Paint Process Quality Tracking for Hidden Defect Detection
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Solution Overview
Problem
Existing quality control methods in vehicle body and attachment part painting processes fail to identify and address quality deficiencies in real-time due to the fast production cycle, leading to undetected or difficult-to-identify defects in lower layers.
Innovation Solution
A method involving workpiece-specific and workpiece-carrier-specific data sets that are created, supplemented, and stored during the production process, allowing for real-time identification and classification of quality deficiencies using machine-learning algorithms to detect systematic issues and adapt production processes accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quality control checks are performed after each individual coating layer, then quality deficiencies can be identified early, but production cycle time increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by creating digital twins and process data models before actual painting occurs. Process parameters, coating thicknesses, and quality requirements are pre-defined and stored in databases, allowing the system to predict and prevent quality issues before they manifest in the physical painting process, eliminating the need for intermediate quality checks.
Solution Approach 2:
The patent replaces physical quality control measurements and manual inspection processes with digital simulations and data analysis. Digital twins virtualize the painting process, allowing quality assessment to occur in the digital domain rather than requiring physical interruption of the production line for measurement and inspection.
2Manufacturing precision
If elaborate quality control checks are performed on vehicle bodies, then quality of paintwork improves, but detection of defects in lower layers becomes difficult
Solution Approach 1:
The digital twin acts as an intermediary that provides visibility into lower layers without requiring physical disassembly or destructive inspection. The virtual model maintains a complete history of all coating layers applied to each workpiece, allowing quality controllers to examine any layer's characteristics and detect defects that would be hidden in the physical multi-layer structure.
Solution Approach 2:
The system creates digital copies (digital twins) of physical workpieces and their coating structures. These digital replicas contain complete information about all layers, including those hidden beneath surface coatings. Quality controllers can inspect the digital copy to detect defects in lower layers without affecting the physical object or requiring destructive testing.
3Loss of information
If process data from all production steps are collected and stored, then quality deficiency causes can be identified, but data management complexity increases
Solution Approach 1:
The digital twin database serves multiple functions simultaneously: it stores process parameters, tracks quality data, enables defect analysis, supports predictive modeling, and provides a basis for process optimization. This multi-functional digital infrastructure consolidates what would otherwise require separate systems for data collection, storage, analysis, and optimization.
Solution Approach 2:
The patent merges previously separate data streams (process parameters, quality measurements, production metadata) into a unified digital twin structure. By combining these elements into a single integrated database organized around individual workpiece identities, the system reduces complexity compared to maintaining separate databases and correlation systems for each data type.
Data Source
AI summary
In order to provide a method for analysing quality deficiencies of workpieces, preferably vehicle bodies and/or vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants, by means of which method quality deficiencies can be avoided and/or by means of which method quality deficiency causes in the production process can be determined, avoided and/or remedied, it is proposed in accordance with the invention that the method comprises the following steps:creating a workpiece-specific data set, uniquely assigned to a workpiece, at the start of a production process, in particular at the start of a painting process and/or creating a workpiece-carrier-specific data set, uniquely assigned to a workpiece carrier, at the start of a production process, in particular at the start of a painting process;supplementing the workpiece-specific data set while a workpiece is passing through the production process, in particular the painting process, with in particular quality-relevant process data and/or supplementing the workpiece-carrier-specific data set while a workpiece carrier is passing through the production process, in particular the painting process, with in particular quality-relevant process data;storing the workpiece-specific data set in a database and/or storing the workpiece-carrier-specific data set in a database.