Eggshell crack detection based on the transient impact analysis and cross-correlation method.

Area of Science:

• Agricultural Engineering
• Materials Science
• Acoustics

Background:

• Eggshell integrity is crucial for food safety and quality.
• Current detection methods may lack efficiency or accuracy.
• Understanding the transient impact process is key to developing better detection systems.

Purpose of the Study:

• To investigate the transient impact process for effective cracked eggshell detection.
• To develop and optimize an experimental system for acoustic signal acquisition.
• To evaluate the performance of cross-correlation and Bayes classification for crack identification.

Main Methods:

• An equivalent mechanical model was developed for impact analysis.
• A self-designed automatic excitation device was utilized.
• Dynamic force signals were analyzed using power spectrum analysis.
• Acoustic response signals were acquired and processed using cross-correlation and Bayes classification.

Main Results:

• Impact speed influences impaction energy, while impact head material and excitation rod weight affect energy and cut-off frequency.
• A cut-off frequency covering the egg's inherent frequency was achieved with an impact tup weight below 6.62 g.
• The optimized system achieved 97% crack detection and 1% false rejection rates.

Conclusions:

• The proposed method provides insights into optimizing impact devices for eggshell analysis.
• The findings support the simplification of classification algorithms for online detection systems.
• This approach offers a promising solution for automated cracked eggshell detection in the food industry.