Papers by James Femi-Oyetoro
Metals, Apr 23, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
ASME 2022 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Sep 12, 2022
Metals
Additive manufacturing (AM) is a layer-by-layer manufacturing process. However, its broad adoptio... more Additive manufacturing (AM) is a layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist re...
Proceedings of Student Research and Creative Inquiry Day, Apr 29, 2021
This study investigates the ample contribution of machine and deep learning algorithms as a predi... more This study investigates the ample contribution of machine and deep learning algorithms as a predictive tool in additive manufacturing. This research aims at leveraging the high computational ability of machine learning algorithms to build predictive models that can be applied in prediction of mechanical behavior of additively manufactured components. Objective Methodology The samples are printed according to ISO 178 standard. The computer aided diagram (CAD) geometry of the samples are designed with SOLIDWORKS. The sample has a dimension of 80mm by 10mm by 4mm. Ultimaker 4.8.0, the slicing software used converts the CAD model from stereolithography (STL) format to a Gcode format which can be easily read by the printer. The printing parameters , which is an important consideration on the research are easily controlled on the slicing software, and the variation in these parameters can be easily investigated. The test samples are printed using Ultimaker S5 dual extruder 3D printer.
Proceedings of Student Research and Creative Inquiry Day, Apr 29, 2021
Additive manufacturing (AM) is a widely used layer-by-layer manufacturing process. However, it is... more Additive manufacturing (AM) is a widely used layer-by-layer manufacturing process. However, it is limited by material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is the most widely used AM technologies. Thus, it is adopted in this research. Low-cost metal ME is a new AM technology used to fabricate metal composite parts using sintering metal infused filament material. Since the materials and the process are relatively new, there is a need to investigate the dimensional accuracy of low-cost metal ME fabricated parts for real-world applications. Each step of the manufacturing process such as 3D printing of the samples and the sintering will affect the dimensional accuracy significantly. By using several machine learning (ML) algorithms, a comprehensive analysis of dimensional changes of metal samples fabricated by low-cost metal ME process is developed in this research. ML methods can assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. In this study, single linear regression, linear regression with interactions and neural networks were utilized to assess and predict the dimensional changes of components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy among the other ML methods. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process
Journal of Applied Mathematics and Computational Mechanics, 2017
In this work, a heat transfer study is carried out in a convective-radiative straight fin with te... more In this work, a heat transfer study is carried out in a convective-radiative straight fin with temperature-dependent thermal conductivity and a magnetic field using the variation of parameters method. The developed heat transfer model is used to analyze the thermal performance, establish the optimum thermal design parameters and investigate the effects of thermo-geometric parameters and non-linear thermal conductivity parameters on the thermal performance of the fin. The results obtained are compared with the results in literature and good agreements are found. The analysis can serve as basis for comparison of any other method of analysis of the problem and it also provides a platform for improvement in the design of fin in heat transfer equipment.
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Papers by James Femi-Oyetoro