Research on the precision forming cathode design method of electrochemical machining all-metal screw drill stator
In order to shorten the cathode design cycle, reduce design cost and improve forming accuracy for all-metal screw drill stator electrochemical machining (ECM), this paper proposed a precision forming cathode design method based on particle swarm optimization BP neural network (PSO-BP). The cathode d...
        Saved in:
      
    
          | Published in | International journal of advanced manufacturing technology Vol. 133; no. 7-8; pp. 3663 - 3671 | 
|---|---|
| Main Authors | , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        London
          Springer London
    
        01.08.2024
     Springer Nature B.V  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0268-3768 1433-3015  | 
| DOI | 10.1007/s00170-024-13910-3 | 
Cover
| Summary: | In order to shorten the cathode design cycle, reduce design cost and improve forming accuracy for all-metal screw drill stator electrochemical machining (ECM), this paper proposed a precision forming cathode design method based on particle swarm optimization BP neural network (PSO-BP). The cathode design algorithm model of all-metal screw drill stator electrochemical machining was established, completed the camber feed cathode design. By using self-developed large scale horizontal CNC electrochemical machining equipment, under the condition of voltage 19 V, electrolyte 15%NaCl, electrolyte temperature 35 ± 1℃, electrolyte inlet pressure 1.6 MPa, and feed speed 10 mm/min, the stable and reliable electrochemical machining processing of the 4-m length of 38CrMoAlA all-metal screw drill stator was completed. The contour forming accuracy is ± 0.03 mm, and the surface roughness is Ra0.848 μm. Research showed that it is an efficient and feasible method to design the electrochemical machining camber feed cathode of all-metal screw drill stator using PSO-BP neural network. | 
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14  | 
| ISSN: | 0268-3768 1433-3015  | 
| DOI: | 10.1007/s00170-024-13910-3 |