Robot Process Quality Assurance Using Machine-Learned Virtual Sensing
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Solution Overview
Problem
Existing robot-supported processes face challenges in quality assurance, particularly in reliability, speed, and range of application, due to limitations in measuring and improving process quality effectively.
Innovation Solution
A method utilizing a machine-learned process model to classify and adjust process success, incorporating robot-specific data from sensors, enables improved quality assurance by classifying processes into discrete or continuous success categories, allowing for real-time adjustments and reducing manual data selection, thereby enhancing reliability and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality assurance methods are used for robot-assisted processes, then the system is simpler to implement, but the reliability and accuracy of quality assessment deteriorates
Solution Approach 1:
The patent replaces traditional mechanical measurement systems with a machine learning-based virtual sensor model. The system uses data from existing robot sensors (encoders, current sensors) to train a neural network that virtually measures process quality without physical measurement devices. This substitution maintains reliability while reducing device complexity.
Solution Approach 2:
The patent creates a virtual copy of the measurement function through machine learning. Instead of physical sensors directly measuring quality parameters, the system trains a model that copies the measurement capability by learning from sensor data and process outcomes. This virtual copying enables accurate quality assessment without additional physical measurement hardware.
2Productivity
If manual selection of process data is used, then the system is easier to configure, but the speed and efficiency of quality assurance deteriorates
Solution Approach 1:
The system performs self-configuration through automated machine learning model training. The neural network automatically identifies relevant process parameters and selects optimal data sources during the training phase, eliminating the need for manual data selection. The system serves itself by learning which sensor data correlates with quality outcomes, thereby increasing speed without sacrificing ease of operation.
3Measurement precision
If additional sensors are deployed for quality measurement, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical measurement sensors with a virtual sensor implemented as a machine learning model. The neural network processes data from existing robot control sensors to predict quality parameters, achieving measurement precision without adding physical measurement devices. This substitution eliminates the need for additional sensors while maintaining or improving measurement accuracy.
Solution Approach 2:
The system makes existing robot sensors serve multiple functions: both robot control and quality measurement. By training the machine learning model to interpret data from standard robot sensors (encoders, current sensors), the system enables these sensors to provide both motion control information and quality assessment information, eliminating the need for dedicated measurement sensors.
4Adaptability or versatility
If extensive process data is collected and processed, then the scope of application improves, but computing time increases
Solution Approach 1:
The system performs preliminary data processing during the model training phase. The neural network is trained offline on extensive historical process data, learning patterns and relationships in advance. During actual quality assurance operations, the pre-trained model quickly processes new data without requiring extensive real-time computation, thereby expanding application scope while minimizing computing time delays.
Data Source
AI summary
The invention relates to a method for providing quality assurance of a process comprising the following steps repeated a plurality of times: - executing (S10) the process; - detecting (S10) learning process data during this execution of the process; and - detecting (S20) a learning evaluation of the executed process; the step: - machine-learning (S30) a model (10) of the process on the basis of the detected learning process data and learning evaluations; and the steps, in particular repeated a plurality of times: - renewed execution (S50) of the process; and - detecting (S50) current process data during this renewed execution of the process; wherein, on the basis of the current process data and the machine-learned model (10) - a success of the newly executed process is determined, in particular classified (S60), and/or - the newly executed process is modified (S60).