Quality assurance and structural integrity evaluation are the crucial parts of the successful design and service of additively manufactured (AM) components. Discontinuities and flaws in AM parts can affect the mechanical properties of a component during manufacturing and service. It is very important to identify the discontinuities in AM parts in terms of location, size, and geometrical properties using nondestructive testing (NDT) techniques. Existing research in both mechanical testing and nondestructive evaluation involves developing methods for characterizing and inspecting AM components as the use of such materials continues to rise. Although there exist relatively mature ultrasonic inspection techniques for defect detection, AM polymer components face the challenge of considerable internal inhomogeneities caused by the design and printing strategies. It has been shown that the ultrasonic signals are very sensitive to the material inhomogeneities, consequently the reflection/diffractions from the defects will be significantly influenced and defect detection will be very challenging. This work aims to present the potentials and challenges in ultrasonic detection of defects in polymer AM parts. Air-coupled ultrasonic tests to be demonstrated and followed by results and discussions. The role of porosity on detectability in the ultrasonic NDT tests is described and a possible way for attenuation assessment is demonstrated. Finally, the effect of AM part inhomogeneities on detection probability of seeded defects with different sizes and locations in AM parts is presented.
Advancement in wireless communication as well as recording and transferring data over the internet provides a lot of possibilities for smart inspection and monitoring for machines and structures. The big data recorded and transferred through such a system must be analyzed efficiently on the go to provide accurate feedback to the system. Neural network (NN) data processing techniques are an effective methodology for fast and accurate analyses of the data and provide feedback to the system. An NN methodology is proposed for structural health monitoring of bridge structures. The proposed platform uses the direct and indirect sensors mounted on the bridge structure and on the passing vehicle, respectively. This proposed approach will decrease the cost and the potential damages to the sensors in direct methods, and will increase the accuracy and reliability of monitoring in indirect techniques. The methodology and data processing techniques have been validated using a lab-scaled test bed.
Ensuring adequate quality for additive manufactured (AM) materials presents unique metrology challenges to the on-line process measurement and nondestructive evaluation (NDE) communities. AM parts now have complex forms that are not possible using subtractive manufacturing and there are moves for their use in safety criticality components. This paper briefly reviews the status, challenges and metrology opportunities throughout the AM process from powder to finished parts. The primary focus is on new acoustic signatures that have been demonstrated to correlate process parameters with on-line measurement for monitoring and characterization during the build. In-process, quantitative characterization and monitoring of material state is anticipated to be potentially transformational in advancing adoption of metal AM parts, including offering the potential for early part rejection, part condition guided process control or even potentially in-process repair. This approach will enable more effective deployment of quality assessment metrology into the layer-by-layer material build with designed morphology. In this proof-of-concept study acoustic-based process monitoring signals were collected during the Direct Energy Deposition (DED) AM process with different process conditions to investigate and determine if variations in process conditions can be discriminated. A novel application of signal processing tools is used for the identification and use of metrics based on temporal and spectral features in acoustic signals for the purpose of in-situ monitoring and characterization of conditions in an AM process. Results show that the features identified in signatures are correlated with the process conditions and can be used for classifying different states in the process.