bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parametric Analysis of ANFIS, ANFIS-PSO, and ANFIS-GA Models for the Prediction of Aluminum Surface Roughness in End-Milling Operation
Authors: Balonji Serge , Okokpujie Imhade Princess , Tartibu Lagouge 
Published: February 2023

Abstract
Milling is one of the old and common cutting processes that utilize rotating tools to take materials off the main component with a combination of tools and workpiece movements. The texture of a machined surface is a key factor in defining how an essential component interacts with its environment. Trial-and-error machining to produce high-quality surfaces has been a time-consuming method that yields lower production and poor revenue. In this paper, the performances of an Adaptive Network-based Fuzzy Inference System (ANFIS) model has been employed for the prediction of the surface roughness (SR) of a block of Aluminum alloy AI6061 machined on an end-mill CNC machine by varying four input settings namely: The spindle speed of rotation, the tool cutting rate, the radial depth, and the axial depth. The approach consisted of a parametric analysis carried out within each system to obtain the finest models for the prediction. The hybrids ANFIS-PSO and ANFIS-GA have been employed to find out which one, either PSO or GA, optimizes better ANFIS for the prediction of Al6061 SR. Their performances produced better results than the stand-alone ANFIS, with ANFIS-GA yielding the best results of the most negligible RMSE value of 0.01097 and the regression values of 0.9939 for training and 0.8102 for testing.
Investigation of the Mechanical Properties of High-Temperature Polymer (Polyether Ether Ketone-PEEK) With Material Extrusion Additive Manufacturing
Authors: Sarker Md Rashedul H. , Glassmeyer Joseph , Ruble Alexander , Hamidi Youssef K. , Billah Kazi Md Masum 
Published: February 2023

Abstract
Polyether-ether-ketone (PEEK) is one of the high-performance polymers and has versatile applications in different industries. It has higher mechanical strength compared to other high-temperature polymers, and achieving this mechanical strength using additive manufacturing is not straightforward. Additive manufacturing of PEEK requires great attention before, during, and after 3D printing. This study focused on pre-processing and printing parameters to avoid warped samples and achieve high-quality tensile samples. Coating the build plate with high-temperature Nano-polymer glue at different stages has played a significant role in producing high-quality tensile samples and avoiding warped samples. This study also investigates the mechanical properties of 3D printed PEEK with different raster angles (0°, 90°, 45/90°, and +45/−45°) with and without annealed tensile samples. Controlled printing parameters were maintained, including 400 °C extrusion temperature, 160 °C bed temperature, and 90 °C ambient temperature. Higher mechanical strength was found in annealed samples for 0° and 90° raster angles, 97.445 Mpa and 98.4 MPa, respectively.
Assessing Undergraduate Students’ Level of Awareness of Commercialization of Engineering Research Innovation at a Historically Black College and University
Authors: Addo Sampson , Tyagi Pawan , Mutunga Eva 
Published: February 2023

Abstract
Engineering Research Innovation Commercialization (ERIC) seeks to translate products or services from the research laboratory to the marketplace or the end-user for societal benefit. Research indicates that universities have distinctive capabilities that allow them to play an important role in the process of research innovation commercialization. Historically Black Colleges and Universities (HBCUs), though originally established mainly as teaching and blue-collar trade institutions to educate African Americans, have been gradually commercializing several research innovations through patenting. However, this is significantly lower compared to that of their counterparts (specifically, Predominately White Institutions – PWIs). This according to available research is mainly because HBCUs have been traditionally under-served and under-resourced. Currently several programs such as the National Science Foundation (NSF) I-Corps and National Science Foundation Center of Research Excellence in Science and Technology (NSF-CREST) Center for Nanotechnology Research and Education (CNRE)) are being implemented by HBCUs to promote the commercialization of research innovations by training innovators about commercializing innovations. However, little research has been done to assess the level of awareness of engineering undergraduate students at an HBCU about the commercialization of engineering research innovations. This pilot study, therefore, seeks to investigate the level of awareness of engineering undergraduate students at an HBCU about engineering research innovation commercialization. The authors of this study have conducted a similar study that focused only on engineering graduate students in an HBCU. To achieve the purpose of this study, we developed a survey that adopts both binary and ordinal scales of question and administered it to 30 engineering undergraduate students in an ABET-accredited HBCU. After collation and analysis, the results indicate a low level of awareness of engineering research innovation commercialization by engineering undergraduate students at this University. Though at a pilot stage (to be validated by a larger study in different HBCUs), the study recommends that HBCUs initiate new or strengthen ongoing innovation commercialization training programs by including it in undergraduate engineering first-year courses such as “Introduction to Engineering” and related courses to help students become more aware of the opportunities in engineering research innovation commercialization processes.
The Use of Low-Code During a Skill Shortage
Authors: Büscher Aaron , Schilberg Daniel , Wiegert Lars 
Published: February 2023

Abstract
With rising requirements for digitalization, corporations must increase the speed in which their production and products are digitized. Because of that, there is an increasing demand of software developers which has led to a high skill shortage in Germany. Combined with rising expectations for speed and costs to develop a platform, this led to a problem, that needs to be solved.
The goal of this paper is to estimate the benefit of using Low-Code platforms instead of traditional programming languages. Therefore, the elaboration deals with the central research question whether a Low-Code platform can support corporations through the skill shortage and at the same time reduce time and costs that it takes to develop an application.
To answer this question, a Value Benefit Analysis was used to specify which platform provides the highest benefit for a use-case in the production of Vitesco Technologies in Dortmund. Thereupon, an example application was built to determine the usability.
As final result, it can be concluded that the usage of Low-Code platforms in average leads to less time and costs that is needed for the development. In addition, these platforms are easy to use even without prior knowledge which leads to an advantage that can be used during a skill shortage. On the other hand, it also results in less flexibility which makes it harder to program complex functionalities.
This demonstrates that a Low-Code platform can provide a great benefit when used for simple applications, whereas it is not useful for the use of more complex applications.
Utilizing Bilayer Shrinkage to Assemble Complex Ceramic Shapes
Authors: Hartwell Alexander R. , Slabaugh Nathaniel , Ahn Jeongmin 
Published: February 2023

Abstract
The art and engineering worlds have exemplified the power of ceramic materials in seemingly limitless applications for millennia. Despite these achievements, limitations have been pervasive with regards to obtainable final geometries. To continue to expand the repertoire of achievable ceramic forms, a variety of advanced manufacturing techniques have been explored. One method, increasingly common within the world of soft matter, is self-assembly. By utilizing deformation behaviors of materials through swelling, shrinking, stiffening, relaxing, etc. materials can form themselves into seemingly unimaginable shapes. Though it is less intuitive to apply this idea to the seemingly rigid medium of ceramics, this work shows it is possible by utilizing bilayer shrinkage. Through a combination of computational simulation and experiment, predictions for curvature as well as the final shape of the ceramic composite are made. Unexpected behaviors at the macroscale and microscale are discussed as targets for future investigation. Both within the fundamental world of material behavior as well as in the realm of engineering application, this method shows tremendous potential.
Intelligent Modelling and Machining Characteristics of Hybrid Machining for Hybrid Metal Matrix Composites
Authors: Reddy Janvita , Yadav Ram Singar 
Published: February 2023

Abstract
With the invention of advanced engineering materials, the shaping of such materials became a challenging issue. Two or more reinforcements are added to the metal matrix and form hybrid metal matrix composites (HMMCs) with preferable material properties, but shaping became quite challenging. Hybrid Surface grinding Electrical Discharge Diamond Face Surface Grinding (EDDFSG) is a suitable hybrid machining process capable of machining complicated HMMCs. With the help of EDDFSG, adverse effects of both traditional and non-traditional techniques are overcome while combining their benefits for a better machining results. A hybrid composite machined with a hybrid machining process requires an advanced technique for modeling and the performance prediction of complex machining characteristics. Material removal rate (MRR) and (Ra) rely on the process parameters, their influence must be extensively studied. Machine learning, a subset of Artificial Intelligence, allows machines to learn, develop, and execute tasks like human beings based on data rather than explicitly programmed. In the present work, an attempt has been made to develop a Machine Learning (ML), K Nearest Neighbor (KNN) based model, to predict MRR and Ra for machining EDDFSG of Al/Al2O3p/B4Cp and Al/SiCp/B4Cp HMMCs. The KNN algorithm is one of the efficient ML models for regression. Our training data set is normalized using the Min-max scalar to avoid a biased algorithm towards one process parameter. The model’s accuracy is validated by average standard error metrics on the test data set. The impacts of the process parameters like pulse-on time, gap current, wheel speed, pulse-off time, grit number, table speed over the response variables of the ML model is studied and analyzed in depth. The remarkable results are found pertaining to machining characteristics of the EDDFSG process over traditional modelling techniques.
Experimental Characterization of Critical Heat Flux and Minimum Film Boiling Heat Flux for Additively Manufactured Cooling Channels for Liquid Nitrogen Saturated Flow Boiling
Authors: Ortega Debra J. , Amador Alejandro , Choudhuri Ahsan R. , Rahman Md Mahamudur 
Published: February 2023

Abstract
This work experimentally characterizes the critical heat flux (CHF) and minimum film boiling heat flux (MFBHF) in additively manufactured cooling channels for regeneratively-cooled rocket engines during high pressure saturated internal forced convective boiling of liquid nitrogen (LN2). Three different channels with hydraulic diameters of 1.8 mm, 2.3 mm and 2.5 mm were fabricated by the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC). The channels were fabricated using Powder Bed Fusion (PBF) advanced 3D printing of the rocket engine material, GR-Cop42, a copper-chrome-niobium alloy. The fabricated channels were tested using a custom-built cryogenic High Heat Flux Test Facility capable of operating up to 4 MPa of pressure and 10 MW/m2 of heat flux. The channels were asymmetrically heated from the bottom to simulate the performance of the cooling channels of a rocket engine. The high-pressure flow boiling tests were performed at 1.38 MPa with respective saturation temperature of 109 K using LN2 as the working fluid in horizontal orientation of the channels. The volumetric flowrate of LN2 is held approximately constant at 47 cm3/s for all channels. The experiments were performed beyond the CHF to ensure film boiling inside the channels, and then gradually decreased the given power until MFBHF was reached. A CHF of 543 kW/m2 and a MFBHF heat flux of 486 kW/m2 were achieved for the 1.8 mm hydraulic diameter channel. Furthermore, the experimentally measured CHF values were compared with the correlations available in literature. More than 84% increase in CHF has been experimentally measured for the additively manufactured rough cooling channels as compared to the CHF prediction based on literature correlation for smooth channels.
Hydrophobicity Improvements of Polymers Used in Biomedical Applications
Authors: Hossain Mohammad Motaher , Lokasani Vinay Reddy 
Published: February 2023

Abstract
Improvement in hydrophobicity is important for polymers used in various applications such as biomedical applications, as it can delay their degradation due to long-term exposure to moisture environments. Although a number of surface modification techniques have been developed over the years to improve hydrophobicity, their specific influences on hydrophobicity enhancement as well as long-term mechanical and tribological performances are yet to be fully understood. In this study, surface textures, with variation in type and geometry, are introduced on Ultrahigh Molecular Weight Polyethylene (UHMWPE) and High Density Polyethylene (HDPE) surfaces to study the effect of surface modification on hydrophobicity and long-term mechanical and tribological performances. Based on the theoretical study using Wenzel and Cassie-Baxter models, surface textures of various types and dimension are introduced on UHMWPE and HDPE surfaces. The results show that introduction of surface textures significantly improves the hydrophobicity of polymers. Specific relationship between texture type and geometry, and improvement in hydrophobicity is explored. Based on the comparison between experimental results and theoretical models, transition state modeling seems to be more suitable in describing the change in hydrophobicity with the addition of surface texture. The study provides useful guidelines to improve hydrophobicity of polymers for biomedical applications.
Investigating the Thermal and Mechanical Properties of Polyurethane Urea Nanocomposites for Subsea Applications
Authors: Okolo Chinyere , Elmarakbi Ahmed , Birkett Martin 
Published: February 2023

Abstract
Commercial applications of polymer nanocomposites for materials used in offshore settings is continuously touted as a potential solution to expand the material property envelope of polymers used in high pressure and temperature environments. In this regard, polyurethane urea (PUU) has been successfully used in such environments, however, they are limited in terms of their ability to offer multifunctional behavior i.e., thermal conductive behavior with mechanical properties. This gap offers the opportunity for their properties to be enhanced as an advanced multi-functional polymer.
Hence, in this study, polyurethane urea/graphene nanocomposites were synthesized using commercial Polyurethane urea (Task 12), and graphene nanoplatelets (GnP). The graphene nanoplatelets were dispersed in one part of the polyurethane urea component using facile dispersion methods. The properties of the new PUU nanocomposite materials were studied using SEM, mechanical and thermal analysis techniques (DMA and Hot Disk), to examine the development of the multifunctional properties in the PUU nanocomposite. Our analysis describes the influence of graphene nanoplatelets at ultra-low concentrations on multi-functional properties of the PUU nanocomposites. The developed nanocomposites recorded a 16% increase in the tensile strength and an 8% increase in the thermal conductive values. The property improvements are credited generally to the high aspect ratio of graphene nanoplatelets, dispersion and filler-polyurethane interactions at the interface. The impartation of multi-functional behavior, in enhancing the thermal conductivity whilst maintaining the mechanical properties makes it a potentially valuable for subsea applications.
Integration of Hybrid Porous Casting in Solar Receivers to Increase Solar Systems Efficiency
Authors: Gören Sara , Barbosa Flavia , Constantino Erany , Puga Hélder , Teixeira José 
Published: February 2023

Abstract
Concentrated solar thermal (CST) technologies have been considered a promising solution to achieve carbon neutrality by 2050. However, to make CST systems attractive to the international energy sector, their efficiency must be enhanced and low-cost manufacturing processes should be used. In this context, an innovative solar receiver for parabolic-dish solar concentrators is developed in this work, focusing on the improvement of the absorption capacity and heat transfer to the thermal fluid. To enhance solar radiation absorption, a pyramid-shaped texture surface is constructed. In addition, the multiple jet impingement process combined with porous media is applied to ensure high heat transfer rates to the thermal fluid. To evaluate the system efficiency, an experimental setup is developed using a parabolic reflector with a solar tracking system and the flow dynamics of multiple jets impinging on the porous surface is analyzed using Particle Image Velocimetry (PIV). The results show that the tested absorber surface increases the solar absorption efficiency by 6.5 %, compared to the smooth surface. Furthermore, the jet’s flow dynamics and heat transfer analysis shows that the porous surface combined with the air jets increases the heat transfer rate, obtaining optimal values for jets velocities ranging between 5 and 10 ms−1.
Experimental Investigation on the Use of Vibration Signals Combined With Supervised Classification to Predict Radial Load Condition in Roller Element Bearings
Authors: Abu-Mahfouz Issam , Banerjee Amit , Rahman Esfakur 
Published: February 2023

Abstract
In this work, vibration response of a rolling element bearing under the influence of static radial loading is investigated. Radial loading results in a periodically varying stiffness (or compliance) which causes a cyclic dynamic response of the bearing assembly even under perfect balancing and other operating load conditions. These loads cause high stresses to develop in bearing elements and may cause fatigue, cracks, and spalls that limits the life of these components. A special bearing test rig was designed and manufactured to apply varying levels of radial load and measure the vibration response of the loaded roller bearing. The test is focused on new bearings free from any faults or defects. The radial load is varied in steps and the vibration signal is collected and analyzed at each level for different rotor speeds. The spectral components are analyzed using Fast Fourier Transform (FFT) and time-frequency wavelet transform. Statistical techniques are applied to both the vibration signature obtained using a piezoelectric accelerometer sensor and the wavelet decomposed approximations and details of the original vibration signals. The statistical measures, wavelet approximation and details are first processed for feature set reduction since many of the features are highly correlated. This is done using three feature reduction and subset selection methods — ReliefF, Recursive Feature Extraction (RFE) and Multi-Cluster Feature Selection (MCFS). These features and the original extracted features are used as features to train two classifiers. The classification is used to estimate high and low thresholds for both radial load and running speed. The classifiers used are (1) radial-basis function support vector machine (RBF-SVM), and (2) k-nearest neighbor (kNN). Performance of machine learning algorithms depends on the training data and physical collected datasets are often limited to specific operating conditions, necessitating the use of training with many models using multi-fold cross-validated subsets. In this study we have used ten models using two-fold cross validation for training and validation. The classification results reported are average of these models. In limited experimentation, the RBF-SVM outperforms the kNN classifier and among the feature sets used, the ReliefF set seems marginally superior to the other sets. However, the accuracy, precision, and recall (combined as an F-score) of the original extracted feature set are better than the reduced feature sets; the downside being the relatively high run time in the training phase.
Investigation of the Dimensional Accuracy of Additively Manufactured High-Temperature Material (PEEK)
Authors: Atatreh Saleh , Alyammahi Mozah Saeed , Susantyoko Rahmat Agung , Mohammed Abdallah 
Published: February 2023

Abstract
Additive Manufacturing (AM) of spare parts is a compelling production route with various benefits to the industry in terms of cost, lead time, and quality. Multiple research studies have highlighted the positive impact of high-performance manufacturing polymers such as Polyether Ether Ketone (PEEK) using Fused Filament Fabrication (FFF) technology in industrial and medical applications. With regards to the dimensional accuracy of the end products after 3D printing and associated thermal post-processing, there is a need for a systematic approach to quantitative evaluation. In this research study, we proposed a new benchmarked design of a test part along with a design of an experiment to evaluate the dimensional accuracy of PEEK parts in spare parts applications. The results revealed a fair dimensional accuracy with a deviation less than 1.6% of the original design. Furthermore, we found no significant effect of annealing on the dimensional accuracy of the PEEK 3D printed parts.
Simulation for Optimization of a Filter Cake System
Authors: Johnson Alyssa , LeBleu Allison , Zhang Ning 
Published: February 2023

Abstract
The purpose of this project is to create a computational fluid dynamics (CFD) simulation of the cake filtration process for Agrilectric Research. Cake filtration is the process by which solid particulate is filtered out of a mixture of fluid and solid phases. As the mixture flows through the system, the particulate accumulates on a filter cloth to form a filter cake, which acts as an added filtering medium to the system. The results obtained from this project can be used to aid Agrilectric in the development of their experimental testing methods by providing the ability to measure and predict filtration properties and to experiment with different filter cake designs to improve filtration methods and performance, without reliance on empirical data.
The work was completed using the Workbench platform with the Fluent solver from the Ansys Student software bundle. A 20 cm long, 10 cm diameter filter cell was modeled and meshed through Workbench and was imported into Fluent to conduct the simulation. The flow of a mixture of water and a particulate phase of rice hull ash (RHA) was simulated using the Eulerian multiphase model. This would show the movement of the RHA through the system and the accumulation of the particulate on a filter cloth over time. The filter cloth was modeled using a porous cell zone to mimic filtering properties.
The simulated solution shows RHA flowing into the system and initially passing through the filter cloth. After some time, a packed layer of particulate forms across the width of the filter cloth that allows a filter cake to accumulate in the system.
The results from the multiphase simulation prove that with more research and simulation development, the model can be improved to represent a realistic cake filtration system which can be used to supplement or to replace existing laboratory testing.