CNC Data Client for Real-Time Capture and Contextualized Transfer
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Solution Overview
Problem
Current embedded systems in industrial manufacturing, such as CNC machines and industrial robots, lack the computational resources, memory, and communication bandwidth to efficiently capture and transfer large amounts of process-related data for Big Data analytics, leading to limited data access and inadequate data analysis capabilities.
Innovation Solution
A client device is introduced to record and preprocess process-related data from CNC machines or industrial robots, featuring multiple data communication interfaces for real-time and non-real-time data channels, data mapping capabilities, and secure internet communication, allowing for the transfer of contextualized data to cloud platforms for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If embedded systems in CNC machines or industrial robots are used to capture and transfer process-related data for Big Data analytics, then data access is limited due to restricted computational resources, memory, and communication bandwidth, but adding more resources would increase device complexity and cost
Solution Approach 1:
The patent extracts the data capture and pre-processing functions from the embedded control system to a separate client device. The client device is configured to continuously capture process-related data from the CNC machine or industrial robot via data interfaces, and performs pre-processing operations including contextualization, filtering, and aggregation. This separation allows the control system to maintain its primary real-time control function while the client device handles the additional data analytics workload, thus increasing data quantity without significantly increasing the complexity of the control system itself.
2Quantity of substance
If embedded systems capture large amounts of process-related data for Big Data analytics, then data transfer capability is insufficient due to limited communication bandwidth, but increasing communication infrastructure would increase device complexity
Solution Approach 1:
The client device performs pre-processing operations on the captured data before transfer to the cloud platform. This includes contextualization (adding metadata and timestamps), filtering (removing redundant or irrelevant data), and aggregation (combining multiple data streams). By performing these actions preliminarily at the data source, the volume and complexity of data requiring transmission over the communication network is reduced, allowing efficient use of existing communication bandwidth without requiring substantial infrastructure upgrades.
3Quantity of substance
If embedded systems are used for both real-time control and Big Data analytics, then data completeness and integrity are improved, but processing speed and real-time performance deteriorate
Solution Approach 1:
The system is segmented into distinct functional components: the embedded control system that maintains real-time control operations, and the client device that handles data capture, pre-processing, and transfer. This segmentation allows each component to optimize for its specific function - the control system maintains high-speed real-time processing while the client device handles the computationally intensive data analytics tasks. The client device captures data through interfaces that do not interfere with the control system's processing speed, thus maintaining both data completeness and real-time performance.
4Measurement precision
If more data is captured and transferred for analysis, then data analysis accuracy is improved, but computational burden on the embedded system increases
Solution Approach 1:
The client device serves as an intermediary between the embedded control system and the cloud-based data analysis platform. It captures comprehensive process-related data including control commands, process parameters, and sensor data, performs pre-processing to contextualize and filter the data, then transfers the processed data to the cloud platform for advanced analytics. This intermediary role allows the system to achieve high data analysis accuracy through comprehensive data collection while minimizing the computational burden on the embedded control system, as the intensive processing is performed by the client device and cloud platform rather than the resource-constrained controller.
Data Source
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AI summary
The present invention relates to a client device (1) and a system (100) for data acquisition and pre-processing of process-related mass data from at least one CNC machine (10) or an industrial robot and for transmitting said process- related data to at least one data recipient (10), e.g. a cloud- based server, the client device (1) comprising at least one first data communication interface (2) to at least one controller (11) of the CNC machine (10) or industrial robot, for continuously recording hard-realtime process-related data via at least one realtime data channel (7), and for recording non-realtime process -related data via at least one non- realtime data channel (8). The client device (1) further comprises at least one data processing unit (5) for data- mapping at least the recorded non-realtime data to the recorded hard-realtime data to aggregate a contextualized set of process -related data. Moreover, the client device (1) comprises at least one second data interface (3) for transmitting the contextualized set of process-related data to the data recipient (20) and for further data communication with the data recipient (20).