AI-Guided Assembly Instructions for User-Adaptive Resource Reduction
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Solution Overview
Problem
Current assembly instruction sets do not optimize resource usage effectively, leading to inefficiencies and errors in the assembly process, as they do not account for user-specific factors such as gender, age, and fatigue levels.
Innovation Solution
An assembly assistance device that generates optimized assembly instruction sets using artificial intelligence to analyze resource requirements based on sensor data and user input, combining preliminary instruction sets and sensor data to determine the most efficient sequence of assembly steps, reducing resource consumption and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional assembly instruction sets are used, then the assembly process can be completed, but resource usage is not optimized and errors occur due to not accounting for user-specific factors
Solution Approach 1:
The patent applies local quality by customizing assembly instructions according to specific user characteristics (age, gender, experience level, physical capabilities). Instead of using a uniform instruction set for all users, the system adapts the instructions to match each user's local conditions, thereby improving both efficiency and accuracy simultaneously.
Solution Approach 2:
The patent implements dynamics by making the assembly instruction set adaptive and changeable based on real-time sensor data and user feedback. The system dynamically adjusts instructions during the assembly process rather than following a static predetermined sequence, allowing optimization of resource usage and error reduction.
2Productivity
If multiple preliminary instruction sets are generated and tested, then an optimized assembly instruction set can be found, but the complexity of the system increases
Solution Approach 1:
The patent applies self-service by implementing an automated system that generates multiple preliminary instruction sets, tests them through sensor data collection, and automatically determines the optimized instruction set using machine learning algorithms. This self-service approach handles the complexity internally, allowing the system to achieve high productivity without requiring external intervention to manage the complexity.
Solution Approach 2:
The patent implements feedback by using sensor data collected during assembly processes to evaluate the performance of different preliminary instruction sets. This feedback loop allows the system to learn from actual assembly outcomes and automatically refine the instruction sets, managing complexity through data-driven optimization rather than manual configuration.
3Loss of energy
If sensor data collection and AI analysis are implemented, then resource optimization is achieved, but the time and computational resources required for analysis increase
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple candidate assembly instruction sets before actual assembly begins. The system prepares these instruction sets in advance and uses AI analysis to predict their performance, so that when assembly time comes, the optimized instructions are already ready to use, minimizing the time loss during actual assembly operations.
Solution Approach 2:
The patent implements partial action by collecting and analyzing only the most relevant sensor data needed for determining instruction set optimization, rather than monitoring every possible parameter. This selective approach to data collection reduces the computational burden and analysis time while still achieving effective resource optimization.
Data Source
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AI summary
An assembly assistance device for providing an assembling device with an assembly instruction set, the assembly assistance device comprising: a generation device configured to generate a plurality of preliminary instruction sets based on product data indicating a type and amount of at least some components to be assembled into a product, each preliminary instruction set defining a different assembly process for assembling the components into the product; a sensor device configured to measure sensor data characterizing the assembly process according to each preliminary instruction set; and an assistance device configured to use an artificial intelligence method to generate an optimized assembly instruction set such that an amount of resources required when assembling the product according to the optimized assembly instruction set is reduced.