Prediction of mechanical properties of short fiber reinforced composite fabricated by Fused Filament Fabrication (FFF) method using Machine Learning

The tremendous increase in the application of additive manufacturing (AM) has gained much attention in recent times due to its usability and capacity to ascribe improved mechanical properties on printed parts with no tooling. AM process with the use of FFF is becoming an integral fabrication method for producing the complex geometries and machine components with intricate parts. There is a corresponding increase in dataset derived from AM process which has ushered the use of highly computational models like machine learning (ML) and deep learning for analysis, prediction, classification, dimensional accuracy, and optimization of methods and printing properties of fabricated parts. This study explores the contribution of printing parameters, e.g., printing speed, layer height and infill density on mechanical properties of short carbon fiber samples produced using FFF technology. ML models will be used for classification of samples built with different print parameters, the models will analyze microstructural images captured under microscope as input dataset and make prediction and classification based on their microstructural attributes (bead shape). In this study, the computation ability of ML models will be used in the predictions for improved mechanical properties based off results of tensile tests conducted on FFF material samples with various printing parameters. The findings of this study provide evidence and insight that ML can be used to optimize printing performance and its applications

Heat transfer study in a convective-radiative fin with temperature-dependent thermal conductivity and magnetic field using variation parameters method

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

Using Machine Learning Techniques to Predict the Dimensional Changes of Low-cost Metal Material Extrusion Fabricated Parts

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

Temperature Compensation for Electromechanical Impedance Signatures With Data-Driven Modeling

Impedance-based structural health monitoring (SHM) is recognized as a non-intrusive, highly sensitive, and model-independent SHM solution that is readily applicable to complex structures. This SHM method relies on analyzing the electromechanical impedance (EMI) signature of the structure under test over the time span of its operation. Changes in the EMI signature, compared to a baseline measured at the healthy state of the structure, often indicate damage. This method has successfully been applied to assess the integrity of numerous civil, aerospace, and mechanical components and structures. However, EMI sensitivity to environmental conditions, the temperature, in particular, has been an ongoing challenge facing the wide adoption of this method. Temperature-induced variation in EMI signatures can be misinterpreted as damage, leading to false positives, or may overshadow the effects of incipient damage in the structure. In this work, we investigate the feasibility of using data-driven modeling for temperature compensation of EMI signature is presented. Data-driven dynamic models are first developed by fitting EMI signatures measured at various temperatures using the Vector Fitting algorithm. Once these models are developed, the dependence of model parameters on temperature is established. The capabilities of this temperature compensation method are demonstrated on aluminum samples, where EMI signatures are measured at various temperatures and over a broad frequency range

Heat transfer study in a convective-radiative fin with temperature-dependent thermal conductivity and magnetic field using variation parameters method

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

Prediction of Dimensional Changes of Low-Cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

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 researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME-made components after 3D printing and sintering process. In this research, the ML algorithms present a significantly high coefficient of determination (i.e., 0.999) and a very low mean square error (i.e., 0.0000878). The prediction outcomes using a neural network approach have the smallest mean square error among all ML algorithms and it has quite small p-values. So, in this research, the neural network algorithm has the highest accuracy. 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