Robot posture generation method for laser melting deposition

Purpose This paper aims to provide a posture generation method of robot deposition paths based on intersection topology, which is helpful to contribute to improving the flexibility and deposition capability of the deposition system. Design/methodology/approach Via the geometry information and normal vector information of the stereolithography (STL) model, the intersecting edge information is generated and the topological relationship of the model is established. Through the removal of redundant points for the STL model and the sort of robot path points, the position information of robot path points is obtained. According to the geometric relationship between the normal vector information of the STL model and the robot deposition path points, combining with the robot posture representation method of roll-pitch-yaw angles, the posture information of path points is achieved. Then, the generation from CAD model of parts to robot paths for laser melting is realized, and the experimental verification is carried out. Findings For simple parts, the laser melting process can be completed without the posture information of deposition paths. However, in the melting process of a turbine blade, there are some accumulated burls on the sidewall. The posture generation method of robot deposition paths based on the intersection topology can solve this problem. The light spot of deposition points irradiates on the surface of the forming part, and the forming process can proceed smoothly. Practical implications As a motion platform in laser melting deposition (LMD), the application of the multi-joint robot can improve the flexibility and deposition capability of the deposition system, as well as promote the LMD application for individuation manufacturing, parts repair and green remanufacturing. Originality/value The posture is essential for robot deposition paths. This paper first proposes a posture generation method of deposition paths for LMD to improve the flexibility and deposition capability of LMD systems.

DICOM segmentation and STL creation for 3D Printing: A Process and Software Package Comparison for Osseous Anatomy

Background: Extracting and three-dimensional (3D) printing an organ in a region of interest in DICOM images typically calls for segmentation as a first step in support of 3D printing. The DICOM images are not exported to STL data immediately, but segmentation masks are exported to STL models. After primary and secondary processing, including noise removal and hole correction, the STL data can be 3D printed. The quality of the 3D model is directly related to the quality of the STL data. This study focuses and reports on the DICOM to STL segmentation performance for nine software packages. Methods: Multidetector row CT scanning was performed on a dry human mandible with two 10-mm-diameter bearing balls as a phantom. The DICOM image file was then segmented and exported to an STL file using nine different commercial/open-source software packages. Once the STL models were created, the data (file) properties and the size and volume of each file were measured, and differences across the software packages were noted. Additionally, to evaluate differences between the shapes of the STL models by software package, each pair of STL models was superimposed, with the observed differences between their shapes characterized as the shape error. Results: The data (file) size of the STL file and the number of triangles that constitute each STL model were different across all software packages, but no statistically significant differences were found across software packages. The created ball STL model expanded in the X-, Y-, and Z-axis directions, with the length in the Z-axis direction (body axis direction) being slightly longer than that in the other directions. The mean shape error between software packages of the mandibular STL model was 0.11 mm, but there was no statistically significant difference between them. Conclusions: Our results revealed that there are some differences between the software packages that perform the segmentation and STL creation of the DICOM image data. In particular, the features of each software package appeared in the fine and thin areas of the osseous structures. When using these software packages, it is necessary to understand the characteristics of each.

DICOM segmentation and STL creation for 3D Printing: A Process and Software Package Comparison for Osseous Anatomy

Background: Extracting and three-dimensional (3D) printing an organ in a region of interest in DICOM images typically calls for segmentation as a first step in support of 3D printing. The DICOM images are not exported to STL data immediately, but segmentation masks are exported to STL models. After primary and secondary processing, including noise removal and hole correction, the STL data can be 3D printed. The quality of the 3D model is directly related to the quality of the STL data. This study focuses and reports on the DICOM to STL segmentation performance for nine software packages.Methods: Multidetector row CT scanning was performed on a dry human mandible with two 10-mm-diameter bearing balls as a phantom. The DICOM image file was then segmented and exported to an STL file using nine different commercial/open-source software packages. Once the STL models were created, the data (file) properties and the size and volume of each file were measured, and differences across the software packages were noted. Additionally, to evaluate differences between the shapes of the STL models by software package, each pair of STL models was superimposed, with the observed differences between their shapes characterized as the shape error.Results: The data (file) size of the STL file and the number of triangles that constitute each STL model were different across all software packages, but no statistically significant differences were found across software packages. The created ball STL model expanded in the X-, Y-, and Z-axis directions, with the length in the Z-axis direction (body axis direction) being slightly longer than that in the other directions. The mean shape error between the software packages of the mandibular STL model was 0.11 mm, but there was no significant difference in the shape error across the software packages for the mandible STL model.Conclusions: Our results revealed that there are some differences between the software packages that perform the segmentation and STL creation of the DICOM image data. In particular, the features of each software package appeared in the fine and thin areas of the osseous structures. When using these software packages, it is necessary to understand the characteristics of each.

DICOM segmentation and STL creation for 3D Printing: A Process and Software Package Comparison for Osseous Anatomy

Background: Extracting and three-dimensional (3D) printing an organ in a region of interest in DICOM images typically calls for segmentation in support of 3D printing as a first step. The DICOM images are not exported to STL data immediately, segmentation masks are exported to STL models. After primary and secondary processing, including noise removal and hole correction, the STL data can be 3D printed. The quality of the 3D model is directly related to the quality of the STL data. This study focuses and reports on DICOM to STL segmentation performance for nine software packages.Methods: Multi-detector row CT scanning was performed on a dry human mandible with two 10-mm-diameter bearing balls as a phantom. The DICOM images file was then segmented and exported to a STL file using nine different commercial/open-source software packages. Once the STL models were created, the data (file) properties and the size and volume of each were measured and differences across the software packages were noted. Additionally, to evaluate differences between the shapes of the STL models by software package, each pair of STL models was superimposed, with observed differences between their shapes characterized as shape error. Results: The data (file) size of the STL file and the number of triangles that constitute each STL model were different across all software packages, there was no statistically significant difference were found across software packages. The created ball STL model expanded in the X-, Y-, and Z-axis directions, with the length in the Z-axis direction (body axis direction) being slightly longer than other directions. There were no significant differences in shape error across software packages for the mandible STL model. Conclusions: The different characteristics of each software package were noticeable, such as different effects in the thin cortical bone area, likely due to the partial volume effect, which may reflect differences in image binarization algorithms. Although the shape of the STL model differs slightly depending on the software, our results indicate that shape error in 3D printing for clinical use in the operation of osseous structures.

Converting DICOM to STL for 3D Printing: A Process, and Software Package Comparison

Background : Extracting and three-dimensional (3D) printing an organ in a region of interest in DICOM images typically calls for segmentation in support of 3D printing as a first step. Next, the DICOM images are converted to STL data. After primary and secondary processing, including noise removal and hole correction, the STL data can be 3D printed. The quality of the 3D model is directly related to the quality of the STL data. This study focuses and reports on conversion performance for nine software packages. Methods : Multi-detector row CT scanning was performed on a dry human mandible with two 10-mm-diameter bearing balls as a phantom. The DICOM images file was then converted to a STL file using nine different commercial/open-source software packages. Once the STL models were constructed, the data properties and the size and volume of each were measured and differences across the software packages were noted. Additionally, to evaluate differences between the shapes of the STL models by software package, each pair of STL models was superimposed, with observed differences between their shapes characterized as shape error. Further, deformation caused by reduction in the number of triangles was evaluated. Results : The data size and the number of triangles were different across all software packages. The constructed ball STL model expanded in the X-, Y-, and Z-axis directions, with the length in the Z-axis direction (body axis direction) being slightly longer than other directions. There were no significant differences in shape error across software packages for the mandible STL model. No shape change was observed relative to reduction in the number of triangles. Conclusions : Statistically, no significant differences were found across software packages for size and volume. However, different characteristics of each software package were noticeable, such as different effects in the thin cortical bone area, likely due to the partial volume effect, which may reflect differences in image binarization algorithms. Although the shape of the STL model differs slightly depending on the software, our results indicate that shape error in 3D printing for clinical use in oral and maxillofacial surgery remains within acceptable limits.