Design of Automated Production Line and Optimization of Production Scheduling for Die Casting of New Energy Vehicle Motor Shell

This introductory paper is the research content of the paper "Design of Automated Production Line and Optimization of Production Scheduling for Die Casting of New Energy Vehicle Motor Shell" published by Journal of Computers.

1. Overview:

• Title: Design of Automated Production Line and Optimization of Production Scheduling for Die Casting of New Energy Vehicle Motor Shell
• Author: Yan Lu, Da-Lei Zhang, and Hao-Sheng Lu
• Publication Year: 2025
• Published Journal/Society: Journal of Computers
• Keywords: electric motor casing, integrated die casting, particle swarm optimization algorithm, production scheduling

2. Abstract

For the production of drive motors for new energy vehicles, this article focuses on the die-casting island as the production core. By adding robot modules in the handling and transfer process, the automation process around the die-casting island is improved to achieve automatic loading and unloading of the overall production process. Then, based on the production order requirements, the optimization of the entire production process of the motor order is taken as the scheduling objective. Production sorting optimization is carried out through scheduling sub batches, and a composite production cost composed of mold replacement cost, storage cost, and waiting time of the soup changing robotic arm is established as the objective function for optimizing the production scheduling of the die-casting workshop. In terms of solving the optimal solution, the simulated annealing algorithm is integrated into the particle swarm algorithm, and the improved particle swarm algorithm is used to optimize the objective function. Finally, the number of die-casting machines and orders in production are simulated to verify the effectiveness of the scheduling algorithm in this paper.

3. Research Background:

Background of the research topic:

New energy vehicles are the primary direction of the global automotive industry. The electric drive system is a vital component of new energy vehicles. The drive motor is the core component that determines the vehicle's power performance. The motor housing is the main component structure of the driving motor.

Status of previous research:

There is relatively little research on the integrated die-casting molding process of the drive motor housing for new energy vehicles. Previous research has focused on production layout, numerical simulation of aluminum liquid flow, die-casting process parameters, and intelligent unit design. Some research exists on embedded steel sleeves to improve hardness. [6, 7, 8, 9, 10]

Need for research:

Existing die-casting production of drive motor housing needs improved production line design, production efficiency, and product quality.

4. Research purpose and research question:

Research purpose:

To design a new energy vehicle drive motor housing die-casting production line, and study the rationality of the production line design, as well as the improvement of production efficiency and product quality throughout the entire production process.

Core research:

1. Design an automated die-casting system production line centered around the die-casting island.
2. Develop a mathematical model for production cost, including mold replacement, storage, and waiting time of the soup changing robotic arm.
3. Use an improved particle swarm algorithm to design an optimal scheduling plan.
4. Verify cost savings through simulation.

5. Research methodology

The research uses a combination of design, modeling, and simulation:

• Research Design: System design of an automated production line, focusing on the die-casting island and incorporating robot modules.
• Data Collection: Real order data and die-casting island numbers are used as parameters.
• Analysis Method: An improved particle swarm optimization algorithm (integrating simulated annealing) is used to optimize the objective function (composite production cost).
• Research Scope: Die-casting of new energy vehicle motor shells, focusing on automation and production scheduling.

6. Key research results:

Key research results and presented data analysis:

• An automated production line design was created, centered around the die-casting island (Fig. 2).
• A control system framework was designed using Siemens PLC, model S7-1200 (Fig. 3).
• An internet control system framework was designed (Fig. 4).
• A mathematical model for production scheduling was established, considering mold replacement cost (Eq. 2), storage cost (Eq. 3), and waiting time of the soup changing robotic arm (Eq. 4, 5, 6).
• An improved particle swarm optimization algorithm was developed (Fig. 7).
• Simulation results showed the effectiveness of the scheduling algorithm (Fig. 10).

List of figure names:

• Fig. 1. Structure of die-casting motor
• Fig. 2. Functional layout of the overall production line
• Fig. 3. Industrial internet of things architecture framework diagram
• Fig. 4. Schematic diagram of the overall framework structure of the system
• Fig. 5. Time error table for mold replacement
• Fig. 6. Completion time, delivery date, and delay diagram
• Fig. 7. Algorithm flow chart
• Fig. 8. Production line design structure
• Fig. 9. System flow chart
• Fig. 10. Schematic diagram of algorithm iteration effect results

7. Conclusion:

Summary of key findings:

The paper presents a comprehensive design and optimization of an automated production line for die-casting motor housings. It addresses automation, production scheduling, and cost optimization using an improved particle swarm algorithm.

The research provides a practical solution for automating and optimizing die-casting production lines for new energy vehicle motor housings. It contributes to the field by integrating automation, scheduling optimization, and cost reduction strategies. The improved particle swarm algorithm offers a valuable tool for solving complex scheduling problems in manufacturing.

8. References:

• [1] J.-T. Zhai, Analysis of Development Status and Prospect of New Energy Vehicles, Automobile Applied Technology 48(20)(2023) 193-198.
• [2] X.-H. Wang, D.-X. Gao, Design of Test and Analysis System for Electric Vehicle Driving Motor, Automation & Instrumentation 38(11)(2023) 7-12.
• [3] X.-J. Bai, Z.-Y. Zhu, Driving Innovation: China's NEV Development Model and Future Outlook, China Economist 19(6)(2024) 31-57.
• [4] X.-W. Wang, M.-L. Wang, C.-Y. Kang, W.-H. Xu, L.-Y. Li, Analysis of Machining Process and Fixture Design for the Motor Case of New Energy Vehicles, Development & Innovation of Machinery & Electrical Products 37(3)(2024) 53-56.
• [5] T.-R. Luo, Z.-Z. Fan, H.-X. Hu, J.-H. Wang, Y.-Z. Tian, Research Status and Development Trend of Integrated Die-casting Aluminum Alloy for New Energy Vehicles, Special Casting & Nonferrous Alloys 43(11)(2023) 1472-1478.
• [6] L.-H. Tao, X.-C. Chi, D.-W. Gu, Optimal Scheduling of Automatic Die Casting Production Line Based on Improved PSO, Mechanical Engineer (9)(2021) 4-7+10.
• [7] H.-T. Xiao, J.-Q. Liao, W.-J. Guan, H. Liu, J.-H. Gu, Die Casting Process for the New Energy Motor Shell, Foundry Equipment & Technology (5)(2022) 8-12+25.
• [8] J. Liu, X.-M. Wang, H.-H. Liu, Z.-J. Zhou, Study on sand mold gravity gating system for box type magnesium alloy castings, World Nonferrous Metals (10)(2022) 178-180.
• [9] X.-D. Huang, T. Chang, F.-L. Wei, Design and Research of Intelligent Die Casting Unit, Foundry Equipment & Technology (6)(2019) 48-52.
• [10] F.-Z. Deng, F. Zhong, X. Zhao, J. Cai, A Kind of Optimization Design of New Energy Automobile Motor Side Shell Embedded Steel Bushing Design in the Die-casting Mould, China Southern Agricultural Machinery 53(19)(2022) 25-28.
• [11] X. Ji, L.-J. Liu, Z.-X. Jia, J.-Q. Li, D.-H. Liao, J. Fang, Numerical Simulation of Die Casting Process for Motor Housing and Prediction of Thick Wall Defects, Special Casting & Nonferrous Alloys 44(5)(2024) 708-712.
• [12] H.-Z. Li, W.-Z. Si, J.-J. Cao, Y.-S. Feng, H.-W. Zhang, H. Ruediger, Design and simulation of a handling robot for wheel-hub die casting, Manufacturing Technology & Machine Tool (8)(2021) 85-90+96.
• [13] P. Wang, H. Yu, S.-L. Zhang, Optimal Design of New Energy Vehicle Drive Motor Shell and Iron Core Interference, Auto Electric Parts (10)(2024) 16-17+21.
• [14] M. Cong, C.-Q. Lu, D. Liu, Q.-Y. Xiao, R.-D. Li, Detection method for tiny defects in casting appearance based on Refine-ACTDD, Computer Integrated Manufacturing Systems 28(9)(2022) 2815-2824.
• [15] H.-J. Zhou, Y.-T. Wu, Y.-L. Zhao, H.-Y. Liu, S. Zhang, Die Casting Process Analysis and Defect Improvement of Clutch Housing, Special Casting & Nonferrous Alloys 43(5)(2023) 717-720.
• [16] F. Tian, G.-X. Wang, J.-Q. Zhou, Study on optimization of development process for body integration structure based on high pressure die casting process, Die & Mould Industry 49(8)(2023) 17-23.
• [17] D. Gao, K. Duan, X.-C. Li, X. Wang, Analysis on Technological Characteristics of Automatic Casting Production Line, Heavy Casting and Forging (1)(2024) 18-22.
• [18] Z.-T. Liu, A Multi-machine Batch Scheduling Method Based on the Improved Particle Swarm Algorithm, Progress in Textile Science & Technology 46(5)(2024) 18-24.
• [19] X.-Y. Le, J.-G. Yu, Research on Solving Job-shop Scheduling Problem Based on Improved NSGA-II Algorithm, Mechanical & Electrical Engineering Technology 53(1)(2024) 110-114+197.
• [20] Q.-Q. Liu, H.-L. Yang, Y. Zhang, Batch production scheduling considering time window and deterioration effect, Systems Engineering-Theory & Practice 44(6)(2024) 2033-2045.

9. Copyright:

• This material is a paper by "Yan Lu, Da-Lei Zhang, and Hao-Sheng Lu": Based on "Design of Automated Production Line and Optimization of Production Scheduling for Die Casting of New Energy Vehicle Motor Shell".
• Source of paper: https://doi.org/10.63367/199115992025023601018

This material was created to introduce the above paper, and unauthorized use for commercial purposes is prohibited. Copyright © 2025 CASTMAN. All rights reserved.