Additive manufacturing (AM) methods allow the fabrication of complex geometries without increasing the complexity of the manufacturing process. However, technical challenges associated with process repeatability and reliability make it difficult to ensure a final part quality without resorting to destructive test methods. Thus, effective in situ monitoring methods are necessary to encourage further adoption of AM methods for large volume part fabrication. In this project, we are exploring the possibility of using vibroacoustic emission monitoring for AM process failure detection. Vibroacoustic emission monitoring, in combination with wavelet and time-frequency analysis methods, allows early fault identification. The failure identification process may be automated by using machine intelligence algorithms such as K-means clustering. Developed automated process monitoring and failure identification tools will allow in situ monitoring and quality control for AM methods.
Collaborators: Dr. Vis Madhavan (WSU) and Dr. Wilfredo Moscoso-Kingsley (WSU)
Vibro-Thermal De-Icing of Aircraft Structures
Although a wide variety of de-icing methods are currently in use, most of these systems involve substantial mass-loading and power consumption. Further, the use of methods such as electro-thermal blanket systems result in thermal loading gradients unsuitable for composite wing and fuselage components, while the use of de-icing fluids is an environmental concern and limited to ground aircraft de-icing. Here, we are exploring the combined use of ultrasonic vibroacoustic waves and novel polymeric films to develop efficient ultrasonic de-icing tools for composite aircraft components.
Collaborators: Dr. Gisuk Hwang (WSU)
Predictive Engineering Tools for Injection Molded Long Carbon-Fiber Thermoplastics
Long fiber thermoplastic composites offer many advantages for light materials and are finding increasing use in the automotive and aerospace industries. The local elastic properties of such materials are a function of fiber length and fiber orientation. Thus, predicting the fiber length distribution and fiber orientation distribution during injection molding of a component is critical for predicting the component elastic properties. Further, high precision experimental data are necessary to validate numerical predictive tools. In this collaborative effort, we developed a semi-automated fiber orientation and fiber length measurement tools for experimental verification of various numerical models. The tools utilize MATLAB to implement image processing routines and obtain stereological fiber orientation and fiber length distributions. The codes are available on request.
Collaborators: Pacific Northwest National Lab., Autodesk Inc., Magna, Toyota, Plasticomp Inc., Prof. Mike Sangid (Purdue University), Prof. Charles Tucker III (UIUC)
Project Sponsors: Department of Energy
X-ray Tomography Characterization of Fiber Reinforced Polymer Composites
We use micro-tomography coupled with digital image processing to develop a data analysis framework, which hinges on a careful synergy between the two. Accurate measurement and analysis of material microstructure requires efficient means of separating and uniquely identifying the preferred microstructure from other constituents of the sample. Although there are several software capable of thresholding and segmentation, there are limits to user customization of these techniques. Motivated by this, we use the resources available under MATLAB’s Digital Image Processing Toolbox and a programmed metadata dependent correlation to develop an in-house MATLAB code to perform thresholding and segmentation on gray scale tomograms. These tools provide a platform to quantify the microstructure and fully understand its evolution with applied loading within SFRC’s currently being used extensively in the aerospace and the automotive industries. We use tomograms of glass fiber/polypropylene composites to demonstrate the method. However, this can be applied to characterize other short fiber reinforced polymer composites materials.
Collaborators: Prof. Mike Sangid (Purdue University), Ronald Agyei (Purdue University), Xianghui Xiao (APS Argonne National Lab)
In-Situ Study of Mechanics of Sedimentary Rock Cutting
The chip formation process during the cutting of Indiana limestone is studied using high-speed in situ imaging. Synchronized imaging and force measurements are used to explain the observed differences in the cutting force fluctuations during a cutting cycle at low and high depth of cuts in the context of the various chip formation events. The high-speed images show that coarse chip formation occurs primarily through intra-granular fracture at low depth of cut and through inter-granular fracture at high depth of cuts. Cutting force measurements performed on two limestones with similar mechanical properties but different grain sizes show that the forces required to cut at a given depth are lower for rocks with larger grain sizes. Post-mortem analysis of the cutting chips reveals that brittle fracture is the dominant cutting mechanism across all the studied depths. Nanoindentation tests are performed to characterize the critical ductile-brittle transition depth. Results reveal that ductile chip formation can occur in limestones only at very low depth of cuts (< 600 nm).
Collaborators: Prof. S. Chandrasekar (Purdue University), Dr. H. Yeung (National Institute of Standards and Technology)