Mechanical Machine Research Articles

Overview of Dual Mechanical Port Machines in Transportation Electrification

Overview of Dual Mechanical Port Machines in Transportation Electrification

An experimental study on material removal rate and surface roughness of Cu-Al-Mn ternary shape memory alloys using CNC end milling

Abstract This study investigates the impact of Computer Numerical Control (CNC) milling parameters on Cu-Al-Mn SMAs (Shape memory alloys) to evaluate the effects on Surface Roughness (SR) and Material Removal Rate (MRR). The primary variables examined comprise of cutting speed, feed rate, and depth of cut. Results indicate that the Shape Memory Effect (SME) is higher in Copper Aluminium Manganese (CAM 3) compared to CAM 1 and CAM 2, with SME improving from 3.5% to 5.5% as Manganese (Mn) content increases, reflecting an increase in dislocations within the metal's crystal structure. Surface roughness increases with higher feed rates and depths of cut but decreases with increased cutting speed. MRR shows a positive correlation with feed rate, depth of cut, and cutting speed, though it decreases with higher Mn content. Notably, CAM 3 exhibits lower MRR compared to CAM 1 and CAM 2. Scanning Electron Microscopy (SEM) reveals that at lower feed rates (0.10 mm/rev), the surface is smooth and free of ridges or feed marks, while at higher feed rates (0.18 mm/rev), noticeable surface imperfections and plastic deformation occur. The addition of Mn improves surface smoothness and machinability, it also affects MRR. Further suggesting that Mn content and milling parameters significantly influence both the mechanical properties and machinability of Cu-Al-Mn SMAs respectively.

Foreword

The Congress of Automotive and Transport Engineering is an annual scientific event initiated in 1990 by the Society of Automotive Engineers of Romania (SIAR) and various Romanian universities with an interest in Automotive and Transport Engineering. This Congress is endorsed by EAEC (European Automotive Engineers Cooperation). The XXXII-nd edition of SIARS’s International Congress with the theme “Automotive and Transport Engineering” and the XVII-th European Automotive Congress was organized for the third time by Department of Mechanical Machines, Equipment and Transportation from the Politehnica University Timisoara. The Congress purpose is to gather members from academia, industry and government affiliated presenting their possibilities for investigations and latest research, to achieve collaborations in the near future in the field of automotive and transport engineering. The Congress hosted 201 participants from Europe, United States of America, Moldova presenting their scientific achievements within plenary sessions and congress sections. Thus, the auditorium had the opportunity to: • Gain insight into the cutting edge automotive and transport industry and ideas for future developments; • Update their skills and knowledge by attending focused technical sessions; • Network with potential new partners, clients and suppliers; • View the latest technology products and services in the technical exhibition. The papers included in this volume were selected by the Scientific Committee amongst the ones presented within the Congress and are covering the latest issues such as advanced powertrain and propulsion, automotive design and testing, terrain vehicles, advanced engineering methods, materials, automotive technology and maintenance, road safety, mobility of things. EAEC-MVT 2022 Student Congress was integrated into the main congress and offered students from around the world the opportunity to participate at an international conference and to present their papers on the main congress topics. This was also the meeting place for the young students (SIAR junior’s members) in an International Congress. We hope that these papers will be of interest to experts from academia and industry around the world and we wish them to create a favourable framework for addressing key issues in the field of Automotive and Road Transport Engineering that concern contemporary society today. Yours faithfully, EAEC-MVT 2022 Proceedings Editors List of Acknowledgement, Organizing Committee, Sponsors are available in this Pdf.

Amino-functionalized magnetic humic acid nanoparticles for enhanced Pb(II) adsorption: Mechanism analysis and machine learning prediction

It is an effective removal method to adsorb heavy metals by adsorption materials, so as to realize their separation from environmental media. Optimizing the structure of the materials and adsorption conditions are essential for improving the adsorption performance. Amino-functionalized magnetic humic acid nanoparticles (AF-MHA) was synthesized using a co-precipitation method. And the adsorbing material showed remarkable lead (Pb(II)) adsorption capacity under neutral pH conditions and a maximum adsorption capacity of 119 mg/g. The adsorption process was attributed to complexation with functional groups like amine, carboxyl, and phenol hydroxyl. In order to enhance the optimization of adsorption parameters, machine learning (ML) models including Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and CatBoost were employed. After comparative study we find the CatBoost model was found to be the most accurate predictor with a correlation coefficient of R2=0.95. ML also facilitated the identification of key factors influencing adsorption capability by assessing the importance of input features. With ML-assisted optimization parameters offering a strategic advantage in optimizing adsorption conditions and enhancing performance, AF-MHA is a promising adsorbent for treating divalent metal-contaminated water.

Effect of Ti and S Content on the Properties and Machinability of Low-Carbon Ferritic–Pearlitic Steel

This research was focused on the effect of Ti and S content on the formation of non-metallic inclusions and their influence on the mechanical properties and machinability of low-carbon ferritic–pearlitic steels. An analysis and classification of the non-metallic inclusions were carried out. The tensile strength and impact toughness were determined from samples taken in the rolling direction. Machinability investigations were carried out on a CNC turning machine and by analyzing the surface roughness. TiO-TiN inclusions are present in steels with an increased Ti content. In these steels, the hardness, tensile strength, and cutting forces increase with a higher proportion of Ti. In the second group of steels with increased contents of S, Al, and Ca, MnS and CaO-Al2O3-MnS non-metallic inclusions are formed. As the S content increases, the tensile strength and cutting forces decrease, while the impact toughness increases. In steels with added Ti, the machining is more difficult, but a finer surface is achieved after turning, while a higher S content results in an increased fraction of softer sulfide inclusions, which reduce the cutting forces but also result in a reduced surface quality.

Enhancement of mechanical properties and machinability of aluminium composites by cupola slag reinforcements

AbstractRapid evolution in engineering and manufacturing demands light weight material with enhanced mechanical properties. Aluminium metal matrix composites with ceramic reinforcement particulates serve this demand due to their reduced density, improved strength and hardness along with higher resistance to wear and corrosion. However, the material cost and reduced machinability hinders the full potential of utilization. To overcome these challenges, in this work cupola slag, an industrial waste generated as by product of cast iron production, has been incorporated as reinforcement of aluminium composites using low cost stir casting method. The enhancement of material properties and machinability by cupola slag inclusion on base alloy has been investigated in detail. Moreover, introspection about the underlying mechanisms responsible for the improvement in material properties has been studied using detailed microstructure and fractographic analysis. The results show improvement of in material properties and machinability up to 7 wt.–% cupola slag inclusion, beyond which the reduced wettablity prohibits sound castings. The ultimate tensile strength and specific strength observed to be improved by 18.20 % and 33.23 % respectively for 7 wt.–% slag reinforced composites when compared with base alloy. This work can be a successful addition to the knowledge pool of ongoing research on low‐cost novel material with enhanced properties.

CNC Mechanical Machine and Musical Sound Analysis of Zero Crossing Rates (ZCR) by Artificial Intelligence Based Tools.

CNC Mechanical Machine and Musical Sound Analysis of Zero Crossing Rates (ZCR) by Artificial Intelligence Based Tools.

The effects of setup parameters on the measured kinetic output of cervical disc prostheses

Mechanical testing machines are used to evaluate kinematics, kinetics, wear, and efficacy of spinal implants. The simulation of "physiological" spinal loading conditions necessitates the simultaneous use of multiple actuators. The challenge in achieving a desired loading profile lies in achieving close synchronization of these actuators. Errors in load application can be attributed to both the control system and the intrinsic sample response. Moreover, the presence of friction in the setup can have an impact on the measured outcome. The optimization of setup parameters can substantially improve the ability to simulate spinal loading conditions and obtain reliable data on implant performance. In this study, a reproducible kinematic test protocol was developed to evaluate the sensitivity of the kinetic response (i.e., measured loads, moments, and stiffnesses) of a cervical disc prosthesis to several testing parameters. In this context, five ceramic ball and socket sample implants were mounted in a 6 DOF material testing machine and tested with a constant axial compressive force of 100 N in two motion modes: 1) flexion-extension (±7.5°) and 2) lateral bending (±6°). Parameters including rotation rate, slider friction, friction between the samples' articulating surfaces, and moment arm were considered to determine their effects on measured kinetic parameters. The sensitivity analysis indicated that all setup parameters except friction between the samples' articulating surfaces had a substantial effect on the results. The findings were then compared to predictions from a free body diagram to determine the optimal setup parameters. Consequently, the setup with the lowest rotation rate and employing passive sliders yielded results that were consistent with the free body diagram. This study demonstrated the significance of a comprehensive setup evaluation for reliable and reproducible testing of spinal implants, also for comparison between labs.

Study of ZnO nanoparticle-doped dental adhesives on enamels with fluorosis: Electron microscopy, elemental composition and shear bond strength analysis.

This study aimed to evaluate dental adhesives containing different concentrations of zinc oxide nanoparticles (ZnO-NPs) for their use in the treatment of dental fluorosis, observe the interaction of the adhesive on healthy enamel surfaces and with mild and moderate fluorosis, measure the adhesive strength and fluorosis, and determine the phosphorus (P) and calcium (Ca) content on these surfaces, as a reference for the potential use of this adhesive with ZnO-NPs for dental fluorosis treatment. Transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM) were used to characterise the ZnO-NPs and analyse the weight percentages of P and Ca in the enamel using X-ray energy dispersive spectroscopy (EDS) and the adhesive strength using a universal mechanical testing machine. FESEM characterisation revealed that the ZnO-NPs were less than 100nm in size, with quasi-spherical and hexagonal prism shapes. The synthesis of the ZnO-NPs was confirmed by TEM, revealing their hexagonal crystalline structure. The adhesive strength by the universal mechanical testing machine showed that the adhesive with a 3% wt. concentration of ZnO-NPs was better in the three groups of teeth, showing higher adhesive strength in teeth with mild (15.15MPa) and moderate (12.76MPa) fluorosis surfaces, and was even higher than that in healthy teeth (9.65MPa). EDS analysis showed that teeth with mild and moderate fluorosis had the highest weight percentages of P and Ca, but there were no statistically significant differences compared to healthy teeth and teeth treated with adhesives. Lay description: This study focused on testing a new dental adhesive containing small particles called ZnO nanoparticles (ZnO-NPs). This study aimed to demonstrate whether this adhesive with ZnO-NPs could be useful for treating dental fluorosis by improving its adhesion to teeth. One of the first objectives was to determine whether the dental adhesive could adhere better to teeth affected by mild or moderate fluorosis than to healthy teeth by measuring whether the levels of two important elements for healthy teeth, calcium (Ca) and phosphorus (P), were affected by the adhesive. The size and shape of the small particles and teeth with mild or moderate fluorosis were observed using scanning electron microscopy. The nanoparticles were small (< 100nm) and had specific quasi-spherical and hexagonal prismatic shapes. More damage to the enamel was observed in teeth with mild or moderate fluorosis than in healthy teeth. The adhesive strength test demonstrated that the dental adhesive with 3% ZnO-NPs had the best adhesion on all healthy conditions of teeth. It was particularly effective in teeth with mild or moderate fluorosis. Finally, the evaluation of the levels of P and Ca on the enamel showed that teeth with fluorosis had higher levels of these elements, but using the dental adhesive with ZnO-NPs did not change the levels of these elements significantly because the adhesive avoided greater detachment because of greater adhesion to these surfaces. In conclusion, adding these small particles to dental adhesives could be an option for treating teeth affected by fluorosis. It stuck well and did not affect the levels of the important elements in the teeth.

Geometry-modulated all organic 3D printed smart PLA fibers for flextension amplified giant mechanical energy harvesting and Machine learning assisted pressure mapping

Piezoelectret sensors offer a promising avenue for harvesting abundant mechanical energy in the environment, providing a sustainable power solution for low-powered electronics. However, effectively harnessing large impact mechanical energy remains a challenge. In this study, we introduce a novel 3D cylindrical self-powered piezoelectret sensor capable of converting irregular axial impact forces into uniform radial pressures, enabling efficient large-scale impact mechanical energy harvesting. The cylindrical piezoelectret sensor, operating via a flextensional mechanism, was fabricated using 3D printed poly(lactic acid) (PLA) fibers as the piezoelectret layer and poly(3,4-ethylenedioxythiophene) (PEDOT)-coated PLA as the electrode layer. Our cylindrical sensor demonstrates the output voltage, and current of ∼ 11.4 V and 7.2 μA, representing remarkable improvement of 250 % and 160 % respectively, compared to flat fiber membrane device. Furthermore, we leverage IoT connectivity and machine learning techniques to utilize the fabricated robotic sensor for pressure mapping applications. Through machine learning algorithms, we achieve a remarkable accuracy of 100 % in character identification based on sensor data. This work highlights the potential of organic robotic sensors for efficient energy harvesting and intelligent pressure mapping applications, paving the way for the development of sustainable and adaptive sensor systems for various real-world scenarios.

Quantum Mechanics Characterization of Non-Covalent Interaction in Nucleotide Fragments.

Accurate calculation of non-covalent interaction energies in nucleotides is crucial for understanding the driving forces governing nucleic acid structure and function, as well as developing advanced molecular mechanics forcefields or machine learning potentials tailored to nucleic acids. Here, we dissect the nucleotides' structure into three main constituents: nucleobases (A, G, C, T, and U), sugar moieties (ribose and deoxyribose), and phosphate group. The interactions among these fragments and between fragments and water were analyzed. Different quantum mechanical methods were compared for their accuracy in capturing the interaction energy. The non-covalent interaction energy was decomposed into electrostatics, exchange-repulsion, dispersion, and induction using two ab initio methods: Symmetry-Adapted Perturbation Theory (SAPT) and Absolutely Localized Molecular Orbitals (ALMO). These calculations provide a benchmark for different QM methods, in addition to providing a valuable understanding of the roles of various intermolecular forces in hydrogen bonding and aromatic stacking. With SAPT, a higher theory level and/or larger basis set did not necessarily give more accuracy. It is hard to know which combination would be best for a given system. In contrast, ALMO EDA2 did not show dependence on theory level or basis set; additionally, it is faster.

Mechanical Properties, Structure and Machinability of the H13 Tool Steel Produced By Material Extrusion

Mechanical Properties, Structure and Machinability of the H13 Tool Steel Produced By Material Extrusion

Fabrication of PVA-Silica Sol Wood Composites via Delignification and Freezing Pretreatment.

The efficient exploitation of planted fast-growing wood is crucial for enhancing wood resource utilization. In this study, the fast-growing poplar wood was modified by in situ impregnation through vacuum impregnation with polyvinyl alcohol and nano-silica sol as impregnation modifiers, combined with delignification-freezing pretreatment. The samples were characterized by FTIR, XRD, SEM, and the universal mechanical testing machine. The results showed that the wrinkle deformation and cracking of the wood blocks were greatly alleviated after the delignification-freezing pretreatment and the polyvinyl alcohol and nano-silica sol were successfully integrated into the wood. The resulting polyvinyl alcohol-silica sol poplar composites exhibited about 216%, 80% and 43% higher compressive strength with respect to delignified wood, natural wood and impregnated natural wood, respectively, thereby demonstrating superior mechanical properties and potential opportunities for value-added and efficient utilization of low-quality wood.

Viscoelastic Mechanical Signature-Based Machine Learning for Assessment of Liver Fibrosis and Its Regression

Viscoelastic mechanical characteristics of the liver play a crucial role in fibrosis and cancer progression. Despite the evolution of liver fibrosis, as a pivotal step toward cirrhosis and hepatoma, originates from molecular dysfunctions and further extends to cellular and tissue levels, current clinical assessment is still largely based on tissue level stiffness. This study introduces a novel multiscale viscoelastic signature-based machine learning model for liver fibrosis evaluation. Utilizing a hybrid hierarchical theory-microrheology approach, we unveil a universal two-stage power-law rheology capturing dynamic mechanical variations in diverse liver conditions. Our analysis of multiscale viscoelastic disparities through a self-similar hierarchical theoretical framework enhances our understanding of fibrosis evolution. Distinct mechanical signatures observed across liver states provide valuable insights for assessing fibrotic individuals and treatment responses at different spatial scales. Furthermore, we propose a series of threshold values for each marker in the diagnosis of liver fibrosis. Notably, based on these new viscoelastic signatures, we eventually propose a Light Gradient Boosting Machine (LightGBM) diagnostic model that outperforms conventional stiffness-based classification, offering superior diagnostic precision for fibrotic treatment. This research contributes to the growing knowledge of viscoelastic characteristics in soft tissues and holds promise for innovative diagnostic strategies in various diseases and cancers.

The Condenser PBJ cipher machine

Condenser PBJ is a mechanical cipher machine developed by Pavel Baráček-Jacquier (1885-1969). It was created between 1922 and 1924, and was used by the Czechoslovak diplomatic service until 1934. The machine was designed to be used as a tool for condensing telegraphic messages in a similar way to a codebook, but with encryption added to the result. The encryption algorithm is a polyalphabetic substitution cipher—an autokey cipher with a priming key of length 10. This autokey cipher is different however from the classical Vigenère autokey cipher. We have been able to fully reconstruct the encryption algorithm. We also present a detailed description of the machine, and point out some weaknesses in the cipher’s design.

Intelligent mechanical metamaterials towards learning static and dynamic behaviors

The exploration of intelligent machines has recently spurred the development of physical neural networks, a class of intelligent metamaterials capable of learning, whether in silico or in situ, from observed data. In this study, we introduce a back-propagation framework for lattice-based mechanical neural networks (MNNs) to achieve prescribed static and dynamic performance. This approach leverages the steady states of nodes for back-propagation, efficiently updating the learning degrees of freedom without prior knowledge of input loading. One-dimensional MNNs, trained with back-propagation in silico, can exhibit the desired behaviors on demand function as intelligent mechanical machines. The framework is then employed for the precise morphing control of the two-dimensional MNNs subjected to different static loads. Moreover, the intelligent MNNs are trained to execute classical machine learning tasks such as regression to tackle various deformation control tasks. Finally, the disordered MNNs are constructed and trained to demonstrate pre-programmed wave bandgap control ability, illustrating the versatility of the proposed approach as a platform for physical learning. Our approach presents an efficient pathway for the design of intelligent mechanical metamaterials for a wide range of static and dynamic target functionalities, positioning them as powerful engines for physical learning.

The rotational inertia of a rigid body

The role of moment for inertia in rotational dynamics is equivalent to mass in linear dynamics and can be understood as an object's resistance to rotational motion. It establishes the relationship between several quantities such as angular momentum, angular velocity, torque, and angular acceleration. Accurately calculating the moment of inertia is crucial for designing various rotating systems and mechanical devices in engineering. For example, when dealing with rotating mechanical parts or machines, their moment of inertia ensures stability and performance in design needs to be considered. In physics and engineering, the analysis of rotational motion for rigid bodies relies on the concept of moment of inertia. It allows us to understand the dynamic behavior of a rigid body around an axis, including applications such as rotational stability, gyroscopic motion, and conservation of angular momentum. In this paper, the rotational inertia of a rigid body is studied by different method. Calculating the moment of inertia helps us gain a deeper understanding of an object's inertial properties during rotational motion while providing an important foundation for engineering design, scientific research, and material analysis.

Fault Diagnosis of Hydropower Units Based on Gramian Angular Summation Field and Parallel CNN

To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model’s accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units.

Effect of infill pattern on the mechanical properties of PLA and ABS specimens prepared by FDM 3D printing

Fused deposition modelling (FDM) is a three-dimensional printing technique which has become more popular and spreading in many fields. In this process, the molten thermoplastic is deposited layer by layer using a heated nozzle. Users can set the infill pattern to save the material and printing time. The main objective of this study is to investigate the tensile properties of polylactic acid (PLA) and Acrylonitrile butadiene styrene (ABS) tensile specimens with different infill patterns such as lines, tri-hexagonal and lightning. Further, the test specimens were prepared as per ASTM D638 using Ender 3D printer with varying infill patterns. The specimens were subjected to tensile tests in a mechanical tensile testing machine. It was observed that the specimens with linear infill patterns obtained higher tensile strength than the tri-hexagonal and lightning pattern infilled specimens. Later on, a microstructural study was examined on the fractured surface using scanning-electron-microscopy (SEM). It shows that the line pattern specimens have more uniform structure when compared to hexagonal and lightning pattern specimens.

Application of small punch test to estimate mechanical behaviour of SUS304 austenitic stainless steel

The small punch test with an application of a relatively small specimen has recently become a reliable material mechanics testing method. In this study, the small punch test is set up based on the conventional mechanical testing machine for SUS304 stainless steel to evaluate the mechanical properties of SUS304 steel at different displacement rates of the punch in quasi-static loading condition in the case of with and without heat treatment. Although heat treatment has an insignificant effect on the microstructure and hardness of the material, the mechanical properties of the material in the small punch test are greatly reduced after heat treatment. Both cases with and without heat treatment have a similar tendency for the rate - sensitivity of the applied force - displacement curve. A higher value of force is applied to obtain the same value of displacement at a low displacement rate in the stable plastic deformation zone. Meanwhile, the maximum value of applied force is higher at a higher displacement rate in the stage that initiation of crack might appear. In the examined range of displacement rate, a positive rate - sensitivity of displacement at the maximum force. Therefore, a correlation between equivalent fracture strain and fracture toughness of the material can be achieved.