Press Quality Sampling Using Material and Production Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for monitoring component quality in motor vehicle production using presses are inefficient due to time-consuming and costly measurements, and the difficulty in integrating quality control into the production process, leading to incomplete data collection and suboptimal process control.
Innovation Solution
A method that selects samples based on material and production parameters using an electronic computing device, employing sensors and machine learning algorithms to detect component properties and deviations from target values, enabling automated quality control and process optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous monitoring of all components is performed, then measurement precision and reliability are improved, but productivity and cost increase due to time-consuming and costly measurements
Solution Approach 1:
The patent applies partial action by performing measurements on only a selected subset of components rather than all components. The selection is based on material properties and production parameters that indicate higher risk of quality deviations, thereby achieving adequate quality monitoring while reducing measurement time and costs
Solution Approach 2:
The patent changes the parameter of measurement frequency from continuous (all components) to selective (based on material and production parameters). By using material properties and production parameters as selection criteria, the system achieves efficient quality monitoring without requiring continuous measurement of every component
2Productivity
If selective sampling is performed, then productivity is improved by reducing measurement time, but measurement precision may deteriorate due to incomplete data collection
Solution Approach 1:
The patent implements feedback by using detected component properties and quality deviations to adjust and optimize the selection criteria for future measurements. The system learns from detected deviations and refines which material properties and production parameters are most indicative of quality issues, improving measurement precision over time while maintaining high productivity
Solution Approach 2:
The patent replaces random or manual sampling with an automated electronic selection system that uses material properties and production parameters. This substitution of the sampling mechanism with an intelligent selection algorithm ensures that the most relevant components are measured, maintaining measurement precision while improving productivity
3Reliability
If comprehensive data collection is implemented, then reliability of quality control is improved, but device complexity increases due to additional sensors and computing requirements
Solution Approach 1:
The patent segments the data collection system into modular components: material property sensors, production parameter sensors, component property detectors, and an electronic selection device. Each module performs a specific function, and the segmented architecture improves reliability through specialized detection while managing complexity through modular design
4Ease of operation
If automated selection and detection is implemented, then ease of operation is improved, but device complexity increases due to electronic computing devices and algorithms
Solution Approach 1:
The patent implements self-service by enabling the electronic computing device to automatically select components for measurement based on material properties and production parameters without manual intervention. The system autonomously determines which components require quality detection, improving ease of operation while the automated nature manages the complexity internally
Data Source
AI summary
A method for operating a press, wherein a relationship between a detected component property (current value) of the final product (e.g. surface quality), the provided material property of the semi-finished product (e.g. sheet thickness), and the detected production parameter (e.g. pressing pressure) is determined, for example, by a self-learning algorithm, on the basis of precedingly determined differential values between target and current values of the component property. The algorithm is subsequently used for targeted selection of samples for quality monitoring.