21 minute read
PERSPECTIVES
PLC Programming Language Decisions
By David Greenfield
Director of Content/Editor-in-Chief
Though PLC (programmable logic controller) programming languages may not receive the attention that general computing programming languages, such as JavaScript, C#, or Python do, they remain critical to the manufacturing and processing industries. And while PLC programming languages have not been through as many changes or updates as general computing programming, these languages haven’t been static either.
To check in on the status of PLC programming languages, we spoke with Doug Yerger, principal engineer at Grantek, an industrial automation system integrator, for a recent episode of the “Automation World Gets Your Questions Answered” podcast series.
The demise of Instruction List?
One thing that hasn’t changed recently with PLC programming languages is that there are still two basic types: textual programming using typed out commands and graphical programming where logic sequences are arranged by moving objects around in the programming environment. Beginning our discussion with a focus on the textual languages, Yerger noted that the Instruction List textual PLC programming language is “a very low-level, mnemonicbased language” whose days look to be numbered.
Instruction List has been “deprecated in IEC 61131-3 and will probably not be in the next version of the standard, according to the standard itself,” he said. “I think you’ll see controllers and programming software that support Instruction List now will continue to support it for quite a number of years, but I don’t think you’re going to see anyone put it into their [new] product lines if it’s a deprecated standard.”
This deprecation of Instruction List in the IEC standard is happening because the language is seen as an “outdated, assembler-like language,” Yerger said. “And talking with my peers in our company, no one’s seen Instruction List utilized in any of our projects. We stick to Structured Text and the other languages.”
Despite the grim outlook for Instruction List, Structured Text will be sticking around as it’s a high-level programming language. “If you’re coming from a computer science background, Structured Text is going to be very native fi eld for you. It’s also very good for looping and string manipulation,” said Yerger. “And if you have to parse barcodes, strip out the ASCII characters, and things like that, it’s so much easier to do in a high-level textual language than in [a graphical programming language like] Ladder Diagram, where you’re going to be dealing with each byte individually.”
The graphical programming realm
Explaining the principal di erences among the graphical PLC programming languages, Yerger started with Ladder Diagram, noting its development from relay logic. “It (Ladder Diagram) looks like old school hardware relay logic diagrams. It’s basically all Boolean math and Boolean decisions, for the most part,” he said. Meanwhile, the Function Block language allows programmers to arrange di erent blocks on a graphical screen that represent the inputs and outputs between the controller and the devices connected to it.
Yerger describes the Sequential Function Chart language as being “the oddball of the fi ve [PLC programming] languages. We don’t view it as a language, it’s a structure of the fl ow of the logic, because it allows for branching into parallel processes that are decision points based on conditions. Basically, with Sequential Function Chart, there’s an action item at each block and each action can have a series of events. These events are what happens in each of those action blocks. But the events will probably be programmed in Ladder Diagram from the Structured Text function blocks. Sequential Function Chart controls the fl ow [of operations] by saying, ‘do this and wait until this condition is met.’ It parses down the program fl ow this way by giving directions based on conditions being met. So that’s where it’s a little di erent from the other languages.”
This program fl ow nature of Sequential Function Chart is why it’s so often used. “It’s great for stepped or discrete sequencing. A classic example would be a batching engine where you might have, say, three ingredients added together for a recipe and those ingredients are going to be three parallel branches in the programming language. But you’re not going to turn on the mixer’s agitator until all
three of them are added. So you’ll have this series of steps that need to happen—some parallel, some sequential—where the controller is going to wait for the conditions between them to be met before moving forward. When you get to the agitation step, control of the motors might be done in Ladder Diagram, which will trigger the Sequential Function Charts running in a separate process. When the Sequential Function Chart program sees the trigger of the agitations completed, it’s going to move on to the next action in the sequence.”
In North America, more than 90% of control programming is done in Ladder Diagram, said Yerger. “It’s great for discrete logic systems that use a lot of Boolean algebra and have a lot of limit switches and on/o s,” he said. “It’s also very easy for experienced electricians to understand because they’re coming from the hardware reading (that Ladder Diagram is based on). Newer technicians that have learned C or other highlevel programming languages are probably going to lean toward Structured Text because it will be more familiar to them.”
Function Block programming is preferred for continuous processes where you’re taking an analog input and scaling it, said Yerger. That’s why it’s often used for PID loops. There are also several safety systems that use Function Blocks as their programming method, he added.
The main takeaway when looking at the di erent PLC programming languages is that it’s rare for just one to be used. “Especially if it’s a large program, we’re going to have routines and programs in them that use a variety of languages,” Yerger said. “The majority might be in Ladder Diagram, but if we have a big array processing to do, we’re going to use Structured Text to handle those arrays. We’re not going to stick to just one [language] unless, of course, our customer has that requirement. And there are several customers that do say: Use Ladder only. But we usually try and talk them out of that and move them to a more modern philosophy of using the right tool for the job.”
Beyond IEC 61131-3
The introduction of programmable automation controllers (PACs) for more complex automation tasks expanded the realm of controller languages beyond the IEC 61131-3 languages, allowing for the use of languages more connected with the IT world, such as C and C+.
Yerger said there was a big push several years ago to bring the “C” programming languages to the plant fl oor. But he said that push slowly faded away in favor of the IEC languages. However, he did note that IT programming languages are still prevalent with SCADA systems. “If we need to do high level C, Python processing, or even Java processing, we’re going to take it up to the SCADA level and do it on computer hardware that has the power designed specifi cally for those languages,” he said.
Listen to the full podcast on PLC programming language decisions.
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Small Potato Producer Gets a Grip on Growth via Robotics
By Aaron Hand
Editor-in-Chief, ProFood World
The multi-generational potato farmers of Tasteful Selections began in Nebraska, relocating to Arvin, Calif., to take advantage of the rich farmland and year-round growing season. The journey since has been not only geographical but one of innovation as well. Always searching for business growth opportunities, the company pioneered the category of bite-sized potatoes to o er consumers a simpler and more convenient source of nutritious potatoes.
Tasteful Selections also employs state-of-theart production technology. To maintain high standards of quality, fl avor, freshness, size uniformity, and best practices for sustainability, the company owns the entire planting, growing, harvesting, and packaging process. When it wanted to expand and optimize its potato production, robotic automation was seen as the key.
“We are constantly planting, harvesting, and picking the freshest product for our customers,” says Nathan Bender, chief operating o cer for Tasteful Selections. “We’re growing so fast that, without automation, we can’t keep up.”
Fanuc robots have been a big part of Tasteful Selections’ journey, including 11 Fanuc M-410iB palletizing robots and fi ve Fanuc M-3iA delta robots for case packing. “What we’re trying to do is fl atten our curve of our costs, so we can provide a great service to our customers and keep our pricing down,” says Ernie Waldo, plant manager for Tasteful Selections. “As we’ve rolled out automation, it has been a real blessing to our employees to bring consistency to the line and bring consistency to our costs.”
Tasteful Selections got its start in robotics with the help of Schneider Packaging Equipment, a Fanuc Authorized Systems Integrator. The potato company liked Schneider’s OptiStak, which creates recipes for the layers of boxes to be stacked on the pallets. Based on Fanuc software, OptiStak optimizes pallet confi gurations and enables operators to change patterns on the fl y.
“They have been turnkey, out of the box, with minimal programming needed on our end, and super, super successful in creating consistencies throughout our plant,” Waldo says.
In just a few years, Tasteful Selections has grown from 100 workers to close to 900. The robotic palletizers have also made a di erence in employee accidents and injuries. Previously, workers stacked 50-lb. boxes onto pallets up to 92 in. high, but now the robotic palletizers perform that task. “We were able to come up with a very modular, unique design for our palletizers,” says Justin McHenry, vice president of plant operations for Tasteful Selections. “It’s capable of picking up any one of our boxes in this facility, stacking a pallet as high as will fi t in the back of any refrigerated truck, and does all that without requiring any tooling change.”
Shifting mesh bags of small potatoes presented handling challenges, ultimately tackled by Soft Robotics’ mGrip soft gripper. Source: Tasteful Selections
Finding the right robotic gripper
As the company increased the speed of the processing lines, manually packing the bags of potatoes in boxes became an issue. The packing space did not allow for additional line sta . Additionally, the company wanted to minimize the human touch points to ensure the best food safety and quality practices.
“We want to make sure our potatoes are the safest they can be from farm to fork,” says Lindsey Mebane, food safety manager for Tasteful Selections. “Before we had the robotics, everything was done by hand. For food safety, that is a little bit of a concern because there are more hands touching the product. Granted, they are wearing gloves, but there’s still human contact.”
Robots handling the potatoes, however, was cause for concern as well, since they could potentially cause more bumps and bruises than human handling. Having not only a food-safe but also gentle robotic solution was of utmost importance.
Tasteful Selections approached Soft Robotics to fi nd a robotic gripper that could reliably grab a shifting bag of potatoes while easing the positioning tolerance requirements of the robot and the
machine vision system guiding it. The technology chosen was Soft Robotics’ mGrip soft gripper.
“The end-of-arm tooling for this application needed to be able to handle a lot of variability,” explains Ben Gibson, applications engineering manager of packaging solutions for Soft Robotics. “These are mesh bags of baby potatoes. Depending on how the product settles on the belt, it can end up in lots of di erent shapes or sizes. We were able to handle all of this with a single end-of-arm tool.”
Plus, the unique gripper material allows the bags to be grabbed without any concern of damaging the product by applying too much force or using a rigid gripping tool. “We chose Soft Robotics because of the softness of the plastic and its integration with the Fanuc robot,” says Emilio Lemus, automation manager for Tasteful Selections. “It speeds up the process a lot and, at the same time, takes care of our product the way we want it to.”
The pneumatic actuated tool allows the operators to control the air pressure to either tighten or loosen the grip on the bags of potatoes. It also knows the correct number of bags to fi ll a box as well as where to place them in the box.
“It helps with the quality—hands aren’t being harsh on them—and it helps with the overall effi ciency because we are sending out what we are supposed to be sending out; so it eliminates a lot of human error,” Mebane says.
Innovation gets results
As a result of the mGrip-enabled robotic work cell, Tasteful Selections was able to exceed a packing rate of 45 bags per minute and increase its packing line’s overall equipment effectiveness (OEE) by 15%, achieving 100% return on investment (ROI) within nine months.
Adding robotics also enabled the company to achieve another advantage in 2020—a way to easily social distance employees on the production line. “Because of the robotics, we had less people on the line, so that really helped with the social distancing,” Mebane says. “It allows people to be farther apart because the person putting the bags in boxes isn’t standing right next to the person putting boxes on the pallet.”
With the investment in automation, Tasteful Selections has been able to grow very quickly as well as maintain the quality of its small potatoes for customers. And it’s all been done at an attractive price point.
“If we don’t innovate, we’re not going to be competitive. Our prices will go up,” McHenry says. “We haven’t taken a price increase in 10 years on our product. We’re able to maintain that through automation.”
See more about the explosive growth of robots across industry.
�e rans ormational m act o an n ormation i ital in
By David Greenfield
Director of Content/Editor-in-Chief
Digital twin technology has garnered a great deal of attention recently for its ability to digitally render all the components in a machine, device, or component based on direct data inputs from the real-world physical device. Benefits of digital twin technology include the ability to: • Deliver early warning signs of issues indicating the need for maintenance before unplanned downtime occurs; • Test equipment/system capabilities under differing operational parameters before implementing changes on the plant floor; • Commission plant floor equipment virtually; and • Apply insights for research and development.
Now we’re beginning to see a new application of digital twin technology where it’s being applied to broader sets of asset-related data and information, i.e., an information digital twin.
To better understand the information digital twin and its potential impact on industrial manufacturing and processing operations, we connected with Sean Gregerson, vice president of asset performance management at Aveva, for a recent episode of the “Automation World Gets Your Questions Answered” podcast series.
Gregerson noted that a major driver behind the development of information digital twin technology is the fact that most manufacturing and processing companies still have a disconnected data environment in their plants where “we have these siloed functional islands of data connectivity and communication, as well as divisional and stakeholder silos across engineering, operations, and maintenance.” As a result, people at all levels of industrial business operations “still struggle to find the information they need to make timely, informed, and accurate decisions [which contributes to] asset failures, an inability to always deliver on commitments to customers, loss of profits, and safety incidents,” he said.
An information digital twin can solve a lot of these problems, Gregerson said. “You construct the information digital twin by taking all the information you have about your industrial assets today—the design information, the operations information, the commissioning information, the asset management and financial information—and fuse this together into an information data model. Then you link that information data model back to the physical asset itself in 3D in the context of its connectivity within the plant. This allows for more informed, more timely, and more accurate decisions to be made.”
Data democratization
A term closely associated with the information digital twin and Industry 4.0 in general is data democratization. Referencing recent research on industrial data, Gregerson said 50% of all the industrial data available today has been created in the last two years. And researchers estimate that 96 zettabytes worth of data has been captured, copied, and consumed in the last 12 months alone.
“Data democratization is all about transforming this raw data into actionable and contextualized information that enables accurate and optimized decision making across a business,” he explained. It’s a term that’s universally gaining popularity and is similar to what we hear about the democratization of artificial intelligence in that it’s all about taking artificial intelligence and democratizing it so that it can be used—not just by data scientists—but by any person and role within your business through the use of nocode technologies that enables use of this advanced technology to drive decision making.
Though software is helping industry improve the use of data via data democratization, there’s still a long way to go, Gregerson said. “Unfortunately, [collecting] more data does not translate to better decision making or improved profitability. A recent study by Seagate indicates that only 32% of all the industrial data we have available today is actually being put to work. And it’s reasonable to expect, based on how quickly the amount of data that we have available is continuously growing, that this amount of unleveraged data will continue growing in the same way, unless we take some decisive steps to apply the advanced technologies we have available today.”
Defining data use
To shrink this gap between used and unused industrial data, Gregerson said it’s important for industrial users to not apply technology for technology’s sake.
“We see a lot of industrial operators building things like data lakes in the cloud to store data, but not first defining how that data is going to be used or who is going to use it,” he said. “How can that data be transformed into something that’s meaningful for the consumer of the information if you don’t know who they are. Similarly, we see some industrial operators deploying AI platforms that are touted to solve any and every problem without first understanding what the problems are they’re trying to solve and how that translates into something that’s meaningful for a reliability engineer or a performance engineer.”
In addition to these factors, Gregerson pointed out that industrial companies are still not very good at sharing data. A study conducted by Gartner shows that industrial operators who do share information within their ecosystem of partners, suppliers, OEMs, and customers, receive 3x the economic benefit of those who do not share information.
Dominion Energy’s experience
Providing an example of a company reaping the benefi ts of better data analysis and sharing, Gregerson pointed to Dominion Energy, a supplier of electricity and natural gas with operations across 16 states in the U.S.
He explained that Dominion Energy is currently transforming its energy generation operations and adding more renewables. As part of this transformation, they’ve developed a new service for their residential and industrial customers which allows these customers to understand how their energy is being sourced and what their energy consumption patterns are.
A key technology enabling Dominion Energy to provide this information is the Aveva PI System, which collects operational data from Dominion’s assets at the edge.
“The PI System archives this information in high fi delity—second by second—compresses it, and then organizes it through an asset information model that contextualizes the data so that it’s consumable by all the applications and users connected to it. The contextualization of the data at the source level helps users understand what this information is and how it can be used to make better decisions,” said Gregerson.
Beyond the PI System, Dominion Energy has also implemented self-service analytics and event management. Gregerson said self-service analytics allow the company to track any KPIs (key performance indicators) or analytics they need to drive more informed and timely decisions across the business. And with event management, Dominion can create events that respond to specifi c conditions. “For example, if they want to notify a group of people by email or text when a certain event occurs, or automatically trigger a work order in the enterprise asset management system based on a certain condition being met,” he said.
On top of these data collection, analysis, and communication layers is a “rich visualization layer that brings all this information together and puts it in the context of the consumer of the information and their role within the business,” Gregerson said. And these visual representations are automatically updated whenever new assets are added to the system.
Another step taken by Dominion Energy to better manage and understand all the data it collects is the use of Aveva Data Hub. Gregerson explained that Aveva Data Hub takes data collected at edge and securely transports it to the cloud where the data is further contextualized.
“This information is then made available to Dominion’s customers in a very secure way and on a selective basis,” said Gregerson. Companies can now selectively make this type of information available within their own ecosystem of partners, suppliers, and OEMs, who can then take that information and translate it back into a value proposition for their business.
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Listen to the complete podcast with Aveva’s Sean Gregerson about the Information Digital Twin and Data Democratization.
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W� n ustrial t ernet rotocols Will emain ele ant
By David Greenfield
Director of Content/Editor-in-Chief
Industrial facilities over the past two decades have seen a signifi cant level of fi eldbus communication networks replaced by industrial
Ethernet. According to a 2021 study by HMS Networks, industrial Ethernet accounts for 65% of new installed nodes, while fi eldbuses account for 28% of new installed nodes.
With the continued advance of
Time-Sensitive Networking (TSN) through numerous new product offerings and its ability to bring determinism to standard Ethernet, some might wonder if it’s only a matter of time before industrial Ethernet protocols like Profi net, CC-Link
IE, EtherNet/IP, and EtherCAT begin to recede in the face of TSN. A closer look at TSN and the industrial
Ethernet protocols explains why this is unlikely—and not possible in the foreseeable future.
According to Thomas Burke, global strategic advisor at CLPA (the CC-Link Partner Association),
“TSN technology only addresses network functions at Layer 2 (Data
Link) of the Open System Interconnection (OSI) model for communications. Therefore, it’s only responsible for getting data from one place to another in a deterministic manner without looking at what the data is. What needs to be done with the data is typically handled at the higher-level layers that address application requirements. These are managed by industrial Ethernet technologies.”
Burke notes that some users may wonder why both TSN and industrial Ethernet are needed since most industrial Ethernet protocols can provide determinism when required. He says the answer lies in convergence.
“Typically, most of industrial Ethernet protocols do not allow di erent kinds of tra c to be merged on the same network,” says Burke. “TSN adds this missing capability by allowing multiple tra c types to share the same network while being handled in a deterministic way.”
Burke advises OEMs and end users exploring the use of TSN-enabled industrial Ethernet protocols to ensure those protocols can address the I/O, motion control, and safety aspects of the intended application. For example, he notes that CC-Link IE TSN's protocol uses Layers 3 to 7 of the OSI reference model to build on the Layer 2 TSN capabilities.
“By doing this, it allows I/O, motion, and safety control to be integrated with standard TCP/IP traffi c in a deterministic way,” Burke says.
Read this story on the expansion of TSN product testing for industrial networks.
Read this story on how TSN delivers determinism over Ethernet.
