Kinematic Chain Configuration for Faster Mechanical System Synthesis
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Solution Overview
Problem
Mechanism design in mechanical engineering faces challenges in selecting effective configurations for mechanical systems due to conflicting design goals and the complexity of combining mechanical devices, leading to time-consuming trial-and-error approaches that may overlook more efficient solutions.
Innovation Solution
A computer-implemented method generates a set of candidate mechanical systems using kinematic building blocks, applying dynamic equations to optimize configurations based on mechanical inputs and outputs, enabling automated selection and design of mechanical assemblies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error approach is used to select mechanical system configurations, then designers can eventually find a working solution, but the design process becomes time-consuming and may overlook more effective configurations
Solution Approach 1:
The patent applies preliminary action by pre-defining a comprehensive library of kinematic building blocks with their motion characteristics and connection rules before the actual design process. This allows the automated system to efficiently evaluate multiple configurations without trial-and-error, as the foundational elements and their properties are already established and ready for systematic combination and optimization.
Solution Approach 2:
The patent replaces the manual trial-and-error mechanical design process with an automated computer-implemented system. The automated system uses algorithms to generate, evaluate, and optimize mechanical configurations based on predefined criteria, substituting human iterative testing with computational analysis that can simultaneously assess multiple configurations and identify optimal solutions more efficiently.
2Adaptability or versatility
If multiple mechanical devices are combined to achieve complex functionality, then the system can meet diverse design goals, but the device complexity increases making selection and optimization difficult
Solution Approach 1:
The patent applies segmentation by breaking down complex mechanical systems into standardized kinematic building blocks (such as gears, linkages, and other motion-transforming elements). Each building block has defined motion characteristics and connection interfaces, allowing complex functionalities to be achieved through systematic combination of simpler, well-understood components. This segmentation makes the design and optimization process more manageable.
Solution Approach 2:
The patent creates a universal library of kinematic building blocks that can be applied across multiple design scenarios. These standardized components serve multiple functions and can be combined in various configurations to achieve different mechanical outcomes, reducing the need for custom-designed elements and simplifying the selection process for complex systems.
3Manufacturing precision
If physical attributes of mechanical devices are varied through continuous values to optimize performance, then the system can achieve precise functionality, but the number of possible configurations becomes nearly infinite making selection problematic
Solution Approach 1:
The patent systematically varies parameters of kinematic building blocks (such as gear ratios, link lengths, and transmission ratios) within defined ranges to optimize mechanical system performance. The automated evaluation system assesses how changes in these parameters affect the overall system output, allowing precise functionality to be achieved through controlled parameter adjustment rather than exploring all possible continuous configurations.
Solution Approach 2:
The patent implements feedback through an automated evaluation system that assesses each generated mechanical configuration against predefined performance criteria. The system provides feedback on how well each configuration meets the design goals, allowing the optimization process to iteratively refine parameter selections and identify the most effective configurations without manually evaluating all possibilities.
Data Source
AI summary
A computer-implemented method for generating a mechanical system, the method comprising: based on a mechanical input to the mechanical system and a target mechanical output of the mechanical system, generating a set of multiple candidate mechanical systems for generating the target mechanical output in response to receiving the mechanical input, wherein each candidate mechanical system includes multiple mechanical building blocks that form a kinematic chain; selecting a candidate mechanical system included in the set of multiple candidate mechanical systems; and generating an optimized configuration of the selected candidate mechanical system based on a set of dynamic equations for the mechanical system, wherein each dynamic equation included in the set of dynamic equations corresponds to one mechanical building block of the selected candidate mechanical system.