Announcement of the Results

We have established the world's first remote shell precision manufacturing ecosystem based on the "model-defined enterprise" concept - "DigiCore". This system uses the three-dimensional model of the product as the sole data source and runs through the entire process of design collaboration, process simulation, intelligent manufacturing, quality traceability, and performance feedback. By creating a "digital twin" for each shell product throughout its entire life cycle, we have achieved a 30% increase in production efficiency, a 40% reduction in quality costs, and provided customers with seamless services ranging from virtual verification to physical prototypes, from small-batch trial production to large-scale mass production, thus redefining the delivery model for core structural components of high-end medical devices.

Research and Development Background Challenges

In the development of high-end customized remote shells, the low efficiency of medical engineering collaboration is a common pain point. Between medical device companies (clients) and precision manufacturing suppliers, information transmission relies on two-dimensional drawings, PDF specifications, and multiple round trips of emails, which can easily lead to misunderstandings, version confusion, and inability to meet complex geometric tolerances and process requirements. At the manufacturing end, process planning relies on engineers' experience, and the evaluation cycle for the process feasibility (DFM) of new products is long, with high trial-and-error costs. Quality inspection still largely relies on manual and offline three-dimensional coordinate measurement, with isolated data, making it difficult to achieve preventive quality control. Moreover, for the traceability of clinical application effects, once problems occur, it is difficult to quickly identify whether the deviation lies in the design, materials, or manufacturing process. This fragmented and experience-driven model severely restricts the speed of innovation in medical devices and the consistency of quality.

Core Technological Innovation

• Model-based definition and collaborative platform: We require and support customers to directly use the three-dimensional model (in formats such as STEP AP242) containing product manufacturing information as the design input. The model not only includes geometric shapes but also directly embeds all product manufacturing information such as dimensional tolerances, form and position tolerances, surface roughness, and material specifications. Internally, we use this model to conduct manufacturing feasibility analysis, tolerance superposition analysis, tool path programming, and inspection program compilation within the same digital environment. Customers can view the processing simulation, progress status, and preliminary inspection report of their parts in a secure cloud platform, achieving true design and manufacturing collaboration.
• Digital twin of the process chain and virtual debugging: Before physical processing begins, we build a complete "digital twin of the process chain" in the virtual environment. This includes: machine tool dynamics model, tool model, fixture model, blank model, and CNC code. Through virtual processing simulation, collision, overcutting, and undercutting can be predicted and avoided, and cutting parameters can be optimized. Furthermore, we conduct "virtual machine tool debugging", running the generated G-code on the virtual controller of the machine tool to verify its logical correctness. This increases the success rate of the first trial production of new products from the industry average of 60% to over 95%, significantly shortening the delivery cycle.
• Full-process data loop and intelligent quality control: Assign a unique identification code to each production batch and even each part. During the processing, the machine tool status, sensor data, and in-machine measurement results are uploaded in real time to the manufacturing execution system. After processing, the actual three-dimensional point cloud data of the part is obtained through online or offline high-precision measuring equipment (such as optical 3D scanning, coordinate measuring machine). The system automatically compares the point cloud data with the original design model, generates a color deviation chromatogram, and automatically determines whether it is qualified or not. All process data and quality data are associated with the digital twin of the part, forming a complete data archive. Through big data analysis, real-time monitoring of process capabilities, prediction of tool life, and rapid tracing of the root cause of quality issues can be achieved.

Mechanism of Action

The core mechanism of the "DigiCore" ecosystem lies in "driving physical flows with data streams and optimizing the real world through the virtual world". The model-based definition serves as the starting point for digital threads, ensuring the lossless, unique, and traceable transmission of information from design intent to manufacturing requirements. The digital twin of the process chain is the core engine, which "preforms" the entire manufacturing process in the virtual space, exposing and resolving potential issues at a very low cost, placing manufacturing risks before production, and achieving the ideal of "getting things right the first time" in intelligent manufacturing. The full-process data loop is like the nerve endings and the brain, continuously collecting massive data from the production site in real time, and through analysis and feedback, constantly optimizing process parameters, predicting equipment maintenance needs, and improving quality control levels. Ultimately, what is delivered to the customer is no longer an isolated metal part, but an intelligent product package with its complete "digital birth certificate". This digital twin can continue to "survive" in the customer's product lifecycle management system, and be used to guide subsequent assembly, maintenance, and even报废 recycling.

Efficacy Verification

Since the launch of the "DigiCore" system, key operational indicators have significantly improved: The time from data receipt to completing DFM analysis and providing quotations for new projects has been reduced by an average of 50%; the success rate of the first trial production (achieving standards without rework) has reached a leading industry level of 96%; the response and handling time for quality abnormalities during the production process has been shortened by 70%. For customers, their product development cycle can be shortened by an average of 6-8 weeks. In a typical case, a European robotic surgery company developed a new endoscopic instrument. We completed the manufacturability analysis of the complex shell within 72 hours through the platform and provided optimization suggestions. We delivered the fully functional first prototypes within 2 weeks, which was twice as fast as the traditional mode, helping their products enter clinical validation ahead of schedule. The data traceability capability of the platform was demonstrated without any ambiguity in a simulated quality audit. It could locate all relevant production records, operators, equipment parameters, and original test data for a specific size out-of-tolerance batch within 5 minutes.

Research and Development Strategy and Philosophy

Our strategy is "Digital transformation is not an option, but a way of survival". We believe that for high-end precision manufacturing, the future competition is not only a competition in machine tool accuracy, but also a competition in data-driven capabilities. We are committed to becoming a "software-defined" manufacturing enterprise, converting our decades of accumulated process knowledge and technical know-how into algorithms, models, and digital processes. Our philosophy is: The best service is to ensure that customers' innovation paths are unobstructed. Therefore, we invest in digital infrastructure not for show, but to eliminate collaboration barriers, shorten the innovation cycle, ensure delivery quality, and ultimately enable doctors and patients to access the most advanced medical technologies faster and more safely. We are the customer's extended R&D and manufacturing department, and the most reliable and intelligent link in their innovative supply chain.

Future Outlook

In the future, this ecosystem will evolve into a "cloud intelligent factory" and an "industry collaboration network". We are exploring the possibility of making some process simulation and optimization capabilities available to customers' design engineers in the form of cloud services, enabling them to obtain real-time manufacturability feedback during the design process. At the same time, based on blockchain technology, a trustworthy supply chain quality collaboration network will be established, integrating material suppliers, heat treatment plants, surface treatment plants, etc. into the same digital thread to achieve transparent and traceable quality throughout the entire supply chain. The grander vision is to combine artificial intelligence and additive manufacturing to achieve the ultimate model of "demand-driven, automatic generation, and direct manufacturing": customers input functional requirements and spatial constraints, AI automatically generates the optimal shell design and simultaneously generates manufacturing instructions, and the additive manufacturing equipment directly produces the final parts, reducing the delivery time for high-end customization from several weeks to several days, truly realizing the "on-demand manufacturing" of medical devices.