Production Module Skill Descriptions via Inductive Class Expression Learning
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Solution Overview
Problem
Existing methods for generating machine-readable skill descriptions of production modules are labor-intensive and require domain expertise, especially for legacy or complex equipment, hindering efficient production planning and automation in dynamic manufacturing environments.
Innovation Solution
A semi-automated method and system using inductive learning to generate machine-readable skill descriptions, leveraging inductive logic programming and ontologies to create class expressions, which are ordered and refined by domain experts for accurate skill descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skill descriptions are defined and digitalized manually by domain experts, then the accuracy and completeness of skill descriptions is improved, but the labor time and expertise requirements increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of domain experts analyzing and documenting skill descriptions with an automated information extraction system using natural language processing and machine learning algorithms. The system automatically processes production module data, operational logs, and technical documentation to generate skill descriptions, eliminating the need for manual analysis while maintaining accuracy through structured data processing and validation rules.
Solution Approach 2:
The system enables production modules to self-describe their capabilities by automatically extracting skill information from their own operational data, technical specifications, and performance logs. The modules generate their own skill descriptions through automated data processing, reducing dependency on external domain experts while ensuring the descriptions reflect actual operational capabilities.
2Loss of information
If skill descriptions are manually created for legacy or complex equipment, then the completeness of skill documentation is improved, but the complexity of the process and expertise requirements worsen
Solution Approach 1:
The patent implements a universal skill description generation system that handles diverse production modules including legacy and complex equipment through a single automated platform. The system uses multi-functional data processing capabilities to extract skill information from various sources (operational logs, technical manuals, sensor data) and applies adaptive algorithms that adjust to different equipment types, eliminating the need for separate manual processes for each device category.
Solution Approach 2:
The system introduces an intermediary automated processing layer between the production modules and the skill description output. This intermediary uses natural language processing, data normalization, and validation rules to bridge the gap between raw operational data and structured skill descriptions, handling the complexity of data extraction and transformation while presenting simplified results to users.
3Productivity
If deterministic programming is used for mass production, then production efficiency is improved, but flexibility and adaptability to new product requirements deteriorate
Solution Approach 1:
The patent transforms the static deterministic programming model into a dynamic system where production modules can adapt their behavior based on real-time data and skill matching. The automated skill description generation enables the system to dynamically assign tasks to appropriate modules based on current capabilities and requirements, allowing flexible reconfiguration for new products while maintaining efficient execution through automated decision-making algorithms.
Solution Approach 2:
The system enables flexible adaptation by changing the parameters of production module assignments rather than reprogramming the modules themselves. The automated skill description system generates and updates capability parameters that allow the production plan to be dynamically adjusted for new product requirements while maintaining efficient deterministic execution of assigned tasks, separating the flexibility layer from the execution layer.
Data Source
AI summary
Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.