AI Engineering Framework for Automation Code Reuse
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Solution Overview
Problem
The application of artificial intelligence (AI) in the engineering phase of industrial automation is underdeveloped due to issues such as scarce engineering data, short duration of the engineering phase, and difficulty in capturing human intent and knowledge, leading to longer development cycles and non-reusable codes.
Innovation Solution
The implementation of machine learning techniques, including code classification, semantic code search, and hardware recommendations, to automate engineering tasks by organizing and predicting code and hardware configurations, utilizing neural networks and data curation to improve productivity and code reusability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning techniques are applied to automate engineering tasks, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between human engineers and automation systems. These models learn from historical engineering data and automatically generate code, hardware configurations, and system designs, acting as a mediator that translates engineering requirements into implementable solutions without requiring deep expert knowledge from users.
Solution Approach 2:
The system creates reusable code templates and configurations by learning from past engineering projects. Once a solution is developed for a particular automation task, the machine learning model captures its structure and logic, then automatically generates similar solutions for future projects with analogous requirements, eliminating the need to recreate solutions from scratch.
2Loss of time
If code reusability is improved through machine learning, then loss of time is reduced, but manufacturing precision may worsen due to automated code generation
Solution Approach 1:
The machine learning model incorporates feedback mechanisms where generated code is automatically tested against validation criteria and historical performance data. The system learns from successful and unsuccessful code generations, continuously refining its output quality. User corrections and modifications to generated code are fed back into the training data, improving future generation accuracy while maintaining reusability.
3Productivity
If hardware configuration accuracy is improved through machine learning, then productivity is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The machine learning model performs preliminary validation and verification of hardware configurations before final deployment. It checks compatibility between selected components, validates configuration syntax, and ensures adherence to system requirements upfront, preventing errors from propagating to later stages and reducing the need for manual debugging.
Data Source
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AI summary
Applications of artificial intelligence (AI) in industrial automation have focused mainly on the runtime phase due to the availability of large volumes of data from sensors. Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.