A plastic element is press-fitted into a metal frame. A fully automated monitoring for this process is required. It has to be checked that no cracks in the plastic part emerge during the operation.
Crack detection during press-fitting is extremly important. This is because any cracks in the plastic element can lead to fatigue fracture and functional failure of the component under normal load.
A complete visual inspection of the plastic element is not possible. As QASS’ structure-borne sound technology provides a view of what is going on inside the component even when it is not possible to look inside, it is applied to monitor the assembly procedure.
QASS’ measuring system Optimizer4D works on the basis of structure-borne sound. Crack formations cause characteristic acoustic impulses, which our measuring system can detect virtually at the moment they occur.
The QASS structure-borne sound sensor is mounted as close as possible to the component. Our sensor captures the acoustic vibrations of the joining process and simultaneously converts the sensor signals (time-amplitude signals) into a measurement signal that the Optimizer4D samples at a very high frequency.
The Optimizer4D digitizes all measurement signals and transforms their data in real-time by using the Fast-Fourier Transform in order to obtain the frequency information (pitches) hidden in the signal. The self-developed method we use for spectral analysis is called ‘High-Frequency-Impulse-Measurement‘ (HFIM).
Through HFIM it is possible to reliably detect crack formations and to differentiate them from normal process events. In fact, on the basis of energy levels from two-dimensional time-amplitude signal data, crack formations and process events often cannot be properly distinguished from another. Through additional frequency information the differentiability improves considerably. Within the three-dimensional time-amplitude-frequency signal data, crack formations produce very specific signal patterns with amplitudes up to the high-frequency range, which differentiate considerably from those of usual process events.
All measurement data processed with the HFIM are directly available to the QASS software Analyzer4D.
With smart tools such as pattern recognition and automatic decision routines, the Analyzer4D represents an innovative and powerful program for real-time signal data analysis. All data is clearly visualized for the user in a 3D-spectrogram.
In contrast to crack signals, machine and interference noises produce low-frequency signals, which do not show any high-frequency components. A spectral filter (which we call frequency mask) can be employed to cancel out typical machine and interference noises from the signal data. In this way, the signal-to-noise ratio is improved.
With pattern recognition, it is possible to detect crack formations in the plastic element that occur during press-fitting. In a first step, signal patterns of crack formations are referenced and stored in a pattern library. Then, an automated comparison algorithm scans the incoming filtered signal data for the stored patterns and calculates similarities to the observed patterns. If during assembly a crack forms inside the plastic element, the algorithm detects a high similarity score. Then the Optimizer4D produces a NOK-signal and triggers the ejection of the component.
100% quality control
With Optimizer4D parts can be reliably checked for cracks – even when optical inspection systems reach their limits because of component structure or geometry. QASS’ measuring system monitors each part in-line, using structure-borne sound, and performs real-time signal analysis of the assembly process data. Defective components are immediately detected.
Even under harsh production conditions
Our crack detection method is also applicable in harsh production environments that are corrupted by machine or interference noises. With spectral analysis, we can clearly separate these noises from the useful signal, and using spectral filters cancel them out of the signal data.
Reliability by 3D signal data
On the basis of the spectrally analyzed and filtered signal data, crack events can be clearly distinguished from other process events. Cracks produce a specific signal pattern that the QASS pattern recognition algorithm can automatically detect in the incoming signal data after a single referencing.
Scrap rate reduction
The improved selectivity allows you to effectively reduce your scrap rate by avoiding false classifications of process events as cracks.