Work

Predictive Modeling of Packaging Seal Strength: A Hybrid Vision and Process Data Approach for Non-Destructive Quality Assurance

Year: 2026

Type: article

Abstract: A method for quality inspection of food packaging based on hybrid imaging and machine-learning techniques is presented. The proposed inspection system integrates thermal and visible-light imaging, enabling detection and classification of faults such as weak seals, creases and contamination. For the purpose of the study data acquisition is automated with the use of an industrial manipulator, ensuring repeatability and consistent positioning of samples. Using the acquired images, the temperature distribution in the sealing area and selected process parameters, a predictive model for burst-pressure testing was developed. The proposed workflow includes attribute selection, hyperparameter optimization and the application of regression algorithms. The proof-of-concept results demonstrate a strong alignment between predicted and measured values, as well as high model stability. The best-performing model, ElasticNet, achieved an R2 of 0.815 and an MAE of 0.028 kgf/cm2, confirming its potential for non-destructive quality control of packaging.

Source: Applied Sciences

DOI: 10.3390/app16020923

Authors: Piotr Garbacz, Andrzej Burghardt, Piotr Czajka, Jordan Mężyk, Wojciech Mizak

Institutions: Łukasiewicz Research Network, Łukasiewicz - Institute for Sustainable Technologies, Rzeszów University of Technology

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896