It is meant to be a convenient project template that can be used in the discrete manufacturing of many products, from bottled drinks to wrapped candy. I thought it would be helpful to assemble this guide of PackML basics for anyone who is getting started with this standard.

PackML has three main aspects of its implementation:

• Standard Set of Terminology
• State Model
• Set of Tags

Terminology

Common terminology defines how the machine is broken down. Here are a few definitions that can help you navigate a PackML program:

• Unit – An entire machine
  • A Filler, capper, wrapper, etc.
• Equipment module – a station or section of a unit
  • For a filler, bottle infeed, filling, outfeed
• Control module – an actuator or piece of equipment
  • A servo, VFD, or motor
• State – currently active machine condition
  • Idle, Starting, Execute
• State command – an action that triggers a state change
  • The operator presses a button, machine responds to an alarm
• Mode – a set of states and state commands that carry out a process
  • Production, Manual, Clean in place

State Model

The State Model defines the top-level control of the machine. It coordinates the operation of the machine’s components to execute different actions and react to whatever is happening.

For example, when a PackML machine is starved due to an external process condition, the model sends the machine into a suspended state. In the suspending state, all of the machine’s actuators execute their actions to pause production. Then, when the machine has product again, the model sends the machine into an unsuspended state to resume production.

Tags

The Tags define how the program communicates with other machines in the packaging line and with higher-level control systems. Tags are broken down into three types: Command, Status, and Admin Tags.

Command Tags are used by the machine to make a change, while Status Tags are written to by the machine to be used elsewhere. Admin Tags have data to be used for analysis, or operator information, such as stoppage time, stop reason, and units produced.

Why Use PackML?

When building a new machine, or bringing one into a facility, it is important to take a look at how it will be programmed.

A good project template can be useful to generate the backbone of your project and set up a framework to program within. This can speed up development by removing the need to write and debug those central elements of the program. It also helps to standardize your machines so they’re not as dependent on the exact function of the machine and who programmed it. This allows the controls engineers to collaborate more easily, as well as share useful code from similar parts of disparate machines.

Where to get PackML

More information on PackML is available for free from OMAC.

The full ISA TR 88 is available online as well from the ISA.

If you’re starting a project from scratch, Siemens has a template project ready for you to start working from, in both Step7 V5.5 and TIA Portal V13 Sp1.

Rockwell Automation also offers a base project using its Power Programming v4.

Learn more about our Manufacturing Automation & Intelligence services or Contact Us to get started on a project.

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In case you didn’t make it to NIWeek or weren’t able to join us on August 6, we have posted the presentation to SlideShare. During the presentation, Dan reviewed the many challenges that Leary faces in the folding carton industry. For example, ensuring that glue is properly applied to more than 30 separate locations on a complicated six-pack beer carrier was essentially impossible without a machine vision system. This is especially true considering the folder-gluer can process 10 to 50 cartons per second depending on the size of the carton.

After Dan presented the challenges associated with the high-speed folding carton industry, Ken and I rose to these challenges and presented some theories on designing a vision system. We explained that our favorite hardware platform for high-speed, multi-camera, 100% up-time, machine vision systems is a PC running NI’s real-time operating system. This system is connected to standard cameras as opposed to a smart camera which performs image inspection using an onboard processor.

For many of our vision inspection projects NI’s FPGA cards and expansion chassis have been critical tools for controlling digital inputs and outputs for camera triggering and encoder tracking. Though we mainly focused on giving tips on using the FPGA in this way, I’m also excited about the new tools in the 2014 Vision Development Module that make it easy to offload some intensive image processing algorithms to an FPGA. We did not discuss this in our presentation because it is a brand new feature that was just unveiled at NIWeek 2014. I’m eager to use it in future projects.

We also shared some of the random bits of knowledge we have picked up from experience on these projects. Some of my favorite “Knowledge Nuggets” are:

• Avoid creating and destroying image references. Use a static pool of image references that you create once when the program initializes.
• Use integer math instead of floating point math where possible. We demonstrated a 2.5x increase in processing speed for the same mathematical calculation using integers compared to double-precision numbers.
• Use the VI Profiler tool to determine which VIs are taking the most time to execute. This is how we discovered that some of our VIs were spending too much time creating and destroying image references.

Overall, the presentation went well and was well-received by the audience. I hope that audience members gained some useful tips that they can use on their own projects.

NIWeek 2014 had a vision summit that included some other interesting announcements. I already mentioned the FPGA Vision IP that I am excited to use. Another great new product is the cRIO with integrated display for user interfaces. NI’s real-time operating system is great for high-speed vision inspection that requires 100% availability and reliable, deterministic processing. The only downside was that there was no way to program an interface in the real-time operating system. Our other RT vision systems use a separate PC (typically running Windows) to serve as the HMI, communicating with the RT PC via TCP protocols. Now, we can streamline our development by integrating the HMI into the real-time program. Good job, NI!

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DMC partnered with Siemens MES to showcase the Simatic IT Line Monitoring System (LMS) software package. Simatic IT LMS is an ideal platform to help even small brewers improve performance in the filling and packaging areas of their breweries. 

The LMS presentation demonstrated how Simatic LMS software can be used to monitor filling and packaging equipment regardless of the level of control automation present on the machine. LMS can be configured to grab real time data from equipment PLC’s. If this data is not available, a low cost solution consisting of a few sensors and a Siemens 1200 PLC can be used. In either case, downtime occurrences and tracking of machine availability, quality, and performance can be used to calculate equipment KPI’s including Overall Equipment Effectiveness (OEE). 

OEE is one of the most valuable indicators of machine performance and is used across a range of industries to quantify and improve machine performance. Simatic IT LMS makes it easy to convert acquired data into dashboards, reports, and corrective actions for reducing downtime, improving performance, and increasing equipment OEE.

As a Siemens SIMATIC IT Partner, DMC offers value added services helping customers realize solutions tailored to their unique business environments in order to maximize the efficiency of MES installations.

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The system will measure the empty volume inside of a box filled with contents. Once this volume is known, a subsequent system can optimize the number of packing peanuts required to fill up the box. By using this system, the filling of boxes with packing peanuts can be automated.

The Hardware and Software used to measure the empty volume are:

• 3D camera from IFM Electronic (part number PMD O3D201)
• Windows PC
• OpenCV (open source computer vision library)
• C#/Visual Studio 2008/.NET Framework 3.5

Since the 3D camera can output both gray scale and Cartesian coordinate values (X, Y, Z) for each pixel, implementing the solution is not very difficult.

The 3D camera sends the image data via TCP/IP to the PC. The C# program on the PC calculates the empty volume box by processing these images with OpenCV.

The system first measures the outer volume of the box. Then the system calculates the volume of the contents inside of the box. By subtracting the content volume from the outer volume, the empty volume inside the box is calculated.

Check out the video for more info:

Learn more about DMC's vision inspection services.

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