Acoustic insights into the corn extrusion process for enhanced quality control

In industrial extrusion processes, a solid material is pressed through a die to obtain products of the desired shape and dimension. Fluctuations in the process parameters have a significant impact on the product quality. In food extrusion, the expansion noise at the die can serve as an indicator of the stability of the process. This study employs microphones to characterize the corn extrusion process, focusing on correlating acoustic emissions with predefined process parameters such as feed intake and water content. Experimental data from laboratory and industrial settings reveal distinct domain shifts, yet consistent findings confirm the distinguishability of various extrusion process parameters by analyzing acoustic emissions. Employing machine learning models, including support vector machines and convolutional neural networks, in conjunction with audio features such as log Mel spectrograms, yields promising accuracies above 90\% in discriminating between standard and non-standard process parameters. The proposed acoustic quality control approach has the potential to enhance the stability of extrusion processes and contributes to the development of automated monitoring systems in the field of food extrusion. This ensures consistent quality and reduced waste, ultimately leading to significant cost savings in production.