Acoustic emission-based monitoring of manufacturing processes involving biomaterials

Mishra, Roshan
Thumbnail Image
Other Contributors
Samuel, Johnson
Walczyk, Daniel F.
Mills, Kristen L.
Scarton, Henry A.
Ryu, Chang Yeol
Issue Date
Mechanical engineering
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Next, shear-based cutting of bovine bone is studied using single-tooth bone sawing experiments. More specifically, efforts are undertaken to model the acoustic emission signal power, as a function of the specific cortical bone microstructures and the depth of cut encountered by the sawtooth. First, the acoustic emission signal characteristics from the sawing experiments are related to the haversian and plexiform regions of the cut. The acoustic emission signal power is then modeled based on the energies dissipated in the shearing and ploughing zones encountered by the rounded cutting edge of the sawtooth. For this calculation, the rounded cutting-edge geometry of the sawtooth is divided into a combination of (i) shear-based cutting from a negative rake cutting tool, and (ii) ploughing deformation from a round-nose indenter. The spread seen in the acoustic emission signal power values is captured by modeling the variations in the sawed surface height profile, tool cutting edge geometry, and porosity of the bone. The acoustic emission model is first calibrated on the pure haversian and plexiform regions of the bovine cortical bone and then validated on the transition region containing both haversian and plexiform microstructures. The model shows a good correlation (>0.9) between predicted and experimentally measured acoustic emission signal power values and is useful for process planning purposes.
Finally, a multi-sensor modality involving both acoustic pressure signals and digital images is proposed as an alternative to estimate the gel-state of a bulk agarose hydrogel sample undergoing thermo-reversible gelation at room temperature. Here, a hydrophone is used to monitor the acoustic waves generated in the sample by an external, low-impact energy source. The digital camera is used to monitor the image intensity evolution as the hydrogel changes from a transparent, solution-state to an opaque, gel-state. The two signals are then combined using an empirical relationship to obtain a gel-state estimate. The preliminary findings indicate that this approach holds the promise of being implementable in a 3D printing environment.
First, orthogonal cutting experiments are conducted on haversian and plexiform bone specimens harvested from the bovine femur. The specimens are machined at a 70 micron depth of cut and a 800 mm/min cutting speed, to induce fracture-dominant cutting. With the aid of a high-speed camera, microstructure-specific failure mechanisms in the haversian and plexiform specimens are observed, and their distinct acoustic emission features are studied. Compared to the measured cutting force, the acoustic emission signal is seen to be a more efficient in distinguishing the different failure mechanisms. Differentiating features are derived for each of the specific failure mechanisms using a combination of their specific acoustic emission signals and measured crack lengths.
The research presented in this thesis deals with exploring the effectiveness of using acoustic sensing modalities to monitor manufacturing processes involving two specific biomaterials, viz. bovine cortical bone, and agarose hydrogel. For bovine cortical bone, the acoustic emission signals collected during fracture and shear-based cutting experiments are investigated and related to the observed failure mechanisms. An acoustic pressure signal based monitoring approach is proposed for estimating the gel-state in bulk agarose hydrogel samples undergoing gelation.
August 2020
School of Engineering
Dept. of Mechanical, Aerospace, and Nuclear Engineering
Rensselaer Polytechnic Institute, Troy, NY
Rensselaer Theses and Dissertations Online Collection
Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.