Mechanical & Materials Engineering, Department of


First Advisor

Dr. Prahalada Rao

Date of this Version



Gaikwad, A., 2020, "On Geometric Design Rules and In-Process Build Quality Monitoring of Thin-Wall Features Made Using Laser Powder Bed Fusion Additive Manufacturing Process," MS Thesis, University of Nebraska-Lincoln.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Mechanical Engineering & Applied Mechanics, Under the Supervision of Professor Prahalada Rao. Lincoln, Nebraska: January, 2019


The goal of this thesis is to quantify the link between the design features (geometry), in-process signatures, and build quality of parts made using the laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is the foundational basis for proposing design rules in AM, as well as for detecting the impending build failures using in-process sensor data.

As a step towards this goal, the objectives of this work are two-fold:

1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry related factor is the ratio of the length of a thin wall (𝑙) to its thickness (𝑑) in the X-Y plane along which powder is deposited (raked or rolled), termed as the aspect ratio (length-to-thickness ratio, 𝑙/𝑑), and the angular orientation (ΞΈ) of the part which refers to the inclination of the part in the X-Y plane to the re-coater of the LPBF machine.

2) Monitor the thin-wall build quality by analyzing the images of the part obtained from an in-process optical camera using a convolutional neural network.

To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratios from Titanium alloy (Ti-6Al-4V) material – the aspect ratio 𝑙/𝑑 of the thin-walls ranges from 36 to 183 (11 mm long [constant], and 0.3 mm to 0.06 mm in thickness). These thin-wall test artifacts were built under three angular orientations, 0Β°, 60Β°, and 90Β°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of its geometric integrity was quantified as a function of the aspect ratio and orientation angle, which helped codify a set of design guidelines for building thin-wall structures with LPBF.

The resulting geometric design rules are summarized as follows.

1) The orientation angle (ΞΈ) of 90Β° should be avoided while building thin-wall structures.

2) The aspect ratio (𝑙/𝑑) of a thin wall should not exceed 73 (11 mm / 0.15 mm).

3) The height of a thin wall should not be more than nine times its thickness.

To monitor the quality of the thin-wall, in-process images of the top surface of the bed were acquired during the build process. The online optical images were correlated with the offline quantitative measurements of the thin walls through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, 𝜌) between the offline XCT-measured thin-wall quality and the CNN predicted measurement ranged from 80% to 98%. Consequently, the impending poor quality of a thin wall was captured from in-process data.

Advisor: Prahalada Rao