AI Project Completion Recommendations for Engineering Configuration
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Solution Overview
Problem
Configuring complex engineering projects, such as industrial automation solutions, is a time-consuming and challenging task that requires domain-specific knowledge and experience, often leading to suboptimal results, especially for less experienced users.
Innovation Solution
A semi-automatic method and system utilizing artificial intelligence components to calculate latent representations of initial project items, generate recommendations for completing the project, and allow user interaction to refine suggestions, ensuring compatibility and optimizing module selection based on desired technical properties and KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user manually configures a complex engineering project by iteratively selecting modules, then the user can have full control over the configuration process, but the process becomes time-consuming and requires significant experience and domain-specific knowledge
Solution Approach 1:
The system enables semi-automatic completion by allowing the configuration process to serve itself through AI-generated recommendations. The system automatically analyzes the initial sequence, generates completion recommendations, and presents options to the user, reducing the manual effort and time required while maintaining user control through selective acceptance of recommendations.
Solution Approach 2:
The AI component acts as an intermediary between the user's partial configuration and the complete engineering project. It processes the initial sequence, generates latent representations, and produces recommendations that bridge the gap between the user's input and the final configuration, significantly reducing the time and expertise needed for completion.
2Reliability
If the configuration process is fully manual with iterative module selection, then the user can make informed decisions based on domain knowledge, but the complexity and difficulty increase for less experienced users
Solution Approach 1:
The system replaces the manual mechanical process of iterative module selection with an AI-based recommendation system. The AI component automatically analyzes compatibility, generates latent representations, and suggests appropriate modules, substituting the complex manual decision-making process with automated intelligence while preserving user oversight.
Solution Approach 2:
The system incorporates feedback mechanisms where the AI continuously analyzes the user's initial sequence, generates recommendations based on learned patterns from historical data, and adjusts its suggestions based on user interactions. This feedback loop ensures high-quality module selection while reducing the apparent complexity for users.
3Adaptability or versatility
If the system generates all possible completion recommendations, then the user has comprehensive options to choose from, but the solution space becomes too large and difficult to navigate
Solution Approach 1:
The system extracts and presents only the most relevant completion recommendations from the full solution space. The AI component generates latent representations and identifies the most promising completions, extracting a manageable subset of high-value options rather than presenting all possible combinations, making the solution space navigable while maintaining versatility.
Solution Approach 2:
The system changes the parameter of recommendation presentation by using AI-generated latent representations to prioritize and rank completions. Instead of showing all possible options equally, it transforms the solution space into a ranked list based on compatibility and relevance scores, making it easier for users to navigate while preserving access to diverse options.
4Productivity
If the system uses AI components to generate recommendations, then the configuration process becomes faster and more accessible to less experienced users, but the system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-training AI models on historical engineering project data before actual configuration tasks. The AI components are prepared in advance with learned patterns and knowledge, enabling fast recommendation generation during actual use without requiring complex real-time computations, thus improving productivity while managing system complexity.
Solution Approach 2:
The system employs a nested architecture where the AI recommendation system is integrated within the existing configuration software. The AI components are embedded as services or modules that work alongside the traditional configuration interface, allowing the system to gain AI-powered productivity improvements without completely redesigning the overall system architecture.
Data Source
AI summary
An initial sequence representing a partially configured engineering project is processed by a recurrent neural network to generate recommendations being a sequence of complementary items that completes an engineering project. A feature predictor component computes a set of features for each recommendation. A bisection component selects a feature from the sets of features that distinguishes some of the recommendations and forms pruned recommendations by choosing all instances from the recommendations that have the selected feature. A user interface displays the selected feature, detects a user interaction indicating that the selected feature is required, outputs the pruned recommendations. The engineering project is completed by combining the initial sequence with the chosen pruned recommendation. As a result, a user is supported in choosing optimal modules, as the selected feature can distinguish the recommendations that have the desired technical properties or target system KPI.