Global supply chain shocks and interruptions underscore the importance of supply chain nervousness (SCN). A clear understanding of nervousness in the event of a crisis allows an organization to make a good plan to deal with interruptions and future shocks. This study aims to develop a model for assessing nervousness parameters in the supply chains (SCs) by incorporating the fuzzy-FMEA theory and gray correlation approach to rank the SCN factors. The integrated model used in this study can improve the accuracy of outcomes when uncertainty exists in the SCN data. The results show that the most important nervousness factors are SC planning, visibility, stability, decision support systems, and SC flexibility. The developed SCN approach allows understanding and prioritizing SCN factors with more realistic and effective assessment. Findings offer beneficial insights for SCN prevention, and suggestions are made to identify nervousness mitigations. The outcomes of this research can be used by researchers and SC specialists to develop decision support systems.

Additive manufacturing (3D printing) materials have gained growing attention in engineering applications due to their design flexibility and cost-effectiveness. However, their mechanical behavior, particularly under dynamic tensile loading, remains insufficiently understood, introducing a challenge for reliable design and modelling. This study investigates the dynamic tensile behavior of 3D-printed materials across a wide range of strain rates through extensive experimental testing and machine learning (ML) modeling. The research work started by conducting extensive experimental work to investigate the 3D printing materials under various strain rates. Afterwards, employing multiple ML models and exploring their applications to predict stress-strain behavior. Six ML models were utilized: Neural Network, Decision Tree, Support Vector Regression Random Forest, Gaussian Process Regression, and k-Nearest Neighbors. Model performance was validated using unseen experimental data at strain rates of 0.5 and 100 /min. For a strain-rate of 0.5 /min, the Neural Network demonstrated an exceptional precision of 92.69%, and lowest MAPE of 7.31%. While for high strain-rate of 100 /min, the Gaussian Process Regression demonstrated the lowest MAPE of 4.49%. On the other hand, the Decision Tree and Random Forest showed the highest MAPE among the aforementioned ML models. The novelty of this work lies in integrating high strain-rate experimental data with multi-model ML evaluation to identify a reliable stress-strain model for 3D-printed materials. The presented results highlight the effectiveness of ML in predicting seen and unseen stress-strain data at wide range of strain-rates, furthermore, the findings open new avenues for data-driven materials modelling and design.