Digital Twin Modeling for Smart Injection Molding
Abstract
:1. Introduction
- The approach for capturing and synthesizing data from multiple sources (e.g, sensors, systems (Enterprise Resource Planing (ERP) and documents) within the injection molding process.
- The impact of integrating AI, specifically Case-Based Reasoning (CBR) and intelligent documentation, on predictive and real-time analytics.
- The role of AM in rapidly prototyping mold designs and making and refining the DT model for greater agility and precision.
- The broader implications of these integrations in steering the injection molding industry toward the innovations promised by Industry 4.0.
2. Basics of Injection Molding Process
3. Knowledge-Based Digital Twin
3.1. Advanced Technologies
3.2. DT Modeling
- -
- IMM, AM, and the ERP/IDMS model.
- Initialization: The algorithm begins with the existing state of the virtual model, denoted as , which represents the most current validated state of the injection molding process.
- Knowledge Extraction: Using a function f, the algorithm processes various types of input data—such as sensor data (), process data (), 3D printing mold data (), material data (), and machine setting parameters ()—to extract relevant knowledge (). This knowledge encapsulates critical insights about the current state of the molding process.
- Model Update Computation: An update function takes the extracted knowledge along with technology updates () and insights from the IDMS () to calculate the necessary updates to the virtual model. This computation reflects changes in the physical process that need to be mirrored in the digital realm to maintain the DT’s accuracy.
- Virtual Model Update: The virtual model is updated to a new state, , by integrating the computed updates. This step ensures that the virtual model remains a faithful and updated replica of the physical injection molding process.
- Output: The final step returns the updated virtual model, , which is now ready for further analysis, simulation, and optimization to support improved decision making and operational efficiency.
Algorithm 1 Update Virtual Model for Injection Molding DT |
- 1.
- Initialization of the Virtual Model:
- -
- The process begins with the current state of the DT model (), representing the operational status of an injection molding machine.
- 2.
- Knowledge Extraction:
- -
- Sensors on the machine gather real-time data () on temperature, pressure, and cycle times.
- -
- Process data () provide insights into production efficiency, material usage, and operational trends.
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- The machine’s recent design changes, facilitated by Additive Manufacturing (), are incorporated into the DT to reflect updated mold configurations.
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- Material data () detail the properties of plastics used, influencing processing parameters.
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- Machine setting parameters () and updates from new technologies () are integrated to keep the model current.
- -
- The system synthesizes these data () to form a comprehensive understanding of the machine’s performance and potential improvements.
- 3.
- Compute Model Update:
- -
- Given the clarification on the role of the IDMS focusing on managing documents like invoices and delivery notes to integrate ERP functions, and the emphasis on material properties monitoring and machine performance through the fault detection system, the algorithm calculates necessary model adjustments () based on the extracted knowledge, considering insights from the Intelligent Document Management System ().
- 4.
- Update Virtual Model and Fault Detection:
- -
- The DT model () is updated, reflecting the latest operational state.
- 5.
- Proactive Operational and Fault Management:
4. Illustration of the Proposed Modeling
4.1. Faults Detection
- Sensor Integration: Equipping the injection molding machines with sensors to collect relevant operational data.
- System Calibration: Setting baseline parameters and performance metrics based on historical data to enable accurate anomaly detection.
- Case Base Development: Designing and developing case base and similarity measurements/algorithms to identify patterns and predict faults based on the collected data.
- Testing and Validation: Conducting trials to test the system’s accuracy and reliability in detecting faults and predicting failures, using a specific case, such as dripper production, as a pilot area for implementation.
- Integration with DT: Embedding the fault detection system within the DT framework to facilitate continuous monitoring, analysis, and optimization of the injection molding process.
Listing 1. Example of temperature/pressure sensor reading. |
Listing 2. Annotated temperature/pressure sensor reading. |
4.2. Additive Manufacturing
4.3. Horizontal and Vertical System Integration
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- Networking between production sites.
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- Integration of the user into the processes.
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- Information exchange throughout the entire value-added process.
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- Intelligent system communication in different departments.
- -
- Networking within the company from the production to the field level.
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- IT systems communicate at all levels.
- -
- Third-party suppliers and our system for the recording of invoices (e.g., invoices from different service providers).
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- API for conversion of different file types. Thus, there are many use cases for which this system can be further developed for other input and output data types.
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- Bidirectional integration between text files and AI engine (training and testing).
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- ERP system integration: Direct linkage with ERP systems to synchronize financial, operational, and resource management data, allowing for a unified view of business processes and enabling more informed decision making.
- -
- Real-time analytics and dashboard integration: Incorporates real-time data analytics and customizable dashboards, providing stakeholders with immediate insights into operational performance, financial metrics, and other key business indicators.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Parameters | Values | Parameters | Values |
---|---|---|---|
Printing duration | 11 h | Material | Rigid 10 K Resin |
Infill density | 100% | Layer height | 50 micron |
Build angle | 45° | Support touchpoint size | 0.5 mm |
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Nasiri, S.; Khosravani, M.R.; Reinicke, T.; Ovtcharova, J. Digital Twin Modeling for Smart Injection Molding. J. Manuf. Mater. Process. 2024, 8, 102. https://doi.org/10.3390/jmmp8030102
Nasiri S, Khosravani MR, Reinicke T, Ovtcharova J. Digital Twin Modeling for Smart Injection Molding. Journal of Manufacturing and Materials Processing. 2024; 8(3):102. https://doi.org/10.3390/jmmp8030102
Chicago/Turabian StyleNasiri, Sara, Mohammad Reza Khosravani, Tamara Reinicke, and Jivka Ovtcharova. 2024. "Digital Twin Modeling for Smart Injection Molding" Journal of Manufacturing and Materials Processing 8, no. 3: 102. https://doi.org/10.3390/jmmp8030102