A Study on Monitoring Technology for Improving the Efficiency of Metal 3D Printing
Article information
Abstract
Abstract
In this study, a process monitoring approach was developed to collect and analyze in-situ data during the Ti64ELI laser powder bed fusion (PBF) process. Real-time images of each layer and corresponding energy mapping data were obtained using a built-in high-speed camera system. The results demonstrated that excessive heat input regions, observed as red dots in energy maps, were associated with over-melting and structural collapse in the upper layers. Mechanical testing of six specimens fabricated under identical process parameters (laser power: 450 W, scan speed: 1,500 mm/s, layer thickness: 50 μm) showed average yield strength of 906 MPa, ultimate tensile strength of 997 MPa, and elongation of 12.3%, consistent with the typical range for Ti64ELI additively manufactured alloys. However, specimens with locally concentrated energy input exhibited reduced elongation due to uneven heat distribution. These results confirm that energy distribution and geometric instability during the AM process have a significant effect on mechanical properties. The captured layer-wise images and energy mapping data were studied as monitoring technology capable of analyzing defects that may occur during the process and predicting quality in the 3D printing process.
1. Introduction
Metal Additive Manufacturing (AM) is a next-generation manufacturing technology capable of high-precise fabrication of metal parts with a complex shape by using a laser or electron beam to deposit metal powder in layers1). The adoption of metal AM is consistently expanding2) owing to increased demands for high-functional components in various fields, including the aerospace, automobile, medical, and energy industries. Compared to conventional manufacturing processes, AM provides the advantages of flexibility in process design, lightweight components, and single-part production, but it also entails the issue of variations in quality under the same conditions due to process complexity and inherent nonlinearity3). Quality instability becomes one of the greatest barriers to expanded industrial applications and product reliability.
In the AM process, several variables such as the melt pool temperature, laser output, deposition speed, and powder thickness directly influence the microstructure and mechanical properties4), and these variables interact with one another to form complex patterns. Therefore, for stable control of the metal AM process and to achieve decent quality, it is necessary to develop deposition-based process monitoring technology to collect and analyze process-related data in real time.
In recent years, research has been conducted on monitoring technology for predicting process quality and analyzing defects using process data5). For example, analyses have been conducted on surface defect detection using high-speed cameras, infrared sensors, and optical sensors, and on correlation analysis between energy input and shape defects6). However, it is challenging to accurately predict quality using 3D shape data, deposition process images, and energy mapping data since various 3D printing process variables are taken into consideration7). Therefore, this study attempted to control the deposition material, laser output, and speed to the greatest possible extent, and analyzed the mechanical and chemical properties of actual deposition samples using deposition image captured using internal cameras during the process and the energy mapping data.
2. Experimental Materials and Methods
2.1 Experimental Materials
This study used a titanium alloy from TENKA as the powder for metal AM. Fig. 1 shows the average particle size of metal powder (Ti64ELI), while Table 1 provides the chemical composition of the metal powder. To ensure the reliability of the titanium metal powder which is sensitive to oxygen content8), the oxygen and hydrogen contents were analyzed using the N/O/H equipment from LECO as shown in Fig. 2.
Analysis of metal powder (a) shape, (b) particle size
3D printing metal powder(Ti64ELI) chemical component(wt.%)
Analysis of metal powder N/O/H, (a) New, (b) Used
For the deposition process, a laser-based PBF metal 3D printer (Metal300 model) developed by CY Autotech was used based on CAD-based 3D modeling. Inside the equipment, a high-speed camera from OMRON is installed to collect the data during the process, along with GigE Vision, which is for energy mapping analysis. Fig. 3 shows the actual image and schematic diagram of the equipment used in this study. This particular equipment enables real-time data, including laser output, scanning path, and deposition image during deposition, to be acquired simultaneously. Fig. 4 provides the image of the actual deposition sample (diameter: 45 mm, height: 100 mm) example of energy mapping data.
Example of the (a) Metal 300(CY Autotech社), (b) Monitoring System, and (c) Camera Installation Image
Example of (a) process image and (b) energy mapping image (Sample size: 45 mm diameter)
2.2 Experiment method
The lamination process was performed by slicing the CAD model to generate a path by layer. The process conditions are set to laser power of 450 W, scanning speed of 1,500 mm/s, and layer thickness of 50 μm as shown in Table 2. All deposition processes were performed under an inert gas (Ar) atmosphere to prevent oxidation of titanium. The images of each layer were collected in real time through the internal camera during the process, which were utilized to determine surface condition and defects. The process monitoring is detailed in Fig. 5.
3D printing process parameter
Detailed process monitoring methods
The process can be controlled from a computer using the 3D printing equipment and through Ethernet communication. Real-time analysis is enabled by inspecting the processing status and powder application using a layer inspection camera, and by saving the processing light intensity (melting) inspection data from a photodiode. The manufacturing process data for predicting the deposition quality are acquired from the collected deposition images and energy mapping data.
To analyze the mechanical properties of the sample based on 3D printing process variables, the tensile characteristics were analyzed using the 8874 universal testing model from Instron.
3. Experiment Results and Discussion
3.1 Securing AM process monitoring data
In this study, the Ti64ELI specimen underwent the deposition process based on a CAD 3D model, and the deposition process was captured using an internal camera. Through the deposition monitoring process, data helpful for analyzing defects at different locations in the actual deposition specimen were obtained. Fig. 6 illustrates the actual model as well as the image of the actual deposition specimen. The sample size of the design data was set to a diameter of 45 mm and a height of 100 mm. Compared to the actual deposition sample, the Z-axis height had almost no error, but the diameter had an error of 0.2 mm. Post-cooling shrinkage seems to have occurred; to minimize the error range of the deposition sample, the design data need to be modified or revised by considering characteristics of the metal, such as thermal shrinkage.
Layer-by-Layer CAD data and corresponding in-situ build image, (a) early layer, (b) middle layer, (c) final layer
When the deposition image of each layer was analyzed, the last layer showed a distorted shape compared to the CAD data. By utilizing real-time monitoring technology for deposition samples to analyze defects in the manufacturing process, defect data can be collected and optimized process parameters can be derived. The analysis results can be used as indicators of geometric reproducibility and serve as a basis for process control and quality prediction9).
3.2 Obtaining energy mapping monitoring data
In this study, an internal camera was used during the metal AM process to capture the deposition process of each layer in real time, and energy mapping analysis was performed for each layer. Fig. 7 shows the energy mapping results for early, middle, and final layers of deposition, where red dots indicate the areas where energy is concentrated. The images were automatically captured when each layer was completed, and process stability was evaluated by analyzing the changes in brightness distribution over time.
Layer-by-Layer energy mapping data, (a) early layer, (b) middle layer, (c) final layer
As shown in Fig. 8, the parts with concentrated laser energy are marked with red dots, indicating that excessive heat input can result in overmelting. In addition, the light green dots represent the parts that have a possibility of incomplete melting or partial fusion of the powder due to low heat input. Real-time image analysis enables prompt detection of these defects that can occur during the deposition process. For example, energy mapping analysis in a specific layer can be interpreted as a signal for local defects caused by unstable laser power or uneven powder distribution. Furthermore, heat input does not occur evenly at the boundary between layers, which can result in deformation or residual stress.
Energy mapping data
These results signify that process monitoring using an internal camera is effective for real-time quality management of metal AM processes. Deposition images taken in real time and energy mapping data were confirmed to be useful for analyzing defects and predicting process stability in relation to energy input.
3.3 Evaluating mechanical properties for process optimization
In this study, the mechanical properties were evaluated for the Ti64ELI specimens fabricated under single process conditions (laser power of 450 W, scanning speed of 1,500 mm/s, layer thickness of 50 μm, and hatch spacing of 110 μm). A total of six specimens were fabricated under the same conditions; the correlation between the changes in shape during the process and the final mechanical properties was analyzed using the energy map and the deposition images obtained during the process.
The tensile test was performed according to the ASTM E8 standard, using the 8874 universal testing model from Instron. The yield strength, ultimate tensile strength, and elongation were measured for each specimen, and the average value of three repeated measurements was calculated as the result. The test results are shown in Table 3 and Fig. 9.
3D printing metal powder(Ti64ELI) chemical component(wt.%)
Correlation between the energy map and in-situ monitoring images
The tensile test showed that the fabricated specimens had an average yield strength of around 906 MPa, an ultimate tensile strength of 997 MPa, and an elongation of 12.3%, which are similar to the properties of conventional Ti64ELI deposition material10). However, specimens 5 and 6, fabricated under the same conditions, demonstrated superior results for yield strength and ultimate tensile strength compared to the other specimens, and they recorded the highest and lowest elongation, respectively. As shown in Fig. 8, it can be related to heat concentrated (red dot) areas found in energy map images. Energy mapping in Fig. 10 also shows that energy is concentrated in the middle layer of deposition in specimen 6. Uneven heat input can be considered to contribute to the decreased elongation compared to the other specimens, which indicates that it can directly affect mechanical properties.
Results of energy mapping analysis of the tensile specimens
Therefore, the findings of this study signify that energy distribution and shape instability occurring during deposition can have a significant impact on the final mechanical properties, even under identical process conditions. These physical data can be linked with manufacturing process monitoring technology in the future to be utilized for real-time defect analysis or quality prediction.
4. Conclusion
This study obtained real-time processing data during the metal AM of Ti64ELI and examined a manufacturing process monitoring technology to analyze changes in the quality of the deposition process.
1) Deposition images of each layer were collected in real time using the internally mounted high-speed camera, through which data were obtained to predict shape changes and defect locations during deposition. Shape distortion and uneven melting were observed in the final layer.
2) The energy map obtained during the deposition process was used to visually identify local overmelting and undermelting regions caused by unstable laser output or uneven distribution of powder. It showed that uneven distribution of heat input during the process can result in early defects.
3) The tensile test of the specimens fabricated under the single processing condition (laser power of 450 W, scanning speed of 1,500 mm/s, layer thickness of 50 μm) showed the average yield strength of 906 MPa, ultimate tensile strength of 997 MPa, and elongation of 12.3 %. However, some of the specimens showed heat concentration (red dot) areas, which are considered to have significantly affected elongation.
4) Real-time process monitoring data can be utilized in the future to acquire technological data related to defect detection and quality prediction during processing.
Overall, this study verified that laser energy distribution and shape instability can have a significant impact on final mechanical properties even under single process conditions. Moreover, real-time manufacturing process monitoring technology was also confirmed to be applicable for quality stabilization and predictive control of metal AM processes.
Acknowledgement
This research was supported by “Support to strengthen the ecosystem of service-customized automotive parts industry,” “Development of a metal 3D printed additive manufacturing based silencer for firearms,” and “Metal 3D printing industry technology advancement and technology support platform development”, This research, undertaken at Kyungpook National University, was supported by the Regional Innovation System & Education (RISE) program through the Daegu RISE Center, funded by the Ministry of Education (MOE) and the Daegu Metropolitan City, Republic of Korea.