Thursday 23 June 2022

HOW DEEP LEARNING AUTOMATES PACKAGING SOLUTION INSPECTIONS

Increasingly, packaging products require their own custom inspection systems to perfect quality, eliminate false rejects, improve throughput, and eliminate the risk of a recall. Some of the foundational machine vision applications along a packaging line include verifying that a label on a package is present, correct, straight, and readable. Other simple packaging inspections involve presence, position, quality (no flags, tears, or bubbles), and readability (barcode and date/lot codes present and scannable) on a label.


But packaging like bottles, cans, cases, and boxes—present in many industries, including food and beverage, consumer products, and logistics—can’t always be accurately inspected by traditional machine vision. For applications which present variable, unpredictable defects on confusing surfaces such as those that are highly patterned or suffer from specular glare, manufacturers have typically relied on the flexibility and judgment-based decision-making of human inspectors. Yet human inspectors have some very large tradeoffs for the modern consumer packaged goods industry: they aren’t necessarily scalable.

For applications which resist automation yet demand high quality and throughput, deep learning technology is a flexible tool that application engineers can have confidence in as their packaging needs grow and change. Deep learning technology can handle all different types of packaging surfaces, including paper, glass, plastics, and ceramics, as well as their labels. Be it a specific defect on a printed label or the cutting zone for a piece of packaging, Cognex Deep Learning can identify all of these regions of interest simply by learning the varying appearance of the targeted zone. Using an array of tools, Cognex Deep Learning can then locate and count complex objects or features, detect anomalies, and classify said objects or even entire scenes. And last but not least, it can recognize and verify alphanumeric characters using a pre-trained font library.

Here, we are going to explore how Cognex Deep Learning does all of the above for packagers and manufacturers.

PACKAGING DEFECT DETECTION

Machine vision is invaluable to packaging inspections on bottles and cans. In fact, in most factories, it is machine vision which not only inspects the placement of labels and wrapping but also places and aligns them during manufacturing.

Labeling defects are well-handled by traditional machine vision, which can capably detect wrinkles, rips, tears, warpage, bubbles, and printing errors. High-contrast imaging and surface extraction technology can capture defects, even when they occur on curved surfaces and under poor lighting conditions. Yet the metal surface of a typical aluminum can might confuse traditional machine vision with its glare as well as the unpredictable, variable nature of its defects, not all of which need to be rejected. Add to those challenging surface inspections countless forms and types of defects—for example, long scratches and shallow dents—and it quickly becomes untenable to explicitly search for all types of potential defects.

Using a novel deep learning-based approach, it’s possible to precisely and repetitively inspect all sorts of challenging metal packaging surfaces. With Cognex Deep Learning, rather than explicitly program an inspection, the deep learning algorithm trains itself on a set of known “good” samples to create its reference models. Once this training phase is complete, the inspection is ready to start. Cognex Deep Learning can identify and report all defective areas on the can’s surface which deviate outside the range of a normal acceptable appearance.

PACKAGING OPTICAL CHARACTER RECOGNITION

Hiding somewhere on almost all consumable packages, regardless of material or type, lies a date/lot code. Having these codes printed cleanly and readable is important not only for end-users and consumers doing their shopping but also for manufacturers during the verification stage. A misprinted, smeared, or deformed date/lot code printed onto a label on a bottle or package of cookies, for example, causes problems for both.

Typically, traditional machine vision could easily recognize and/or verify that codes are readable and correct before they leave the facility, but certain challenging surfaces make this too difficult. In these cases, a smeared or slanted code printed on specular material like a metal soda case could be read with some effort by a human inspector but not with much reliability by a machine vision inspection system. In these cases, packagers need an inspection system that can judge readability by human standards but, critically, with the speed and robustness of a computerized system. Enter, deep learning.

Cognex's deep learning OCR tool is able to detect and read the plain text in date/lot codes, verifying that their chains of numbers and letters are correct even when they are badly deformed, skewed, or—in the case of metal surfaces—poorly etched. The tool minimizes training because it leverages a pre-trained font library. This means that Cognex Deep Learning can read most alphanumeric text out-of-the-box, without programming. Training is limited to specific application requirements to recognize surface details or retrain on missed characters. All of these advantages help ease and speed implementation and contribute to successful OCR and OCV application results without the involvement of a vision expert.

PACKAGING ASSEMBLY VERIFICATION

Visually dependent assembly verification can be challenging for multi-pack goods which may have purposeful variation, as in the case of holiday-themed or seasonal offerings. These packs showcase different items and configurations in the same case or box.

For these sorts of inspections, manufacturers need highly flexible inspection systems which can locate and verify that individual items are present and correct, arranged in the proper configuration, and match their external packaging. To do this, the inspection system needs to be able to locate and segment several regions of interest within a single image, possibly in multiple configurations that can be inspected line-by-line to account for variations in packaging.

To locate individual items by their unique and varying identifiable characteristics, a deep learning-based system is ideal because it generalize each item’s distinguishable characteristics based on size, shape, color, and surface features. The Cognex Deep Learning software can be trained quickly to build an entire database of items. Then, the inspection can proceed by region, whether by quadrant or line-by-line, to verify that the package has been assembled correctly.

PACKAGING CLASSIFICATION

Kitting inspections require multiple capabilities of its automated inspection system. Consumer product multi-packs need to be inspected for the right number and type of inclusions before being shipped. Counting and identification are well-loved strengths of traditional machine vision. But to ensure that the right items are included in a multi-part unit requires classifying included products by category—for example, does a sunblock multi-pack contain two types of sunblock, or does it contain an extra sunblock lip balm?

This categorization is important yet remains out of reach for traditional machine vision. Luckily, Cognex's deep learning classification tool can easily be combined with traditional location and counting machine vision tools, or with deep learning-based location and counting tools if the kitting inspection deals with variable product types and requires artificial intelligence to distinguish the generalizing features of these types.

Deep learning-based classification works by separating different classes based on a collection of labelled images and identifies products based on these packaging discrepancies. If any of the classes are trained as containing anomalies, then the system can learn to classify them as acceptable or unacceptable.

New deep learning-enabled vision systems differ from traditional machine vision because they are essentially self-learning and trained on labeled sample images without explicit application development. These systems can also be trained on new images for new inspections at any time, which makes it a valuable long-term asset for growing businesses.

Deep learning-based software is also quick to deploy and uses human-like intelligence which is able to appreciate nuances like deviation and variation and outperform even the best quality inspectors at making reliably correct judgments. Most importantly, however, is that it is able to solve more complex, previously un-programmable automation challenges.

Manufacturers in the packaging industry are increasingly demanding faster, more powerful machine vision systems, and for good reason: they are expected to make a great number of products at a higher quality threshold and for less cost. Cognex is meeting customers’ rigorous requirements head-on by offering automated inspection systems that marry the power of machine vision with deep learning in order to manufacture packaging more cost effectively and robustly.

TO KNOW MORE ABOUT MACHINE VISION DEALER INDIA FOR PACKAGING SOLUTIONS CONTACT MENZEL VISION AND ROBOTICS PVT LTD OR CONTACT US AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM

Wednesday 1 June 2022

3D VISION SOLUTIONS FOR FOOD AND BEVERAGE APPLICATIONS

 In the food and beverage industry, packaging quality verification protects a brand’s image and prevents product spoilage, but the system requires precision. The following three vignettes highlight food and beverage inspection challenges and how they are solved with the right machine vision solution.

CAN INSPECTION

Inspecting aluminum cans for missing or damaged features must be done quickly to prevent bottlenecks. A small dent in a can or a tab lifted by just two degrees can result in a failure. Defects this small are difficult or impossible to identify with 2D imaging systems. However, moving to a 3D solution may disrupt production and require retraining workers. Every second counts in high-speed applications, where a few seconds may represent hundreds or thousands of products.


To achieve higher levels of detection without disrupting production or implementing long training programs, Cognex developed the In-Sight 3D-L4000 vision system. This high-performance smart camera delivers the best-quality patented laser imaging. It can detect:


  • Blobs or volumes.
  • Edges.
  • Surface angles.
  • Step heights, etc.

With both 2D capabilities and true 3D vision, the In-Sight 3D-L4000 can simultaneously run both a 2D and 3D inspection of the part. The In-Sight 3D-L4000 is available in three models to meet specific requirements for a range of applications, such as can, packaging, and product inspection.


FINAL PRODUCT INSPECTION

Food comes in many shapes and sizes. Identifying different candies, verifying frostings and decorations are correct and determining whether a finished product will fit inside its packaging are all complex tasks for automated imaging systems. To keep up with industry, 3D solutions and easy-to-use software work together to make the food and beverage industry even sweeter.

Brand image is important. If a customer sees a missing cookie, broken cereal bars, or cupcake frosting smashed into the lid of a container, they may not purchase the product, and instead associate the brand with poor quality. To verify a product’s quality, 3D vision solutions are needed. Tasks include:


  • Detecting defects.
  • Identifying parts – e.g., cookie versus frosting.
  • Verifying heights.
  • Ensuring proper volume.
  • Checking flatness.
  • Verifying presence and absence of components.

High-quality optics and smart cameras are needed to accurately detect features and to determine volumes. The In-Sight 3D-L4000 provides the performance needed to guarantee product quality. However, the biggest challenges may not be solved with the camera alone. The next application demonstrates the need for software interfaces that are easy to set up, operate and maintain.

CEREAL BAR INSPECTION

For a cereal bar application, the key to success is finding software that works easily and effectively without extended training or third-party technicians. Intuitive In-Sight software allows in-house technicians to quickly set up tools. Then the software handles the rest — determining every pixel above and below the set plane with linear measurements and highlighting features in a simple interface to communicate results clearly to every user.

Many production line workers are already familiar with In-Sight’s spreadsheet programming paradigm, while new operators can learn the system in minutes. In one example, 50 new users were trained on the 3D In-Sight program in less than three hours. Additionally, the In-Sight 3D-L4000 high-performance smart camera detects products that have been rotated or tilted. Its high-quality 2K resolution, with up to 4 kHz scan rate and patented speckle-free blue laser optics, provide a range of machine vision solutions with fast, accurate, and repeatable results.


The In-Sight 3D-L4000’s unique blue-laser optical design has several benefits including:

  • 2M eye safe operation.
  • More light delivered to the surface than competing solutions.
  • Accurate 3D point clouds for measurements.
  • Capability to capture a scan even with a percentage of the laser is blocked by debris.

This last feature is an unprecedented achievement in 3D laser scanning, made possible by the patented speckle-free laser optics. Most laser scanning applications limit the designer’s option to mount the scanner upside down because of concerns about debris blocking the laser light.

A HEALTHIER TECHNOLOGY

When it comes to the food and beverage industry, inspection can be the difference between success or millions in lost revenue. Having less product in a container than advertised can damage a brand’s image, and too much product may cause packaging errors downstream. Challenges increase as food production lines become faster, more automated, and more dynamic.

Even a simple packaging or product line may require advanced solutions for inspecting various volumes, surfaces, and features. With easy-to-use spreadsheets for effective communication, the In-Sight 3D-L4000 delivers accurate data for inspection and keeps the food and beverage lines moving.


TO KNOW MORE ABOUT MACHINE VISION SYSTEM PRODUCT DEALER IN MUMBAI INDIA CONTACT MENZEL VISION AND ROBOTICS PVT LTD CONTACT US AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM