ChatGPT
Assembly Line
Introducing Materials.AI: Your AI Assistant for Material Selection
With advances in material science and manufacturing technologies like 3D printing, it can be overwhelming (not to mention time-consuming) to find the right material for your project needs. That’s why we created Materials.AI: a first-of-its-kind artificial intelligence assistant, powered by ChatGPT and Fictiv’s expansive manufacturing database, to help you navigate the complex landscape of plastic and metal materials.
Quality Execution System® – two use cases in the European metals industries
Data from various automation levels is consolidated to represent each coil, bridging the gap between the physical and digital realms. This requires data to be transmitted flawlessly, enabling the virtual coil to mirror the physical coil. As the coil progresses through the production route, a digital counterpart is created at each stage of the process. The Quality Execution System (QES®) is designed to gather, combine, and examine all the data pertaining to a coil, thereby establishing the foundation for its digital twin.
Speira, a leading European aluminum rolling and recycling company, is expanding the QES® application ‘Automatic Coil Grading & Release and Genealogy’ to two of its strip coating line routes as part of a long-term digitalization initiative it launched earlier. Speira’s aluminum rolling mill in Grevenbroich, Germany stands for high-quality automotive, beverage can, foil and lithographic products.
TATA Steel, the second largest European steel manufacturer, is also expanding cooperation with SMS as part of a long-term and early-started digitalization initiative. TATA started with their automated coil release at the cold mill in 2012. One of the main goals was to improve the utilization of surface inspection data and conduct post-processing. TATA invested in the automatic coil release for the DSP (direct sheet plant) and Hot Mill shortly after. The Cold Mill now started implementing the PDW part of the DataFactory. Wouter Overgaauw, Manager Quality Assurance Cold Rolling Mill Ijmuiden, states: “The amount of measurement data is steadily increasing, the possibilities for data-driven applications are improving, the PDW gives us the possibility to make better use of both data and applications.”
Can ChatGPT Create Usable G-Code Programs?
Mike Wearne is an educational content creator at CAMInstructor, has a take on the GPT-3 G-code. “If we use a basic program that’s a drill four holes sort of thing, and compare this to someone who’s just learning G code, I would say it’s not bad,” he says. “I would give it a low B or a high C.” The overall structure was there — it put the right codes in the right places, such as G20 and G21 to switch between metric and imperial units, and G90 for absolute positioning at the top of the program. “If you’re new to G-code programming, those are usually the tough things to remember and to get in the right spot,” he notes. However, it was missing some elements, such as tool changes and spindle speeds.
Wearne also noticed a marked improvement in the G code GPT-4 produces. “It’s like GPT-4 can think more about its answers and GPT-3.5 just spits out whatever it comes up with as quick as it can,” he explains. With its most recent update, Wearne says it can program simple parts almost perfectly. Whereas GPT-3 was getting a high C or low B as a grade for its code, “For the simple parts, if we’re in G-code 101, GPT-4 is getting an A,” he says.
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.
TwinCAT Chat integrates LLMs into the automation environment
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.
Cadence Design Is Working With Renesas To Build The World’s First LLM Tool For Up-Front Chip Design
Cadence has been aggressively rolling out reinforcement learning-based tools to help chip design teams accelerate the processes of digital design, debugging, verification, PCB layout, and multi-physics optimization. Customers have been eating it up, especially the physical design optimizer “Cerebrus” and the underlying cross-platform consolidated database, “JedAI.”
Now, the company has focused on the most challenging part of designing a chip: defining the specs and creating the first clean version of the design that drives the rest of the entire workflow. Renesas and Cadence have collaborated to develop a novel approach to address the up-front design work by leveraging LLMs, significantly reducing the time and effort from specification to final design. The chip design verification, debugging, and implementation phases remain the same today. They call this accelerating “Correct by Construction” design methodology.
Using an LLM, the team can demonstrate interrogating the plan for compliance with specifications and other design and project documents, in areas such as IP connections for data, control, and test, and other requirements specified in the IP and chip level specifications. These steps of cleaning the design code can take individual engineers and the team weeks of design time and hundreds of meetings to reduce the number of bugs they encounter during the simulation and implementation stages of the project. By using an LLM, Cadence hopes to significantly streamline this process.
Retrocausal Revolutionizes Manufacturing Process Management with Industry-First Generative AI LeanGPT™ offering
Retrocausal, a leading manufacturing process management platform provider, today announced the release of LeanGPT™, its proprietary foundation models specialized for the manufacturing domain. The company also launched Kaizen Copilot™, Retrocausal’s first LeanGPT application that assists industrial engineers in designing and continuously improving manufacturing assembly processes and integrates Lean Six Sigma and Toyota Production Systems (TPS) principles favored by Industrial Engineers (IEs). The industry-first solution gathers intelligence from Retrocausal’s computer vision and IoT-based floor analytics platform Pathfinder. In addition, it can be connected to an organization’s knowledge bases, including Continuous Improvement (CI) systems, Quality Management Systems (QMS), and Manufacturing Execution Systems (MES) systems, in a secure manner.
How ChatGPT Programmed an Industrial Robot
Our initial challenge for ChatGPT involved programming the Yaskawa robot to perform a wire cut. This is a very simple task. However, ChatGPT isn’t intrinsically familiar with the INFORM programming language, which is integral to Yaskawa robots. As such, our first step was to delineate the fundamental commands of this language.
Furthermore, ChatGPT had no understanding of the physical robot, its movements, or the typical process of wire-cutting. To address this, we established several coordinates using the robot’s teach pendant and outlined the basic principles of operation.
With these prerequisites met, we put forward our request for ChatGPT to create the required program. The AI successfully rose to the challenge, generating a program that we then transferred to the robot for a test run. The outcome was encouraging, with the robot effectively performing the wire-cutting task as directed.
ChatGPT for Robotics: Design Principles and Model Abilities
ChatGPT unlocks a new robotics paradigm, and allows a (potentially non-technical) user to sit on the loop, providing high-level feedback to the large language model (LLM) while monitoring the robot’s performance. By following our set of design principles, ChatGPT can generate code for robotics scenarios. Without any fine-tuning we leverage the LLM’s knowledge to control different robots form factors for a variety of tasks. In our work we show multiple examples of ChatGPT solving robotics puzzles, along with complex robot deployments in the manipulation, aerial, and navigation domains.