Industrial Automation GUI With AI Compatibility and Troubleshooting
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Solution Overview
Problem
Designing and troubleshooting industrial automation systems is inefficient due to the lack of design guardrails, compatibility checks, and automated troubleshooting, leading to human error and resource-intensive processes, where designers often rely on manual updates and external experts for minor adjustments.
Innovation Solution
Implementing AI and machine learning to enforce design rules, suggest compatible components and connections, provide historical data-based troubleshooting suggestions, and enable a light engineering client for operators to make minor adjustments, while automatically updating naming conventions and generating project code analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If designers manually write code for each component and manually update naming conventions, then design flexibility is maintained, but design time and productivity are significantly reduced
Solution Approach 1:
The system performs preliminary actions by automatically generating code for each component based on predefined templates and specifications before the designer needs to use it. Naming conventions are pre-configured and automatically applied when components are added to the system, eliminating the need for manual code writing and name updates.
Solution Approach 2:
The design system provides self-service capabilities by automatically generating component code, updating naming conventions, and configuring system parameters without requiring manual intervention from designers. The system monitors itself and performs routine design tasks autonomously based on the configured specifications.
2Reliability
If designers are free to use any objects without compatibility checks, then design freedom is maintained, but system reliability is reduced due to potential incompatibilities
Solution Approach 1:
The system implements feedback mechanisms that automatically check component compatibility when objects are added or connected. The compatibility checking system provides real-time feedback to designers about potential incompatibilities, allowing them to make informed decisions while maintaining system reliability through automated validation.
3Manufacturing precision
If extensive code review and troubleshooting are performed manually, then design accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
The system replaces manual code review and troubleshooting activities with automated computational processes. AI-based analysis tools automatically examine generated code for errors, inconsistencies, and potential issues, substituting the mechanical process of manual review with automated software-based verification that is both faster and more thorough.
4Reliability
If highly trained designers are required to ensure system quality, then design reliability is improved, but operational complexity and cost increase
Solution Approach 1:
The system provides self-service capabilities that automatically perform quality assurance functions previously requiring highly trained designers. Automated code generation, compatibility checking, and error detection enable less experienced personnel to create reliable systems, reducing the complexity associated with designer expertise requirements while maintaining design quality.
Data Source
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AI summary
A graphical user interface (GUI) for designing an industrial automation system via an electronic display includes a design window and a first accessory window. The GUI presents a library visualization representative of a plurality of objects within the first accessory window, each object represented by an icon and corresponding to a respective industrial automation device. The GUI receives a first input indicative of a first selection of a first object from the library, presents the first object in the design window, receives a second input indicative of a second selection of a second object from the library, presents the second object in the design window, determines a suggested next action based on historical data including a plurality of industrial automation system designs having the first and second objects, and updates the GUI to display a notification comprising the suggested next action.