Method and system of generating preform design for single stage hot forging of metals

Abstract

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.

What is claimed is: 
     
         1 . A processor implemented method, comprising:
 obtaining, via one or more hardware processors, a plurality of parameters of a preform of a product and a single stage hot forging press;   generating, via the one or more hardware processors, a plurality of geometric variations of the preform of the product based on the plurality of parameters using a multi-objective optimization technique; and   iteratively performing, via the one or more hardware processors, a plurality of steps for a predefined number of iterations, wherein the plurality of steps comprise:
 computing a fitness value associated with each of the plurality of geometric variations, wherein the fitness value is a function of a geometry fitness value, a grain size fitness value, and a force fitness value; and 
 generating a new set of geometric variations of the preform of the product using a plurality of selected geometric variations from among the plurality of geometric variations based on the associated fitness value, wherein the new set of geometric variations is used in subsequent iterations, and wherein a geometric variation is selected as preform design, from among the new set of geometric variations obtained after performing predefined number of iterations, based on the associated fitness value. 
   
     
     
         2 . The method of  claim 1 , wherein the plurality of parameters comprise i) length of the product, ii) a plurality of sections x i  comprised in the preform, iii) radius of each of the plurality of sections, iv) an initial grain size distribution of material of the preform, v) a target grain size distribution of material of the product, vi) strain rate for single stage hot forging, vii) a strain within the preform, viii) a forging temperature, and ix) capacity of a hot forging press. 
     
     
         3 . The method of  claim 2 , wherein the geometry fitness value for a geometry of the preform, from among the plurality of geometric variations of the preform, is obtained by:
 determining a complexity C of the geometry as a sum of geometrical complexities of each of the plurality of sections calculated using the radius of each of the plurality of sections;   calculating a form filling error F x  for each of the plurality of sections as:   
       
         
           
             
               
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 determining the geometry fitness value as sum of the complexity of the geometry and the form filling error of the plurality of sections. 
 
     
     
         4 . The method of  claim 2 , wherein the grain size fitness value for a geometry of the preform from among the plurality of geometric variations of the preform is obtained by:
 calculating an effective strain at each section of the preform during hot forging into the final product;   determining dynamic recrystallization (DRX) area fraction and DRX grain size based on the calculated effective strain;   updating the initial grain size distribution based on the DRX area fraction and DRX grain size to obtain an updated grain size distribution; and   calculating Root Mean Squared Error (RMSE) between the updated grain size distribution and the target grain size distribution, wherein the calculated error is the grain size fitness value.   
     
     
         5 . The method of  claim 2 , wherein the force fitness value for a geometry of the preform from among the plurality of geometric variations of the preform is obtained by:
 computing a forging force necessary to transform the geometry of the preform into the final product based on i) a flow stress (y ƒ ), ii) contact area (A), and iii) coefficient of friction (μ) between die of the hot forging press and material of the preform, wherein the flow stress is determined based on the forging temperature, the strain rate for single stage hot forging, and the strain within the preform, and wherein the contact area is calculated by multiplying the length of the product and width between the preform and the die of the hot forging press; and   determining variance between the computed forging force and the capacity of the hot forging press, wherein the determined variance serves as the force fitness value.   
     
     
         6 . A system comprising:
 a memory storing instructions;   one or more Input/Output (I/O) interfaces; and   one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
 obtain a plurality of parameters of a preform of a product and a single stage hot forging press; 
 generate a plurality of geometric variations of the preform of the product based on the plurality of parameters using a multi-objective optimization technique; and 
 iteratively perform a plurality of steps for a predefined number of iterations, wherein the plurality of steps comprise:
 compute a fitness value associated with each of the plurality of geometric variations, wherein the fitness value is a function of a geometry fitness value, a grain size fitness value, and a force fitness value; and 
 generate a new set of geometric variations of the preform of the product using a plurality of selected geometric variations from among the plurality of geometric variations based on the associated fitness value, wherein the new set of geometric variations is used in subsequent iterations, and wherein a geometric variation is selected as preform design, from among the new set of geometric variations obtained after performing predefined number of iterations, based on the associated fitness value. 
 
   
     
     
         7 . The system of  claim 6 , wherein the plurality of parameters comprise i) length of the product, ii) a plurality of sections x i  comprised in the preform, iii) radius of each of the plurality of sections, iv) an initial grain size distribution of material of the preform, v) a target grain size distribution of material of the product, vi) strain rate for single stage hot forging, vii) a strain within the preform, viii) a forging temperature, and ix) capacity of a hot forging press. 
     
     
         8 . The system of  claim 7 , wherein the geometry fitness value for a geometry of the preform, from among the plurality of geometric variations of the preform, is obtained by:
 determining a complexity C of the geometry as a sum of geometrical complexities of each of the plurality of sections calculated using the radius of each of the plurality of sections;   calculating a form filling error F x  for each of the plurality of sections as:   
       
         
           
             
               
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       wherein a is a predefined material surplus necessary for complete die filling operation and is defined for the entire preform of the product, 
       
         
           
             
               A 
               p 
               
                 x 
                 i 
               
             
           
         
       
       is area of the geometry at the section x i , and 
       
         
           
             
               A 
               fp 
               
                 x 
                 i 
               
             
           
         
       
       is the area of the product at the section x i ; and
 determining the geometry fitness value as sum of the complexity of the geometry and the form filling error of the plurality of sections. 
 
     
     
         9 . The system of  claim 7 , wherein the grain size fitness value for a geometry of the preform from among the plurality of geometric variations of the preform is obtained by:
 calculating an effective strain at each section of the preform during hot forging into the final product;   determining dynamic recrystallization (DRX) area fraction and DRX grain size based on the calculated effective strain;   updating the initial grain size distribution based on the DRX area fraction and DRX grain size to obtain an updated grain size distribution; and   calculating Root Mean Squared Error (RMSE) between the updated grain size distribution and the target grain size distribution, wherein the calculated error is the grain size fitness value.   
     
     
         10 . The system of  claim 7 , wherein the force fitness value for a geometry of the preform from among the plurality of geometric variations of the preform is obtained by:
 computing a forging force necessary to transform the geometry of the preform into the final product based on i) a flow stress (y ƒ ), ii) contact area (A), and iii) coefficient of friction (μ) between die of the hot forging press and material of the preform, wherein the flow stress is determined based on the forging temperature, the strain rate for single stage hot forging, and the strain within the preform, and wherein the contact area is calculated by multiplying the length of the product and width between the preform and the die of the hot forging press; and   determining variance between the computed forging force and the capacity of the hot forging press, wherein the determined variance serves as the force fitness value.   
     
     
         11 . One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
 obtaining a plurality of parameters of a preform of a product and a single stage hot forging press;   generating a plurality of geometric variations of the preform of the product based on the plurality of parameters using a multi-objective optimization technique; and   iteratively performing a plurality of steps for a predefined number of iterations, wherein the plurality of steps comprise:
 computing a fitness value associated with each of the plurality of geometric variations, wherein the fitness value is a function of a geometry fitness value, a grain size fitness value, and a force fitness value; and 
 generating a new set of geometric variations of the preform of the product using a plurality of selected geometric variations from among the plurality of geometric variations based on the associated fitness value, wherein the new set of geometric variations is used in subsequent iterations, and wherein a geometric variation is selected as preform design, from among the new set of geometric variations obtained after performing predefined number of iterations, based on the associated fitness value. 
   
     
     
         12 . The one or more non-transitory machine-readable information storage mediums of  claim 11 , wherein the plurality of parameters comprise i) length of the product, ii) a plurality of sections x i  comprised in the preform, iii) radius of each of the plurality of sections, iv) an initial grain size distribution of material of the preform, v) a target grain size distribution of material of the product, vi) strain rate for single stage hot forging, vii) a strain within the preform, viii) a forging temperature, and ix) capacity of a hot forging press. 
     
     
         13 . The one or more non-transitory machine-readable information storage mediums of  claim 12 , wherein the geometry fitness value for a geometry of the preform, from among the plurality of geometric variations of the preform, is obtained by:
 determining a complexity C of the geometry as a sum of geometrical complexities of each of the plurality of sections calculated using the radius of each of the plurality of sections;   calculating a form filling error F x  for each of the plurality of sections as:   
       
         
           
             
               
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                   , 
                 
               
             
           
         
       
       wherein a is a predefined material surplus necessary for complete die filling operation and is defined for the entire preform of the product, 
       
         
           
             
               A 
               p 
               
                 x 
                 i 
               
             
           
         
       
       is area of the geometry at the section x i , and 
       
         
           
             
               A 
               fp 
               
                 x 
                 i 
               
             
           
         
       
       is the area of the product at the section x i ; and
 determining the geometry fitness value as sum of the complexity of the geometry and the form filling error of the plurality of sections. 
 
     
     
         14 . The one or more non-transitory machine-readable information storage mediums of  claim 12 , wherein the grain size fitness value for a geometry of the preform from among the plurality of geometric variations of the preform is obtained by:
 calculating an effective strain at each section of the preform during hot forging into the final product;   determining dynamic recrystallization (DRX) area fraction and DRX grain size based on the calculated effective strain;   updating the initial grain size distribution based on the DRX area fraction and DRX grain size to obtain an updated grain size distribution; and   calculating Root Mean Squared Error (RMSE) between the updated grain size distribution and the target grain size distribution, wherein the calculated error is the grain size fitness value.   
     
     
         15 . The one or more non-transitory machine-readable information storage mediums of  claim 12 , wherein the force fitness value for a geometry of the preform from among the plurality of geometric variations of the preform is obtained by:
 computing a forging force necessary to transform the geometry of the preform into the final product based on i) a flow stress (y ƒ ), ii) contact area (A), and iii) coefficient of friction (μ) between die of the hot forging press and material of the preform, wherein the flow stress is determined based on the forging temperature, the strain rate for single stage hot forging, and the strain within the preform, and wherein the contact area is calculated by multiplying the length of the product and width between the preform and the die of the hot forging press; and   determining variance between the computed forging force and the capacity of the hot forging press, wherein the determined variance serves as the force fitness value.