With the popularity of ChatGPT, generative AI has not only shown unlimited possibilities in social media, literary creation and other fields, but has also quietly transformed every inch of the industrial field. This integration of technology is not just simple automation, but also a subversive challenge to industrial production methods and business models.
The integration of generative AI in the field of industrial automation is increasingly becoming a key engine to promote productivity and efficiency. In particular, the combination of AI and programmable logic controllers (PLCs) has opened a new chapter. In this era of digital transformation, companies need to pay attention not only to the mechanical equipment on the production line, but also to the role of artificial intelligence, a new tool, in industrial automation.
As the core equipment of industrial automation, PLC plays a key role in controlling various machinery and equipment. However, traditional PLC mainly relies on preset logic and programs to perform tasks, and its flexibility and adaptability are relatively limited. After the introduction of AI, PLC has more advanced functions and performance.
While you are still immersed in admiration for the power of AI technology, industrial giants have already taken the lead and produced amazing "works".
During the Hannover Industrial Fair that ended last month, Siemens demonstrated the first generative AI product for engineering design in industrial environments, Siemens Industrial Copilot. This generative AI, now connected with TIA Portal, can help engineering teams generate basic virtualization tasks and codes for programmable logic controllers (PLCs) and automatically handle repetitive tasks, greatly reducing the workload of engineering teams while ensuring that engineering design of complex tasks is less prone to errors, thereby shortening development time and improving quality and productivity.
In addition to Siemens, B&R is working with Microsoft to bring AI to its industrial automation software. By integrating with the Automation Studio engineering platform, developers of machine applications will be able to generate, optimize and annotate code using intuitive natural language.
In addition, Beckhoff has developed a TwinCAT Chat client for the TwinCAT XAE development environment. Through TwinCAT Chat, large language models (LLMs) such as ChatGPT can be easily used in the process of developing TwinCAT projects to improve programming efficiency. The TwinCAT Chat client can automatically complete AI-supported programming, such as creating or adding function block code, and can even refactor and optimize code and write documents. It not only saves developers' time, but also avoids errors that occur when manually transferring code.
In China, many large automation manufacturers are also actively trying innovative integration of AI technology and have achieved remarkable results.
On April 8, SUPCON Technology launched the new version of SUPCON SCADA 6.5 at the SUPCON SCADA Spring Conference. This version has interactive AI functions, including a new AI programming assistant with a built-in VBS programming model. It can quickly generate program code by inputting requests, helping users to carry out secondary development of software functions. The AI smart question function has undergone extensive training and has deeply studied a large number of product manuals and application problems. It can provide users with real-time answers and guidance for engineering problems, improving the efficiency of engineering construction.
At the Chengdu International Industrial Expo in April, Dongtu Technology demonstrated the first intelligent controller in China that implements industrial AI, with embedded Intewell® industrial operating system and MaVIEW industrial software. The controller converts human natural language instructions into robot motion instructions through the AI big model, realizing semantic-based real-time motion control .
Well-known PLC manufacturers have turned their attention to the field of AI. Is it just to cater to the development trend of intelligent manufacturing? In my opinion, with the increasing maturity of technology and market, the era of price wars in PLC has passed. The previous market was about products and cost performance, while the current market is more about brands, services, solutions, ecology and other aspects.
In addition to having an early layout in the PLC market and establishing a certain user base and market share, foreign brands also have the advantage of a complete ecosystem. For example, Siemens has a complete software ecosystem as a support, such as TIA Portal. These software not only provide advanced PLC programming and monitoring functions, but also support industrial Internet and digital transformation, helping users to achieve intelligent production and data-driven decision-making. With the support of AI, the brand's ecological advantages are further consolidated, and AI lowers the threshold for software use and improves productivity, providing users with simpler, more efficient, personalized and intelligent solutions. Therefore, automation manufacturers are not only following the development trend of the industry, but also reshaping the market competition landscape.
The PLC programming revolution brought about by AI technology is taking place in the field of industrial automation. Looking at the performance of AI in other industries, engineers can't help but ask: Will I be replaced by AI? Regarding the issue of AI replacing human labor, the editor's view has always been that there is no need to worry, it is too early.
First of all, the writing of PLC programs requires certain electrical, automation and programming knowledge. Although AI technology can theoretically learn and imitate this knowledge, it requires a lot of data and training time to do so. Especially when facing some complex or non-standard tasks, human intuition and experience are still indispensable.
Secondly, electrical engineers are not only responsible for writing PLC programming, but also for a series of tasks such as equipment installation, debugging, maintenance and troubleshooting. These tasks require professionals with rich practical experience to complete, but AI does not yet have these capabilities.
Finally, AI, as a tool for auxiliary programming, will not replace engineers, but will instead drive engineers to continuously improve their professional skills. When automation engineers no longer need to spend a lot of time and energy to manually write code, but instead hand over these tasks to AI programming tools, they can devote more energy to equipment optimization, process improvement, etc., thereby improving production efficiency and product quality. This transformation makes engineers' work more meaningful and valuable.
Therefore, AI and engineers are not in a competitive or replacement relationship, but can promote each other. Imagine if engineers have rich professional knowledge and practical experience, and also understand AI, such compound talents can be said to have a greater competitive advantage. Embracing AI as early as possible is an inevitable choice to keep up with the times. Only what I know can be used by me.
