Abstract
Aiming at the possible defects such as pores and cracks in the manufacturing process of steel castings, a defect detection and repair technology for steel castings based on laser remelting process is proposed in combination with intelligent manufacturing technology. Firstly, intelligent manufacturing technology and its application in steel casting manufacturing are introduced and analyzed. Secondly, the laser remelting process technology for repairing steel casting defects is analyzed in detail. Finally, the Selective Laser Melting (SLM) remelting forming strategy is simulated, and the feasibility and effectiveness of the proposed intelligent repair technology in practical application are verified through experiments and simulation analyses. The simulation results show that the SLM remelting and forming strategy provides new ideas and methods in the field of steel casting manufacturing, and offers an important reference basis for the application of intelligent manufacturing technology in the manufacture of steel castings.
Keywords: steel castings; casting defects; laser remelting process; remelt forming strategy
1. Introduction
Steel castings, as important components in the industrial manufacturing industry, may have various types of defects such as pores and slag inclusions during the manufacturing process, which affect the manufacturing quality and product performance. Therefore, research on defect repair technology for steel castings based on intelligent manufacturing has important practical significance. Utilizing Selective Laser Melting (SLM) technology to repair defects in steel casting manufacturing helps improve the quality level of steel casting manufacturing. Therefore, research on steel casting defect repair technology based on intelligent manufacturing SLM technology is highly in line with current industrial development needs.
2. Intelligent Manufacturing SLM Technology for Steel Casting Defects
Intelligent manufacturing refers to the use of information technology to achieve intellectualization, automation, and efficiency in the production process. The key development areas of intelligent manufacturing technology include industrial automation, intelligent equipment, digital factories, and flexible manufacturing systems. SLM is the most promising metal additive manufacturing technology among laser rapid prototyping technologies. It is a rapid prototyping method capable of manufacturing high-performance and structurally complex metal parts. SLM technology uses a high-power laser beam to melt metal powder according to a predetermined trajectory. After cooling, the molten metal rapidly solidifies and forms a solid metal part through layer-by-layer stacking.
Table 1. Overview of Intelligent Manufacturing SLM Technology
| Attribute | Description |
|---|---|
| Technology Name | Selective Laser Melting (SLM) |
| Principle | Melting metal powder with a laser beam and forming it by rapid solidification after cooling |
| Advantages | High performance, complex structure manufacturing, rapid prototyping |
| Applications | Steel casting defect repair, high-performance part manufacturing |
3. Laser Remelting Process Analysis for Steel Casting Defect Repair
3.1 SLM Technology
SLM technology is mainly divided into three stages: model processing, laser melting, and post-processing. The stages are described below.
- Model Processing: Establish a workpiece model using three-dimensional CAD software, create two-dimensional thin slice data from the three-dimensional model according to the specified layer thickness, save it in STL format, and import the processed model into the processing equipment.
- Laser Melting: Use a laser beam to scan and melt the metal powder on the substrate or deposition layer. The melted powder bonds and solidifies with the substrate or the already formed solid layer. Then, the molding cavity descends by the thickness of one powder layer, and the powder spreading roller lays down a new layer of powder in the molding cavity, which is then melted and formed by the laser.
- Post-processing: Recycle the remaining powder, separate the substrate from the steel casting using wire electrical discharge machining, remove excess material, and perform heat treatment on the part.
3.2 Laser Remelting Technology
Since SLM technology involves melting powder and then cooling and solidifying it into parts, defects such as pores and microcracks can easily form. Laser remelting technology uses a laser beam with reset parameters to perform secondary or multiple melting on the formed area, causing the already solidified molten pool to melt again. Remelting the pre-sintered metal part is equivalent to performing a heat treatment, effectively improving the forming quality of the part.
4. Simulation Study of SLM Remelting Forming Strategy
4.1 Establishment of Finite Element Model
The three-dimensional finite element model of the SLM remelting strategy established using COMSOL Multiphysics. Along the positive direction of the Z-axis are the substrate and scanning layer, with the substrate dimensions of 1.2 mm × 1.2 mm × 0.2 mm and the scanning layer dimensions of 1.20 mm × 0.80 mm × 0.06 mm. To accurately obtain the evolution of the temperature field during the SLM forming process and meet the mesh quality parameters, a fine free tetrahedral mesh division method with a size of less than 30 μm is used, with a maximum element size of 20 μm and a minimum element size of 2 μm.
4.2 Basic Assumptions and Control Equations
Some assumptions are made for the simulation process: assuming that the molten liquid metal formed by powder melting is an incompressible fluid, and the metal liquid flow is laminar; assuming that heat loss through thermal radiation, convection, and internal conduction only occurs on the formed surface. The metal powder accumulation area can be regarded as a geometric body with consistent material parameters, and the material properties are determined by the porosity and thermal properties of the solid material.
4.3 Remelting Strategy and Process Parameters
When using the solver to calculate the solution, further issues in solving the computational model are addressed by changing the solver configuration. Considering the finer mesh division and shorter time steps of the model, to speed up calculations and reduce the required disk space, the PARDISO parallel direct solver is used for full coupling during the solution process. The above research method is used to simulate the temperature field during the laser selective melting processing of Ti6Al4V alloy, studying the mass and heat transfer processes within the molten pool. To eliminate the influence of certain forming defects on the temperature field, reasonable basic simulation process parameters are selected, as shown in Table 2.
Table 2. Laser Remelting Process Parameters
| Parameter | Value |
|---|---|
| Scanning Speed (mm/s) | 1,000 |
| Laser First Melting Power (W) | 200 |
| Laser Remelting Power (W) | 100 |
| Powder Layer Thickness (μm) | 60 |
| Laser Radius (μm) | 40 |
| Scanning Spacing (μm) | 100 |
4.4 Simulation Result Analysis
The temperature distribution of the laser scanning trajectory in the molten pool at different nodes under various remelting paths.
Without Remelting:
As the number of laser scanning passes increases, the peak temperature of the melt tracks rises by 222 K. Notably, the temperature difference between the second and first passes reaches 202 K. This significant increase can be attributed to the preheating effect from the previous melt track, enabling the powder layer to attain a higher peak temperature during formation. As additional scanning passes are applied, the increase in peak temperature and the area of the molten pool region becomes less pronounced, indicating that heat transfer during the forming process has reached a stable state.
With Remelting Strategies:
Similar to the unremelted strategy, the maximum peak temperature difference still occurs at the starting point for the subsequent three remelting paths. However, the incorporation of remelting strategies exhibits distinct advantages in temperature distribution and stability.
- Unidirectional Remelting Path: Displays a more stabilized temperature distribution compared to the unremelted condition.
- Perpendicular Remelting Path: Also contributes to a more uniform temperature profile.
- Circular (or Zigzag) Remelting Path: Provides the most stable temperature distribution among the remelting strategies considered.
To further analyze the melt pool fluctuations during the forming process, the temperature standard deviations for different remelting paths were measured and statistically analyzed.
It is evident that the average temperature standard deviations for the unremelted, unidirectional remelted, perpendicular remelted, and circular (or zigzag) remelted strategies are 137.48 K, 107.84 K, 110.44 K, and 110.00 K, respectively. Compared to the unremelted condition, the average temperature standard deviations for the three remelting strategies are reduced by 21.56%, 19.67%, and 19.99%, respectively. This signifies that the temperature standard deviation during the forming process with remelting strategies is only about 4/5 of that without remelting. The reason for this reduction is that the remelting process targets initially formed metal solids, which results in a more stable process compared to directly melting metal powder, thus reducing melt pool fluctuations.
In summary, the simulation results demonstrate that the incorporation of remelting strategies significantly stabilizes the temperature distribution and reduces fluctuations within the molten pool during the laser-based additive manufacturing of steel castings. This stability is crucial for achieving high-quality repairs and enhancing the overall performance of the finished components.