Samaher Shaheen, California State University, Chico
David Nguiffo, California State University, Chico
Forest Siewert, California State University, Chico
Elizabeth Rodriguez, California State University, Chico
Dennis O’Connor, California State University, Chico
Yang Chao, California State University, Chico
Forest Siewert, Student, California State University, Chico
Vibrational analysis is an important method for evaluating the structural performance of 3D-printed metal components in aerospace applications. Aerospace-grade materials are selected for their ability to resist extreme conditions, and their vibrational behavior must be carefully assessed to ensure reliability in challenging environments. This study examines the vibrational behavior of beams and complex geometries made from Inconel, a nickel-chromium alloy known for its strength and heat resistance. In addition, other materials such as titanium alloys, aluminum, and stainless steel are considered due to their wide application in satellite construction. By introducing the Relative Frequency Shift (RFS) method, which employs vibrational and modal analysis to detect internal defects in 3D-printed metal components, a vast array of predicted responses can be frontloaded into a qualification procedure. This research incorporates machine learning (ML) to improve the organization and efficiency of prediction models and automate defect identification. ML algorithms analyze large datasets to uncover patterns related to voids, cracks, or other irregularities that can occur during the 3D printing process. With ML integration, the study speeds up vibrational analysis, enhances accuracy, and reduces the reliance on physical testing, allowing for faster transitions from design to production. Using Ansys simulation and the finite element method (FEM), modal and harmonic analyses are conducted, incorporating Timoshenko beam theory to account for shear deformation and rotary inertia. The Timoshenko theory is integrated into an algorithm that helps detect potential defects, such as voids or deformations, which may arise during the 3D printing process. Timoshenko beam theory offers a significant advantage over different models, which are crucial for accurately analyzing components with non-uniform geometries. This level of detail ensures that even indistinct internal deformations are captured, providing deeper insights into the material behavior under dynamic conditions. The integration of Timoshenko theory into FEM simulations allows for more realistic and practical results, bridging the gap between theoretical analysis and real-world applications. Ansys, as the chosen simulation platform, excels in handling complex geometries and multi-material assemblies, enabling the study to precisely replicate the operational environment of aerospace components. By leveraging Ansys’ advanced capabilities, the study ensures that all potential scenarios, including thermal expansion and vibrational loads, are accurately modeled, leading to more reliable predictions and safer designs.
Current testing methods for aerospace components often involve extensive physical experiments, including vibration tables, thermal cycling chambers, and destructive testing to evaluate failure points. While these methods provide valuable insights, they are time-consuming, costly, and limited in their ability to explore a wide range of scenarios. This study integrates advanced simulations and predictive algorithms to examine 3D-printed parts through vibrational analysis, FEM simulation, and theoretical modeling. It offers a complementary approach that reduces dependency on exhaustive physical tests while maintaining accuracy and reliability to understanding material behavior.
Predictive methods powered by ML detect potential flaws early, cutting down on delays and preventing material waste as well as preventing possible failure. By identifying natural frequencies and mode shapes, engineers can confirm that components meet performance criteria under operational conditions. The added accuracy from ML-driven insights strengthens these evaluations, making it easier to identify structural weaknesses before they affect performance. This tool not only enhances technical reliability but also supports supply chain efficiency by reducing production errors, minimizing waste, and improving part quality. These findings help aerospace companies streamline their supply chains, lower manufacturing costs, and ensure more reliable, high-performing components for satellite structures, meeting the demanding conditions of space. Collaboration between suppliers and manufacturers improves significantly when shared data and tools are utilized. This approach allows for a supply chain that adapts easily to changes and responds quickly to demands. By addressing current challenges in aerospace production, it also establishes a foundation for future innovation, helping the industry stay ahead and meet the requirements of space exploration.