Hot Forging Preform Geometry Optimization for Grain Size and Force
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Solution Overview
Problem
Existing preform design techniques for single stage hot forging of metals are complex, iterative, and often optimize only specific parameters, ignoring others like material properties and forging press capabilities, leading to inefficiencies and increased production time and cost.
Innovation Solution
A method and system using a multi-objective optimization technique, such as NSGA-II, to generate preform designs by iteratively computing fitness values based on geometry, grain size, and force, ensuring alignment with hot forging press capabilities and desired product qualities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing preform design techniques are used, then some parameters are optimized, but the design process becomes complex and iterative, ignoring other important parameters like material properties and forging press capabilities
Solution Approach 1:
The patent segments the preform design optimization into multiple independent objective functions: geometry fitness value, grain size fitness value, and force fitness value. Each objective is evaluated separately and combined to form a comprehensive fitness assessment, allowing complex multi-parameter optimization to be broken down into manageable components that can be processed systematically
Solution Approach 2:
The patent transforms the design process from geometric parameter optimization only to multi-parameter optimization by introducing material properties and forging press capabilities as additional optimization dimensions. This is achieved by computing multiple fitness values (geometry, grain size, force) that collectively evaluate different parameter sets, enabling simultaneous optimization of diverse parameters without increasing process complexity
2Manufacturing precision
If existing preform design techniques are used, then certain objectives are optimized, but production time increases due to iterative processes
Solution Approach 1:
The patent implements a feedback mechanism where the computed fitness values (geometry, grain size, force) provide immediate evaluation of each preform design candidate. This feedback loop allows the system to iteratively refine design parameters based on quantitative performance metrics, converging to optimal solutions more efficiently than traditional trial-and-error methods by systematically guiding the search toward promising design regions
Solution Approach 2:
The patent performs preliminary computation of fitness values for multiple geometric variations before finalizing the preform design. By pre-evaluating multiple candidates using the multi-objective fitness function and selecting the best variations for subsequent iterations, the system reduces the number of required iterations and accelerates convergence to the optimal design, thereby reducing overall production time
3Manufacturing precision
If existing preform design techniques are used, then specific parameters are considered, but other parameters like material properties and forging press capabilities are ignored
Solution Approach 1:
The patent creates a universal preform design optimization framework that simultaneously handles multiple parameter types: geometric parameters, material properties, and forging press capabilities. The multi-objective fitness function serves as a universal evaluation mechanism that integrates diverse parameters into a cohesive optimization process, making the system adaptable to different materials, products, and press capabilities without requiring separate optimization procedures for each parameter category
Data Source
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AI summary
Preform design is a crucial step in hot forging of metals. Embodiments of present disclosure provide a method and system of generating preform design for single stage hot forging of metals. The method optimizes the preform geometry using three key aspects: geometry, grain size, and forging force by a multi-objective optimization technique. Initially, parameters of a preform of a product and a single stage hot forging press are obtained which are used to generate initial set of geometric variations of the preform using a multi-objective optimization technique. This set is refined iteratively for a predefined number of iterations. At each iteration, a fitness value associated with each geometric variation is computed based on geometry, grain size, and force fitness value, and a new set of geometric variations are generated based on the fitness value which is used in the next iteration.