Machine Tool Digital Twin Architecture for Multi-User Processing
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Solution Overview
Problem
Existing systems for digital twins of machine tools are not suitable for efficient use in multi-user environments due to high computing and storage requirements, exacerbated by the increasing complexity and precision of machine tools, which demand massive memory and computing capacity.
Innovation Solution
A cloud-based service platform with a front-end and back-end layer is used to process and manage digital twins, where the digital twin is created only upon receiving an initial instruction from a client, optimizing resource use by minimizing data exchange and processing, and allowing real-time visualization and interaction through a central processing platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a digital twin of a machine tool is created with high precision and complexity to simulate physical behavior accurately, then simulation precision is improved, but memory requirements and computing capacity increase massively
Solution Approach 1:
The system segments the digital twin data into different layers: a compressed master model stored in a database and detailed slave models generated on-demand for specific simulation tasks. This segmentation allows the system to maintain high simulation precision when needed while reducing overall memory requirements through selective data loading and compression.
Solution Approach 2:
The system performs preliminary compression and storage of the master model data in a database before actual simulation needs arise. This preliminary action reduces the memory footprint of the complete digital twin while preserving the ability to reconstruct high-precision slave models when simulation accuracy is required.
2Measurement precision
If a digital twin of a machine tool is created with high precision and complexity to simulate physical behavior accurately, then simulation precision is improved, but computing capacity requirements increase massively
Solution Approach 1:
The system divides computing resources into a master model processor for data management and multiple slave model processors that generate detailed simulations only when needed. This segmentation allows high computing capacity to be allocated dynamically to specific simulation tasks rather than being continuously consumed by maintaining a complete high-precision model in memory.
Solution Approach 2:
The master model is pre-processed and stored in a compressed format in the database, eliminating the need for continuous high-power computation to maintain the complete model. Computing capacity is consumed only during on-demand generation of slave models for specific simulation scenarios.
3Adaptability or versatility
If the digital twin is made available in a multi-user environment for simultaneous access, then adaptability is improved, but resource contention and processing efficiency deteriorate
Solution Approach 1:
The system segments access rights and model instances by user, with each user receiving a personalized slave model derived from the master model. This segmentation allows multiple users to access the digital twin simultaneously with different levels of detail and focus areas, improving adaptability while maintaining processing efficiency through dedicated model instances.
Solution Approach 2:
The system creates copy instances (slave models) of the master model for different users and applications. These copies allow simultaneous multi-user access without contention, as each user works with their own instantiated version while the master model remains a compact reference in the database.
4Measurement precision
If complete digital twin data is transmitted to client terminals for visualization, then visualization quality is improved, but data transmission time and network bandwidth increase
Solution Approach 1:
The system extracts only the necessary portions of the digital twin data from the master model and transmits them to client terminals as needed. This extraction approach maintains visualization quality by providing relevant detailed data while minimizing transmission time and network bandwidth usage by avoiding transmission of complete model data.
Solution Approach 2:
The master model is pre-compressed and stored in a compact format in the database, and only required portions are decompressed and transmitted on-demand. This preliminary compression action significantly reduces data transmission time while preserving visualization quality for the transmitted portions.
Data Source
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AI summary
For the computationally efficient processing of a digital twin of a machine tool, a computer-implemented method for processing a digital twin of the machine tool in a multi-user environment is proposed. A central processing platform, connectable to multiple clients (C), is proposed. This cloud-based service platform comprises a front-end layer (FS) and a back-end layer (BS). The method includes the following steps: in the back-end layer (BS), a digital twin of a selected machine tool is created after receiving an initial instruction from the first client (C) via the front-end layer (FS). The initial instruction includes the configuration information of the selected machine tool. The digital twin is then created based on this configuration information.Subsequently, output data from the created digital twin is transferred to one or more clients C via the frontend layer FS.