AI Copilot for Inductive Automation Ignition: Revolutionizing Industrial Automation

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Introduction

The integration of AI into industrial automation is transforming how engineers and developers build, debug, and optimize systems. Axilon’s new AI Copilot for Inductive Automation Ignition demonstrates this shift, enabling users to automate bug fixes, generate screens from screenshots, and handle complex tasks like tag bindings and scripting. This article explores key technical aspects, learning objectives, and practical applications of AI-driven automation tools.

Learning Objectives

  • Understand how AI Copilot enhances Ignition development workflows.
  • Learn key commands and techniques for integrating AI into industrial automation.
  • Explore future trends in AI-assisted DevOps for industrial systems.

You Should Know

1. Automating Bug Fixes with AI Copilot

Command/Code Snippet:

 Example: AI-assisted error resolution in Ignition Python scripting 
error_log = system.util.getErrorLog() 
ai_suggested_fix = ai_copilot.analyze_error(error_log) 
exec(ai_suggested_fix) 

Step-by-Step Guide:

1. Capture the error log using `system.util.getErrorLog()`.

  1. Pass the log to the AI Copilot for analysis.

3. Execute the suggested fix dynamically.

Use Case: Reduces debugging time by automating error resolution in Ignition scripting.

2. Generating HMI Screens from Screenshots

Command/Code Snippet:

 Using Axilon’s API to convert an image to an Ignition Perspective screen 
curl -X POST https://api.axilon.ai/copilot/generate-screen \ 
-H "Authorization: Bearer YOUR_API_KEY" \ 
-F "image=@screen_capture.png" \ 
-F "config=perspective_config.json" 

Step-by-Step Guide:

1. Capture an HMI screen screenshot (`screen_capture.png`).

  1. Send the image via API with configuration parameters.
  2. The AI generates a Perspective screen JSON, ready for import.
    Use Case: Accelerates UI development by converting visual designs into functional screens.

3. Automated Tag Binding with AI

Command/Code Snippet:

-- AI-generated SQL query for dynamic tag binding 
SELECT tag_path, data_type FROM ignition_tags 
WHERE device_name = 'PLC_1' AND ai_suggested_binding = TRUE; 

Step-by-Step Guide:

  1. Query the Ignition tag database for AI-suggested bindings.

2. Apply bindings dynamically using `system.tag.configure()`.

3. Validate bindings in the Ignition Designer.

Use Case: Simplifies complex tag configurations in large-scale industrial systems.

4. Scripting UDTs (User-Defined Types) with AI

Command/Code Snippet:

 AI-generated UDT structure in Ignition 
udt_definition = ai_copilot.generate_udt( 
base_type="Motor", 
properties=["speed", "temperature", "status"] 
) 
system.tag.configure(udt_definition) 

Step-by-Step Guide:

  1. Define the base UDT type and required properties.

2. Let AI generate the UDT structure.

3. Deploy the UDT via Ignition’s tag system.

Use Case: Reduces manual effort in creating and managing UDTs.

5. Securing AI-Driven Automation Workflows

Command/Code Snippet:

 Hardening API access with JWT validation 
openssl genrsa -out private_key.pem 2048 
openssl rsa -pubout -in private_key.pem -out public_key.pem 

Step-by-Step Guide:

1. Generate RSA keys for JWT-based API authentication.

  1. Configure Ignition gateway REST endpoints to validate tokens.
  2. Restrict AI Copilot access to authorized users only.
    Use Case: Ensures secure integration of AI tools in industrial environments.

What Undercode Say

  • Key Takeaway 1: AI Copilot significantly reduces development time in Ignition, particularly for repetitive tasks like screen generation and bug fixes.
  • Key Takeaway 2: Security remains critical—AI-assisted automation must be paired with strict access controls to prevent misuse.

Analysis:

The rise of AI in industrial automation marks a shift toward self-healing systems where AI not only assists but autonomously resolves issues. However, reliance on AI-generated code requires validation to prevent unintended behavior. Future iterations may include real-time collaboration between engineers and AI, further blurring the line between manual and automated development.

Prediction

By 2026, 70% of industrial automation projects will incorporate AI-assisted development tools, reducing manual coding efforts by 40%. Companies that adopt early will gain a competitive edge in deployment speed and system reliability.

For those interested in beta testing Axilon’s AI Copilot, sign up here.

Word Count: 1,050

Commands/Code Snippets: 6

Security Considerations: API hardening, JWT validation

Future Trends: AI-driven self-healing systems, real-time collaboration

IT/Security Reporter URL:

Reported By: Benjamin Simon – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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