Machining Machine Neural Network State Vector Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing double- or single-side processing machines face challenges in quickly and reliably monitoring production processes due to the complexity of machine and processing parameters, leading to delayed adjustments and increased rejects, as expert personnel may not be available to interpret measurement data effectively.
Innovation Solution
Implementing an artificial neural network that processes measurement data from sensors to create a state vector, comparing it to target state vectors for optimal machining results, allowing for quick detection of deviations and automated adjustments to minimize rejects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors monitor machine and processing parameters, then measurement data availability improves, but data interpretation complexity increases
Solution Approach 1:
An artificial neural network serves as an intermediary between the sensors and the control device. The neural network receives measurement data from multiple sensors, automatically interprets the complex parameter relationships, and generates control signals for actuators, eliminating the need for expert manual interpretation while maintaining high monitoring reliability
Solution Approach 2:
The patent replaces the mechanical/expert-based interpretation system with an electronic neural network system. Instead of relying on expert personnel to manually analyze sensor data, the neural network automatically processes and interprets the measurement data, substituting human expertise with an automated electronic system
2Manufacturing precision
If expert personnel interpret measurement data, then adjustment accuracy improves, but response time decreases
Solution Approach 1:
The control device performs self-adjustment by automatically interpreting measurement data through the neural network and actuating control elements without requiring external expert intervention. The system serves itself by maintaining optimal processing conditions through automated feedback control
Solution Approach 2:
The patent implements a closed-loop feedback system where sensors continuously monitor processing parameters, the neural network interprets the data in real-time, and actuators automatically adjust machine parameters to maintain optimal conditions. This continuous feedback loop enables both high accuracy and rapid response time
3Device complexity
If manual monitoring and adjustment of processing parameters is used, then system complexity remains low, but productivity decreases
Solution Approach 1:
The control device autonomously monitors processing parameters through sensors, interprets data via neural network, and actuates control elements without requiring manual intervention. This self-service capability eliminates the need for operator time while maintaining simple overall system architecture
Solution Approach 2:
The neural network provides accelerated processing of measurement data, enabling rapid interpretation and response that significantly increases production throughput compared to manual monitoring, while the modular architecture keeps the added complexity manageable
Data Source
Figure 1~3
Figure 4
Figure 5
AI summary
The invention relates to a double- or single-sided machining machine with a preferably annular first working disk and a preferably annular counter-bearing element, wherein the first working disk and the counter-bearing element can be driven to rotate relative to each other by means of a rotary drive, and wherein a preferably annular working gap is formed between the first working disk and the counter-bearing element for double-sided or single-sided machining of flat workpieces, preferably wafers, wherein the double- or single-sided machining machine comprises a plurality of sensors which, during operation of the double- or single-sided machining machine, acquire measurement data on machine and/or machining parameters of the double- or single-sided machining machine, wherein a control device is provided which receives the measurement data acquired by the sensors during operation of the double- or single-sided machining machine.wherein the control device comprises an artificial neural network configured to generate a state vector of the double- or single-sided machining machine from the measurement data and to compare this vector with at least one target state vector. The invention also relates to a method for operating a double- or single-sided machining machine.