The emergence of this new technology not only makes dentists’ work more efficient and precise but also allows them to better meet the needs of patients. In the future, the application of this technology will be further expanded to more fields and create more practical solutions.
Although 3D printing technology and machine learning technology are emerging technologies, it is the development of these technologies that have made breakthroughs in improving efficiency, reducing costs, and improving quality. These technologies allow people to focus more on what they do best, making the production process faster, easier, and more efficient.
3D-printed partial denture holder
In recent years, with the continuous advancement of science and technology, manufacturing technology has also been constantly innovating, and some amazing technologies have emerged. Among them, the metal 3D printing technology of the RPD bracket is one of them. Not only is the workflow simpler than previous manual casting, but it is also able to produce more precise products and meet aesthetic expectations.
In some medical labs, metal 3D printing technology has become mainstream, replacing the previous manual casting. This technique allows computer-aided design to input the bracket model required for the design directly into the printer for printing. Due to the high precision and stability of the printer, the RPD stent manufactured by metal 3D printing technology can fit the patient’s oral shape more accurately, thereby improving the patient’s comfort and use effect.
In addition, the RPD bracket made using 3D printing technology is also more robust and durable. Each brace can be precisely tailored to the patient’s needs to ensure tooth stability and chewing function. Compared with traditional manual casting, metal 3D printing technology can greatly reduce the defect rate of products, thereby improving the efficiency of use and reducing costs. That said, 3D printing also requires a lot of work from dental technicians, a large part of which involves repetitive tasks. This is inevitably reminiscent of support structures.
While support may present some challenges, they are also one of the keys to the success of 3D printing. The use of supports ensures that the printed object remains stable, reducing the possibility of distortion and distortion. In addition, the use of supports protects the printer’s nozzle from excessive residues of the material.
As technology evolves and software improves, print support will become more intelligent, increasing print efficiency and reducing the need for post-processing.
Some software companies claim to offer solutions. They propose standard support strategies that can be applied to dental parts and industrial parts. This will enable the dental technician to automatically generate the correct support.
This is only partially accurate. While these standard strategies do take into account the overhang and position of the part, this level of automation does not provide satisfactory results for RPD brackets.
Although the amount of support required to generate RPD scaffolds is large, with effort and expertise by technicians, this cost can be reduced by adjusting the automatically generated supports. With a highly motivated attitude and professional skills, they spend a lot of time and effort every day working to optimize the manufacturing process to the best possible condition. Under such conditions, we can be confident that RPD stents will be manufactured with high quality and also bring better results for our oral health.
AI powers 3D printing
A simpler solution to support generation lies in feature partitioning.
The density and number of supports depend on the area of the RPD bracket, so the task that needs to be automated is to identify all areas of the bracket, which we call partitioning. RPD automatic segmentation is a good example, through artificial intelligence analysis of data, can make the segmentation accuracy of local dentures reach the same level as dental technicians, greatly shortening the production time and improving work efficiency.
At the same time, the use of this technology can also reduce the pressure on labor costs, and laboratories can focus more on professional technology and research and development. This is also a huge step forward for the industry as a whole to better meet the needs and requirements of its customers. “RPD segmentation is a great example of AI working in tandem with other geometry-based technologies.” “The core of this system is based on artificial intelligence. It can reliably identify different areas such as the inside of the snap ring, the outside of the snap ring, the connector, the edge of the connector, the retention net, and so on. However, this does not result in a very clean segmentation and is of limited use. It needs to be complementary to other approaches. ”
Since partial dentures need to fit each person’s anatomy, they vary greatly. Variability is a good candidate for generalization – and that’s where AI excels.
The first step is to train the AI model on an existing partitioned RPD so that it can generalize and predict partitions of previously “unseen” samples.
This results in a model that identifies certain areas of the RPD frame, but requires more nuance. “After training, the AI can predict the basic region of RPD,” “However, there are other regions and subregions that require different support strategies, so the AI results are also extended to account for these issues.” ”
Additional areas are essential. Buckles need to fit well, which means they need to be printed very precisely and require the right support strategy. The orange area needs support, and the red part inside should not have any support at all.
To scale AI predictions, the team used a geometric-based technique called “zone growth,” combined with post-processing pipelines, to eliminate small errors in predictions. This automatic segmentation strategy identifies all relevant regions.
The broader potential for automated 3D model segmentation
Automatic segmentation is a feature developed for 3D-printed partial dentures, but its potential extends beyond this area. Splitting any part into its constituent features is useful for many downstream applications.
In the manufacturing industry, it is very important to improve production efficiency, and automatic segmentation technology can help enterprises achieve efficient production. By automating CNC milling and sorting, you can reduce the error rate of manual operations, increase work efficiency, and reduce production costs at the same time. The professional artificial intelligence team is committed to continuously promoting the research and practice of this method to further improve the production efficiency of the manufacturing industry.
The application of automatic segmentation technology can improve the efficiency and economic benefits of the manufacturing industry, while also creating more jobs. Therefore, we should actively promote this technology to benefit more businesses and individuals. Let’s work together to promote efficient and sustainable manufacturing!
