placed in the wrong spots. It ordinarily takes at least two people to monitor tasks and fix mistakes on a chocolate assembly line.
Machine-vision technology can analyze the texture of an individual chocolate to determine if it’s in the correct place. Similarly, it spots candies that are upside-down in the trays. Once the system picks up on issues, it diverts the respective boxes of chocolate to a separate area where people can address what’s wrong.
This approach prevents production bottlenecks while reducing the number of people required for perfectly presented sweets. Representatives from the company that built this technology also said it would suit other products often sold in trays, such as cookies and cupcakes.
Manufacturers often engage in process analysis to pursue meaningful improvements. Injury reductions and increased consistency are some of the many perks of this approach. They may ultimately find that certain processes are particularly error-prone. If so, machine-vision technology could be an excellent candidate for minimizing problems.
Address Packaging-Related Errors
One of machine vision’s most significant advantages is that it allows specialized equipment to recognize information and act accordingly. It can often stop issues such as mislabeled packages or products in the wrong packaging. That’s particularly important for pharmaceuticals, food and beverages.
After all, those products go into people’s bodies. Purchasers must evaluate the packaging information and choose whether or not to buy it. Ingredient lists and allergen warnings are some of the many things that help people decide if they can safely consume the items. Machine learning supports packaging accuracy.
Getting Great Outcomes at a Snack Manufacturer
Consider one case in the fast-moving consumer goods industry. These products typically sell quickly and at low prices. They’re often perishable items and consumables. One snack manufacturer dealt with various packaging errors and hoped machine vision could tackle some persistent issues.
Once the client chose a solution, trained it and installed the equipment at the factory, the results were notable. First, there was a 95% decrease in the system approving packages with errors. Also, due to an automatic rejection system, the machines operated 55% more efficiently with the machine-vision system. There was also a 50% rise in productivity.
Supporting Pharmaceutical Companies’ Reputations
Patients increasingly want details about how their drugs are manufactured, stored and transferred. Machine vision in manufacturing can serve a dual purpose in pharmaceutical plants because, in addition to containers having labels, pills and capsules also tend to have distinctive characteristics that help people verify they have the correct medication.
Investing in machine vision allows pharmaceutical companies to take an extra step toward safeguarding against safety risks that could lead to recalls. Such technology also supports the tight regulations they must operate under to remain compliant. Plus, it’s usually easy to adapt machine-vision cameras to make them work with new products. That’s another compelling advantage, especially since many companies in this industry often develop and manufacture new products to stay competitive and add to the bottom line.
Machine Vision in Manufacturing Causes a Competitive Advantage
These examples show how leaders have numerous options for using machine vision in manufacturing. An excellent starting point is pinpointing the weaknesses, challenges or other factors that prevent growth or cause preventable problems.
Next, manufacturers should learn about existing products in the market and determine if they meet identified needs. If not, they should get details about building custom solutions. Even if those cost more and take longer to implement, they may be the most appropriate options.
The final thing to remember is that successfully using machine vision in a manufacturing environment takes time, effort and dedication. However, staying focused on the desired goals will retain motivation.
