LLM-based Control Code Generation using Image Recognition
LLM-based code generation could save significant manual efforts in industrial automation, where control engineers manually produce control logic for sophisticated production processes. Previous attempts in control logic code generation lacked methods to interpret schematic drawings from process engineers. Recent LLMs now combine image recognition, trained domain knowledge, and coding skills. We propose a novel LLM-based code generation method that generates IEC 61131-3 Structure Text control logic source code from Piping-and-Instrumentation Diagrams (P&IDs) using image recognition. We have evaluated the method in three case study with industrial P&IDs and provide first evidence on the feasibility of such a code generation besides experiences on image recognition glitches.
AI for industry: Schaeffler and Siemens bring Industrial Copilot to shopfloor
To support engineers with various automation tasks, the AI-powered assistant is connected to Siemens’ engineering framework Totally Integrated Automation (TIA) Portal via the open API TIA Portal Openness. The Industrial Copilot helps Schaeffler’s automation engineers to generate code faster for programmable logic controllers (PLC), the devices that control most machines throughout the world’s factories. Engineering teams can significantly reduce time, effort, and the probability of errors by generating PLC code through natural language inputs.
Siemens Industrial Copilot has access to all relevant documentation, guidelines and manuals to assist shopfloor workers with identifying possible errors. These capabilities enable maintenance teams to identify errors and generate step-by-step solutions more quickly. This will help to significantly reduce machine downtime, make industrial companies more efficient and thus support sustainability efforts.
Industrial Automation Software Management on AWS—Best Practices for Operational Excellence
Operational and maintenance tasks can become complex, and change control becomes challenging as the number of PLCs and robotics or other automation systems increases. Problems arise when the right version and right configuration of the code is not found. While code and configuration management is a standard DevOps practice for software development, these practices are not as common in the world of industrial automation, primarily due to lack of good tooling. These challenges can now be solved through systematic, secure, and easily accessible solutions in the AWS cloud.
One such solution is Copia Automation’s Git-based source control (Git is an open-source DevOps tool for source code management). Copia Automation brings the power of a modern source control system (specifically, Git) to industrial automation. The Copia solution is deployed in Amazon’s own AWS account. In this type of deployment model, Amazon is responsible for managing and configuring its own infrastructure needed to run Copia’s software.
Fast and efficient PLC code generation and more with artificial intelligence
TwinCAT Chat was developed to offer users a clear advantage over the conventional use of, for example, ChatGPT in the web browser. The key added value lies in its deep integration, especially with regard to the specialized requirements of the automation industry. The core features include the direct integration of the chat function into the development environment (IDE). This greatly simplifies the development process, as communication and code exchange are seamlessly integrated. Furthermore, the basic initialization of our model has been tailored specifically to TwinCAT requests. This way you can ask your specific questions directly and don’t have to tell the model that you are using TwinCAT and expect the code examples in Structured Text. Another highlight is the ability to easily adopt generated code. This not only saves developers time, but also reduces human errors that can occur during manual transfers. Interaction with TwinCAT Chat has been designed in such a way that the need to type commands is reduced to a minimum. Instead, the user can simply click on pre-tested requests that are specifically designed to improve their workflow. These requests include actions such as:
- Optimize: The system can make suggestions to increase the performance or improve the efficiency of the code.
- Document: TwinCAT Chat helps to create comments and documentation so that the code is easier for other team members to understand.
- Complete: If code fragments are missing or incomplete, our system can generate suggestions to complete them to ensure functionality.
- Refactoring: TwinCAT Chat can refactor code according to certain guidelines and policies so that it is more in line with company guidelines.
Overall, this system provides an efficient and intuitive user interface that greatly facilitates the development process.
🌡️🎛️ Why do we need software-defined factories?
The next logical step is to create a software-configurable universal analog front end (AFE). An example is NXP’s N-AFE. In comparison with other universal AFEs currently available, it also integrates all of the discrete components that go into the system, such as protection and signal conditioning, and a slew of diagnostics features. Because it is software-configurable, all the maintenance engineer has to do is replace the sensor and reconfigure the analog input module remotely via the user interface, eliminating the time required to replace the entire measurement unit.
In a system that integrates universal AFEs, the role of the PLC is expanded to include performing safety checks before, during and after these actions by communicating with internal diagnostics and programming terminals to ensure that everything is running smoothly.
Understanding PLC Tags: Controller Scope vs. Program Scope
Every PLC manufacturer, and every developer of text-based code languages, all have a slightly different way of defining a variable within the ladder logic or code, and these methods have evolved over the years.
In some situations, you must have controller-scoped tags. For example, Ethernet and I/O modules always have controller-scoped tags. Likewise, physical part-tracking data should be controller scoped, as every operation will need to modify the part tracking array. Sometimes, HMI tags must also be universal to all programs.
If I’m going to be using program and controller scoped tags within the same machine logic, I like to add a prefix to all global scoped tags so that I can quickly identify if that tag is defined in the controller or the program. Typically I’ll prefix the tag name with a lowercase g which represents global, and program tags would have no prefix.
Comau and Siemens collaborate to integrate robotics and artificial intelligence in the PLC
SIMATIC Robot Library and the “Comau Next Generation Programming Platform” use Profinet’s “Standard Robot Command Interface,” a growing industrial communication protocol. Thanks to this standard, manufacturing companies can quickly and easily program and manage Comau robots using Siemens software and control systems. As the integration and automation between the Siemens PLC and the robotic controller do not require prior knowledge in robotic programming the solution reduces work time and costs, increasing production efficiency.
Dear vPLC, how real-time are you?
Modernizing the factory automation stack requires more than an update of the latest PLC models. Instead, a paradigm shift towards software-defined automation is required. The design and implementation of flexible manufacturing systems for individualized products are crucial for competitive production systems of the future. In such systems, reconfiguration or redeployment of industrial automation systems can be done for every piece, the application of machine learning and artificial intelligence (AI) algorithms is essential, and full-loop feedback systems enable self-optimizing production systems.
Uncoupling of hardware and software not only allows scaling but also helps to overcome supply chain challenges with proprietary PLC hardware due to the vast availability of standard x86 server hardware. The term virtual PLC refers to a soft PLC that runs within a virtual machine managed by a real-time hypervisor in a commercial-off-the-shelf (COTS) server. Servers and computers can offer enough resources to fulfill the functions of PLCs, Human-Machine Interfaces (HMIs), and programming terminals together. A server hosting virtual PLCs that communicate with the shop floor and cloud. Coupling the cloud and shop floor further allows the implementation of software-based PLC operations (Ops), as well as data collection and use of advanced machine learning algorithms, while still satisfying deterministic real-time requirements. Virtual PLCs help overcome the limitations of hardware-based PLCs by offering more flexibility, better resource usage, scalability, and lower costs.
Modern Software Meets Legacy Hardware
However, through the efforts of one of our talented Principal Engineers, Grantek was able to pair the advanced PID Loop Tuning software LOOP-PRO TUNER (from Control Station) with Legacy Siemens/TI 505 PLCs as well as its newest compatible 2500 series PLCs processors manufactured by CTI.
Robot integration ease of use a priority
Leading robot manufacturers – ABB, Comau, Epson, Fanuc, Jaka, Kawasaki, Kuka, Nachi, Panasonic, Stäubli, TM Robot, Yamaha, Yaskawa – joined forces at the initiative of Siemens to develop a solution. Around 70 percent of the world’s robot manufacturers were on board. Now, the joint work has paid off. A uniform data interface between the PLC and the robot controllers has been defined to make robot programming uniform – and thus more efficient – for PLC programmers and PLC suppliers. Via this data interface, robot programs can be written completely in the PLC by calling the robot functions and reporting the required robot state information back to the PLC.
The Old Switcheroo: Hiding Code on Rockwell Automation PLCs
Team82 and Rockwell Automation today disclosed some details about two vulnerabilities in Rockwell programmable logic controllers and engineering workstation software. CVE-2022-1161 affects numerous versions of Rockwell’s Logix Controllers and has a CVSS score of 10, the highest criticality. CVE-2022-1159 affects several versions of its Studio 5000 Logix Designer application, and has a CVSS score of 7.7, high severity. Modified code could be downloaded to a PLC, while an engineer at their workstation would see the process running as expected, reminiscent of Stuxnet and the Rogue7 attacks.
⭐ A Framework for Enhancing the Interoperability of Information across a Plant
Since it is becoming increasingly difficult for a single vendor to meet diversifying user requirements by itself, interoperability among multi-vendor components and control systems such as distributed control systems (DCS) and programmable logic controllers (PLC), has been improved by adopting open industrial communication protocols. However, these protocols, and the information generated, stored, and transferred, are not fully compatible with each other. Accordingly, the open platform communications unified architecture (OPC UA) and related international standards are attracting attention from many vendors and users as a key to high interoperability. This paper introduces how OPC UA improves interoperability among plant components and systems and describes Yokogawa’s prospect.
This paper introduced the trend of FITS and OPC UA FX as standard technologies related to OPC UA. Conventionally, a plant operation system is built by stacking various specialized elements. The system is expected to be integrated vertically and horizontally by industrial-level interoperability standards including OPC UA. As a result, the functional hierarchy will become flat and diverse components and systems will cooperate with each other regardless of the kind of vendors and applications. Yokogawa focuses on the interoperability in the cooperative domain, which was discussed in this paper, and is actively participating in standardization of FITS, OPC UA FX, and IEC/IEEE 60802.
PLCs improve predictive maintenance
There is no doubt PLC technology is already strongly established on the plant floor. However, by embedding IT protocols, Cloud connectivity, and security features into today’s PLCs, it is possible to gather data that may have existed idly and use it to provide a much stronger idea as to what condition devices and machines are in to prevent unplanned downtime.
MES & Machine Learning
As the manufacturing sector continues to embrace digitalization, fully integrated manufacturing execution systems will become more and more useful for managing facilities. However, it is expensive for a plant to fully revamp their IT infrastructure. Manufacturers with partially integrated or non-existent MES won’t upgrade unless there are benefits that outweigh the costs, and returns that can be realized.
Incorporating a MES and subsequent machine learning platform into a facility’s or organization’s infrastructure reduces the cost of manual data processing. Tasks that have traditionally taken hours of manual labor, such as aggregating line data to identify trends, can be automated and completed in minutes or less. In this case, machine learning isn’t competing with statistical process control (SPC) or other traditional quality methods; it’s augmenting them so that engineers spend less time to get better insights into their operations.
Gaining an Edge on Line Control
Edge control provides access to real time OEE and information visualization that changes the value calculation. With edge control, end-users can easily tie together existing equipment, other legacy controllers and new external sensing. The combined raw data can be analyzed at the edge to generate information needed by operators to take fast informed action, and it is the foundation for more advanced production line integration, with the ultimate goal of insight-driven and adaptive operation.
Yaskawa and Phoenix Contact Announce Partnership Collaboration to develop next generation machine controller and PLC Platform
Yaskawa, a manufacturer of motion control, robotics, and variable speed drives and Phoenix Contact, a manufacturer of automation solutions are proud to announce an agreement to utilize PLCnext Technology from Phoenix Contact in the development of the next generation machine controller and PLC platform realizing the i³-Mechatronics solution concept lead by Yaskawa.
Unchain the ShopFloor through Software-Defined Automation
But, what happens as soon as insight is generated and the status of the physical process needs to be changed to a better state? In manufacturing for discrete and process industries, the process is defined by fixed code routines and programmable parameters. It has its own world of control code languages and standards to define the behavior of controllers, robot arms, sensors and actuators of all kinds. This world has remained remarkably stable over the past 40-plus years. Control code resides on a controller and special tools, as well as highly skilled automation engineers, who define the behavior of a specific production system. Changing the state of an existing and running production system changes the programs and parameters required to physically access the automation equipment—OT equipment needs to be re-programmed, often on every single component locally. To give a concrete example, let’s assume we can determine from field data, using applied machine learning (also referenced as Industrial IoT), that a behavior of a robotic handling process needs to be adapted. In the existing world, production needs to stop. A skilled engineer needs to physically re-teach or flash the robot controller. The new movement needs to be tested individually and in context of the adjacent production components. Finally, production can start again. This process can take minutes to hours depending on the complexity of the production system.
Production systems will optimize themselves based on simulated and real experiment. Improvements will rapidly be propagated around the globe. Labor will optimize the learning, not the system. This could also differ over time or by external influence. In times where renewable energy was cheap, output could have been one of the core drivers for optimization, while the minimization of input factors could have been paramount in other circumstances.