Tag Archives: Industry 4.0

Megan Nichols
FST Soapbox

Machine Vision Training Tips to Improve Food Inspections

By Megan Ray Nichols
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Megan Nichols

As machines become more intelligent, every industry on earth will find abundant new applications and ways to benefit. For the food industry, which has an incredible number of moving parts and is especially risk-averse, machine vision and machine learning are especially valuable additions to the supply chain.

The following is a look at what machine vision is, how it can play a role in manufacturing and distributing foods and beverages, and how employers can train workers to get the most out of this exciting technology.

What Is Machine Vision?

Machine vision isn’t a brand-new concept. Cameras and barcode readers with machine vision have long been capable of reading barcodes and QR codes and verifying that products have correct labels. Modern machine vision takes the concept to new levels of usefulness.

Barcodes and product identifiers have a limited set of known configurations, which makes it relatively straightforward to program an automated inspection station to recognize, sort or reject products as necessary. Instead, true machine vision means handlers don’t have to account for every potential eventuality. Machine vision instead learns over time, based on known parameters, to differentiate between degrees of product damage.

Consider the problem of appraising an apple for its salability. Is it bruised or discolored? Machine vision recognizes that no two bruises look precisely alike. There’s also the matter of identifying different degrees of packaging damage. To tackle these problems, it’s not possible to program machine vision to recognize a fixed set of visual clues. Instead, its programming must interpret its surroundings and make a judgment about what it sees.

Apples, machine vision
On an apple, no two bruises are alike. Machine vision technology can help. Photo credit: Pexels.

The neural networks that power machine vision have a wide range of applications, including improving pathfinding abilities for robots. In this article, I’ll focus on how to leverage machine vision to improve the quality of edible products and the profitability of the food and beverage industry.

Applications for Machine Vision in the Food Industry

There are lots of ways to apply machine vision to a food processing environment, with new variations on the technology cropping up regularly. The following is a rundown on how different kinds of machine vision systems serve different functions in the food and beverage sector.

1. Frame Grabbing and 3-D Machine Vision
Machine vision systems require optimal lighting to carry out successful inspections. If part of the scanning environment lies in shadow, undesirable products might find their way onto shelves and into customers’ homes.

Food products sometimes have unique needs when it comes to carrying out visual inspections. It’s difficult or impossible for fallible human eyeballs to perform detailed scans of thousands of peas or nuts as they pass over a conveyor belt. 3-D machine vision offers a tool called “frame grabbing,” which takes stills of — potentially — tens of thousands of tiny, moving products at once to find flaws and perform sorting.

2. Automated Sorting for Large Product Batches
Machine vision inspection systems can easily become part of a much larger automation effort. Automation is a welcome addition to the food and beverage sector, translating into improved worker safety and efficiency and better quality control across the enterprise.

Inspection stations with machine vision cameras can scan single products or whole batches of products to detect flaws. But physically separating these products must be just as efficient a process as identifying them. For this reason, machine vision is an ideal companion to compressed air systems and others, which can carefully blow away and remove even a single grain of rice from a larger batch in preparation.

3. Near-Infrared Cameras
Machine vision takes many forms, including barcode and QR code readers. A newer technology, called near-infrared (NIR) cameras, is already substantially improving the usefulness and capabilities of machine vision.

Remember that bruised apple? Sometimes physical damage to fruits and vegetables doesn’t immediately appear on the outside. NIR technology expands the light spectrum cameras can observe, giving them the ability to detect interior damage before it shows up on the exterior. It represents a distinct advantage over previous-generation technology and human inspectors, both of which can leave flaws undiscovered.

Tips on Training Workers to Use Machine Vision

Implementing machine vision into a productive environment delivers major benefits, but it also comes with a potentially disruptive learning curve. The following are some ideas on how to navigate it.

1. Take Advantage of Third-Party Training Courses
Don’t expect employees to hit the ground running with machine vision if they’re not familiar with the fundamentals of how it works. Google has a crash course on machine learning, and Amazon offers a curriculum as well to help companies get their employees up to speed on the technology and how to use it.

2. Get the Lighting Right
Having the appropriate intensity of light shining on the food product is essential for the machine vision cameras to get a clear photo or video. The most common types of lighting for machine vision are quartz halogen, LEDs, metal halide and xenon lights. Metal halide and xenon are better for larger-scale operations because of their brightness.

Train employees to check the amount and positioning of the lighting before each inspection station starts up for the day, so that no shadows obscure products from view.

3. Single Out Promising Subject Matter Leaders
Companies today don’t seem to have much confidence in how well they’re preparing their workforce for tomorrow, including future innovations. According to Deloitte, just 47% of companies in the world believe they’re doing enough to train their employees on the technologies and opportunities of Industry 4.0.

Machine vision does not involve buying a camera or two, setting them up, then slapping the “autopilot” button. As products turn over, and manufacturing and distribution environments change and grow over time, machine vision algorithms require re-training, and you might need to redesign the lighting setup.

Employers should find individuals from their ranks who show interest and aptitude in this technology and then invest in them as subject matter experts and process owners. Even if an outside vendor is the one providing libraries of algorithms and ultimately coming up with machine vision designs, every company needs a knowledgeable liaison who can align company needs with the products on the market.

Machine Vision Is the Future of Food Inspections

The market for machine vision technology is likely to reach $30.8 in value by 2021, according to BCC Research.

It is important to remember that neither machine learning nor machine vision are about creating hardware that thinks and sees like humans do. With the right approach, these systems can roundly outperform human employees.

But first, companies need to recognize the opportunities. Then, they must match the available products to their unsolved problems and make sure their culture supports ongoing learning and the discovery of new aptitudes. Machine vision might be superior to human eyesight, but it uses decidedly human judgments as it goes about its work.

Brian Sharp, SafetyChain Software
FST Soapbox

How Industry 4.0 Affects Food Safety and Quality Management

By Brian Sharp
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Brian Sharp, SafetyChain Software

The food and beverage industry is moving towards a fully connected production system with more methods available to automate data collection than ever before. But with all the promises of Industry 4.0, what are the true capabilities of communicating real-time plant floor insights? This article will explain how better capturing methods and analysis can drive data-driven decision making to optimize safety, quality and efficiency in food and beverage operations.

What Is Industry 4.0?

The term Industry 4.0 has many pseudonyms, such as Industrial Internet of Things, Manufacturing 4.0, and Smart Manufacturing, but they generally all refer to the idea that manufacturers will be able to connect all operations in their plants. Where the name Industry 4.0 comes into play is the thought that manufacturing is in its fourth wave of change. In the 1780s, the first industrial revolution started with machines and the “production line” and evolved to mass production in the 1870s; manufacturing entered into a new wave after the 1950s when automation was introduced.

In this current fourth wave of manufacturing, new technology is driving the change in production and the capabilities of what can be accomplished in facilities. A report from Deloitte Insights entitled “The Smart Factory” explains this new way of operations as “ a leap forward from more traditional automation to a fully connected and flexible system—one that can use a constant stream of data from connected operations and production systems to learn and adapt to new demands.”

By way of more sensors, connectivity, analytics, and breakthroughs in robotics and artificial intelligence, the future food and beverage plants will be able to meet customers’ demands for higher-quality products while increasing productivity. However, there is a stark reality that many food and beverage manufacturing facilities are over 50 years old and dealing with legacy equipment. And if an investment in new technology is made, often it is made because food and beverage plants need to reach compliance or fill a customer’s requirement.

“Regulatory compliance is huge,” says Steve Hartley of Matrix Control Systems during a recent SafetyChain webinar. “But if you are able to attach additional business value to that compliance, then incorporating technology into the organization becomes a lot easier.”

For instance, new technology that can help a facility follow regulated processes in food manufacturing can also help to create more consistency and increase the quality of your products. Additionally, if input from the entire organization is collected when investing in more technology and automation, then multiple departments will support the budget costs.

“One of the big things that we see happening with our customers is that they are digging into that production equipment,” says Hartley. “Lots of food manufacturing facilities are filled with all sorts of wonderful processing equipment, but leveraging not only the manufacturing capabilities, but also the data collection capabilities of that equipment is really powerful.”

What Automated Data collection Systems Can Do

Because large food and beverage companies sell a high volume of goods to a large number of customers, many have already automated their data collection. These facilities also receive goods from an intricate supply chain that spans vast distribution networks, thus making automated data collection from receiving all the way through shipping a necessity.

However, many companies are going beyond this and integrating production equipment on the plant floor to provide a deeper level of production and quality data. These types of operations are generally interested in going beyond just being in regulatory compliance, but working on their continuous improvement. What this data can do is to provide better data for better decision making. By knowing what parts of the plant are operating optimally and what areas aren’t, plant managers can to make changes that will unlock more potential from the production line.

Getting the most out of operations is one of the most frequently cited needs of food and beverage manufacturers. The best way to do this is to drive plant efficiencies, which means measuring performance, setting baselines and goals, and holding employees accountable. The key here is to not confine efficiencies to just one area of the facility, but to broaden the scope to include end-to-end processes, from supplier to customer.

“Take a scope that is relevant to everyone and that is relevant to the strategy of the company,” states Daniel Campos of London Consulting Group. A company’s overall strategy should drive the focus of all departments. No one lives in a silo, and every part of your operations affects all the other parts. So any one area that is falling below the goal set takes away value from the system as a whole. This becomes more crucial as the enterprise grows even more connected and dependent on data from each other.

Shortfalls of Industrial Automation Systems

When evaluating the scope of an operation, all areas of the plant should be assessed in terms of how data is being collected. Part of this information assessment is to learn what processes aren’t covered by automated data collection. This includes equipment without sensors that can record accurate measurements and readings.

Another area that should be identified as an entry point for possible faulty or incorrect data is where an operator is required to input information. Some of this might be simply validating that SOPs were followed, such as whether a piece of equipment was cleaned or not and if detergents were actually changed when required.

The quality and fidelity of the data is directly related to the effectiveness of the decisions made. As the saying goes, “Garbage in, garbage out.” But even good data alone doesn’t drive value, but rather information gleaned from the facts collected is where the true benefits can be harnessed to improve the food safety and quality of products produced.

So, if data is analyzed and found not to conform to a desired specification, then the goal is to find out why this is happening. Is the data being collected accurate? If not, why? If it is accurate, then what else is going on?
Additionally, the speed and complexity of today’s food processing plants requires this data to not just be in real time, but able to be captured in smaller increments to make better decisions. This type of data that is collected and analyzed infrequently can slip through the cracks because systems to collect and manage this category can be hard to find, unlike industrial automation systems.

One solution to this problem can be found in capturing data via mobile devices. Tablets and phones moving through the plant with operators can help collect information at the source. Plus, these devices enable managers and executives to see critical control point data as well as summaries of operational performance and out-of-spec occurrences, anytime and anywhere.

As food and beverage manufacturing plants continue to automate their data collection and increasingly connect their production processes, more data will come online in a multitude of ways, allowing for better decision making. Ultimately, this is the promise of Industry 4.0 and why digital transformation promises a higher level of food safety and quality in the future.