Machine vision improves safety, productivity for food and beverage manufacturers
Machine vision can help food and beverage manufacturers improve manufacturing quality and performance by eliminating defects, verifying assembly and tracking, and capturing information at every stage of the production process
Food and beverage manufacturers face increasing challenges in their efforts to deliver a safe and high-quality product to consumers. Increasing production rates and higher levels of automation increase the challenges of assembly verification and packaging inspection. Mislabeled allergens can in the worst case lead to an injured customer and frequently result in expensive product recalls.
Food and beverage manufacturers are also serving an increased number of niche markets and regional markets, so they often have hundreds of different labels and need to ensure the correct label is affixed to every product in the correct position and orientation to avoid considerable expenses involved in remaking and reshipping the product. Increasing levels of complexity and automation are further increasing the challenges of diverting packages to the correct location within the processing plant.
Machine vision can help food and beverage manufacturers improve manufacturing quality and performance by eliminating defects, verifying assembly and tracking, and capturing information at every stage of the production process. Vision systems can confirm that the product matches its label, that the label is in the right position and correctly oriented, that the safety ring is present, that the cap is correctly positioned and tightened on the label, etc.
Machine vision can be used to track product quality such as ensuring that ingredients are uniformly dispersed, ensuring liquids in bottles are consistent in color, checking fill level, etc. Machine vision can also be used to divert products to the correct area of the plant for processing or shipping, divert flawed products off the production line before further value is added to them, and trace and track products throughout the manufacturing process and supply chain.
Avoiding labeling mix-ups, recalls
Kraft Foods Canada is taking increasing care to avoid labeling mix-ups that sometimes lead to expensive recalls and potential liability concerns. The barbeque sauce product line at the Saint-Laurent plant produces 30 different SKUs at a rate of up to 265 bottles per minute. Ensuring that each individual package has the correct label is critical because some of the products have ingredients such as mustard and egg that certain customers may be allergic to. When the line is changed over to produce a different SKU number, the proper labels are manually loaded into the filling machine.
However, the possibility exists that the person operating the machine might load the wrong labels or that a few wrong labels might be accidentally mixed in with the correct labels. To address this concern, Kraft originally used laser-based ID scanners to read the 1-D code on each label as it passed by on the line and send the results to the PLC that runs the machine. The PLC compared the code to the proper value, and if the code was wrong, the package was ejected from the line.
The problem with the laser-based scanners is that they are only capable of reading codes located within a small field of view. The label design is market driven, so codes may be positioned at any position depending on the designer’s decision. As a result, when the labels are changed, the code may be in a different position. This required that the position of the laser scanners be adjusted whenever the product line changed to a new SKU number, taking a considerable amount of the technical staff’s time. Yet even when the laser scanners were positioned perfectly, they still often failed to read the code.
“I suggested to Kraft that they consider image-based code reading technology,” said Mike Palmieri, senior technical sales representative for Cadence Automation, a machine vision integrator located in Ste-Thérèse, Québec. The basic idea behind image-based technology is that the reader captures an image and uses a series of algorithms to process the image to make it easier to read. A typical algorithm searches the entire image for the code and identifies the position and orientation of the code for easy reading. Other algorithms handle degradations in code quality due to differences in material types and surfaces.
Dave Fortin, a technician for Kraft Foods Canada, started by replacing a laser scanner with a Cognex ID reader in one of the barcode reading positions on the barbeque sauce line. From the moment it was installed, the image-based reader virtually eliminated read failures, providing 99.9%+ read rates. Kraft made the decision to replace the three other ID readers on the barbeque sauce line with ID readers. Four ID readers are required on the line because it has four spurs.
Read performance has continued to be outstanding with better than 99.9% accuracy. No adjustment is required, so the technical staff has been freed from the need to adjust the position of the reader.
“ID readers have significantly improved the efficiency of packaging lines at Kraft Foods Canada,” Fortin concluded. “In the past our technical team had to spend a considerable amount of time adjusting ID readers on various packaging lines. The production staff also had to spend time dealing with the many bottles with good labels that the laser scanner ID readers were not able to read. The new image-based ID readers have solved these problems by providing near-perfect read rates. They are also economical to purchase and easy to maintain.”
Annual Salary Survey
After almost a decade of uncertainty, the confidence of plant floor managers is soaring. Even with a number of challenges and while implementing new technologies, there is a renewed sense of optimism among plant managers about their business and their future.
The respondents to the 2014 Plant Engineering Salary Survey come from throughout the U.S. and serve a variety of industries, but they are uniform in their optimism about manufacturing. This year’s survey found 79% consider manufacturing a secure career. That’s up from 75% in 2013 and significantly higher than the 63% figure when Plant Engineering first started asking that question a decade ago.