PLC logic, CNC machining code, communication protocols—these high-stability, high-precision scenarios demand that generative AI not only understands code but also grasps industrial processes and equipment.Can generative AI truly enter the manufacturing domain? And if so, how can it be deployed securely, reliably, and effectively?

With tools like ChatGPT and GitHub Copilot becoming mainstream, AI-assisted programming is no longer confined to research labs—it's reshaping real-world development workflows. With just a natural language prompt, AI can now generate complete, functional code. This paradigm of "natural language is code" is sweeping across industries.

This article explores the practical value and implementation strategies of generative AI in manufacturing programming—how it boosts efficiency, streamlines project execution, and accelerates software-driven transformation in industrial environments.

I. The Core Capabilities and Architecture of Generative AI

1. What Is Generative AI and Why Is It Reshaping Programming?

Generative AI refers to a class of deep learning models capable of generating content—text, images, audio, or code—based on input prompts. Unlike traditional rule-based systems, it doesn’t rely on static templates. Instead, it leverages probabilistic modeling and semantic understanding to dynamically construct output.

Models like GPT-4 and Gemini, trained on trillions of data points, can interpret context, infer intent, and generate structured, context-aware code. Especially in software development, this enables a leap from “requirement description” to “code implementation,” driven entirely by language.

2. The Core Mechanism Behind Generative AI Programming

Prompt Engineering: Teaching AI "How to Listen"

Generative AI programming begins with a prompt—a natural language instruction. For example, "Write a PLC program to control motor start and stop." The AI interprets the objective, identifies the target language, and generates appropriate code.

This process is known as Prompt Engineering, and its quality directly affects the accuracy, readability, and functionality of the generated code.

From Code Completion to Functional Construction

Generative AI’s capabilities now go far beyond code completion. It can:

Auto-generate comments and documentation

Identify bugs and suggest fixes

Create unit test cases

Translate logic across languages (Python, C#, Ladder, etc.)

Build complete API request/response handlers

AI is no longer just writing lines of code—it’s becoming a collaborative participant in software development.

3.The Value and Limits of AI-Assisted Programming

In standardized industrial software environments, AI can automatically generate control logic, CNC paths, HMI scripts, and testing interfaces—dramatically reducing development time and human error. Key use cases include:

Rapid prototyping

Modular logic construction

Natural language-to-script transformation

Automated testing and simulation

However, AI-generated code is not flawless. It lacks real-time context and understanding of field devices. Human engineers are still essential to verify, adjust, and validate code to ensure safety, reliability, and system compatibility.

4. Common Industry Applications of Generative Programming

Software Development: Auto-generation of front-end/back-end code, APIs, test scripts

Fintech: Risk modeling, data analysis scripting

Manufacturing: Generating PLC logic, CNC code, machine interfaces

Education & Training: Coding tutorials, assignment evaluation

Game Development: Behavior scripts, scene logic

Healthcare & Bioinformatics: Data analysis, sequence processing pipelines

II.What are the roles of generative AI in CNC programming for manufacturing?

1. Auto-Generation of PLC Control Logic

Traditional PLC programming often relies on manual expertise. Generative AI can convert functional descriptions into structured code or ladder logic, such as:

Standard start/stop logic for Omron or Mitsubishi PLCs

Modular alarm handling

Interlock logic for multi-device coordination

This lowers the barrier to entry and enables non-programmers to contribute to control system design.

2. Rapid CNC Machining Code Generation

For turning, milling, and drilling processes, generative AI can quickly produce G-code from natural language instructions:

“Turn OD φ60, length 100, Ra1.6” → Auto-generated G71 roughing cycle

Automatic embedding of threading, chamfering, and tool compensation

This provides high flexibility and fast response in small-batch, high-mix production scenarios.

3. Automated HMI Scripting and Data Acquisition Logic

AI can generate HMI interaction scripts, alarm bindings, and data handling logic, including:

UI control logic for WinCC or MCGS

OPC UA or Modbus data collection routines

Database interface logic and variable mapping

This enables engineers to describe control needs in natural language and rapidly build working interface logic.

4. Industrial Protocol Integration and Interface Code

AI can assist in writing and testing communication protocols such as:

Modbus RTU/TCP read/write functions

MQTT publish/subscribe structures

PLC-to-SQL data exchange scripts

This is especially useful in complex environments with diverse device interfaces and inconsistent standards.

5. Semantic-Driven Control Logic Design

AI enables a new paradigm: "natural language is control logic."
Example: “Run motor for 30 seconds, then slow to 50% before stopping.”
The system generates the appropriate PLC or scripting logic automatically—bridging the gap between process engineering and logic implementation.

III. Minnuo’s Real-World Actions in the AI-Powered Programming Revolution

Recognizing the transformative power of generative AI, Minnuo has proactively deployed it across three strategic fronts: development efficiency, smart products, and customer enablement.

1. Intelligent Development Workflow

We’ve integrated generative AI into the development of PLC logic, CNC code, and debug scripts, achieving over 20% reduction in project cycles through automated logic generation.

2. AI-Native Device Platforms

Minnuo's latest CNC systems and control platforms come with built-in semantic-driven programming assistants. Users can configure logic and toolpaths using simple natural language, enabling non-experts to deploy complex workflows.

3. Helping Customers Adopt AI-Driven Programming

In collaboration with customers, we’ve embedded AI tools into on-site systems, enabling fast deployment of device logic in non-standard, flexible manufacturing scenarios.

4. Building a Secure, Customizable AI Platform

To safeguard industrial data, we’ve developed a self-hosted AI environment with private models and Prompt templates—ensuring zero data leakage, traceable logic, and continuous optimization tailored to manufacturing needs.

We’re not just using AI to improve our own development—we’re embedding it into our machines, so our customers can code faster, deploy quicker, and manufacture smarter.

Conclusion: AI-Driven Development, Smarter Manufacturing

Generative AI is redefining how software is written—and how manufacturing moves forward. Minnuo has taken the lead by integrating AI deeply into our R&D process and smart device platforms.

Choosing Minnuo means choosing equipment that supports AI programming—giving you faster workflows, smarter deployment, and greater agility.

Ready to see how AI can reshape your operations? Visit Minnuo’s website and discover the future of intelligent manufacturing today.