Types Of Machines Research Articles

Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.

Damping approaches in nonlinear seismic time history analysis for large mobile equipment with uplift

AbstractIn large mobile equipment in seismically active regions, seismic stability is often a decisive design criterion, and the behaviour of this large equipment, which is characterised by uplift, tilting and rocking, can only be calculated by means of nonlinear time history analyses. Here, the model damping approach has a significant influence on the results of these analyses but there are no scientific approaches for large mobile equipment with nonlinear behaviour such as uplift. In this paper, possible theoretical damping approaches for nonlinear time history analysis of structures with uplift such as large mobile equipment are discussed and numerical studies are conducted to investigate the influence of damping in the model on the concise results of the typical behaviour of large mobile equipment under seismic excitation. A suitable new approach is to be developed and a way of practical implementation is being prepared. Experimental studies on real typical bucket wheel machines will identify and validate important natural frequencies and associated damping for the numerical approach. These can be applied in numerical models using the damping approach engineered in the theoretical part. Thus, realistic results of the numerical models under nonlinear seismic time history analyses can be expected and more precise conclusions on the distinctive behaviour of large mobile equipment with uplift can be drawn.

Comparing Optimization Methods for Radiation Therapy Patient Scheduling using Different Objectives

Radiation therapy (RT) is a medical treatment to kill cancer cells or shrink tumors. To manually schedule patients for RT is a time-consuming and challenging task. By the use of optimization, patient schedules for RT can be created automatically. This paper presents a study of different optimization methods for modeling and solving the RT patient scheduling problem, which can be used as decision support when implementing an automatic scheduling algorithm in practice. We introduce an Integer Programming (IP) model, a column generation IP model (CG-IP), and a Constraint Programming model. Patients are scheduled on multiple machine types considering their priority for treatment, session duration and allowed machines. Expected future arrivals of urgent patients are included in the models as placeholder patients. Since different cancer centers can have different scheduling objectives, the models are compared using multiple objective functions, including minimizing waiting times, and maximizing the fulfillment of patients’ preferences for treatment times. The test data is generated from historical data from Iridium Netwerk, Belgium’s largest cancer center with 10 linear accelerators. The results demonstrate that the CG-IP model can solve all the different problem instances to a mean optimality gap of less than 1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\\%$$\\end{document} within one hour. The proposed methodology provides a tool for automated scheduling of RT treatments and can be generally applied to RT centers.

Axial flux machine with non-slotted torus-ns rotor type. Design and investigate for electric traction

Introduction. The drive electric motor is one of the key components in the traction chain of an electric vehicle. Traditional radial flux motors used in electric vehicles, which use permanent magnets or induction motors in an electric field, are experiencing significant development aimed at optimizing their weight and cost. However, it can only go so far, so switching to a completely different type of machine, such as an axial flow, might be a good alternative. The novelty to this item is an axial flux permanent magnet motorization with non-slotted TORUS-NS rotor (single interior stator with two external rotors North-South) type housed in the wheel of the vehicle; this allows power to pass directly from the motor to the wheel, increasing the efficiency of the motor. System complexity is also less, as the transmission, differentials and driveshaft are eliminated. Purpose is to equip the electric car and choose the motor adapted to the application and the available space. The smaller size and weight allows for a lighter vehicle and more batteries, thus increasing range. The focus on customization is because vehicle performance is so dependent on the quality of the vehicle architecture , battery pack and axial flux motor design. The results obtained are in good agreement of accuracy, in particular the flux density at the air gap. The investigation is carried out by the finite element method. Machine model was run on Maxwell 16.0 business code.

Evolution and comparison of three typical permanent magnet machines for all-electric aircraft propulsion

Evolution and comparison of three typical permanent magnet machines for all-electric aircraft propulsion

Production as a link in the market of agricultural machinery for crop farming in Ukraine

The production of agricultural machinery and equipment in Ukraine is one of the main sources of supplying the market with resources and facilities for agricultural production, but as of 2023, it does not fully meet its needs. According to the relevance of the subject matter, the purpose of the study was to investigate the trends in the development of the agricultural engineering industry in Ukraine in dynamics, the impact of military operations on it, and to work out effective ways to overcome their negative consequences. The following methods were used: dialectical, statistical and economic, graphical, index, correlation and regression analysis, empirical, comparative analysis, analytical forecasting, abstract logical, etc. The principles of regional placement of agricultural engineering enterprises are defined, depending on the availability of raw materials, energy and labour resources. The structure of enterprises in the industry by category is determined – most of them are small enterprises (85.8-91.5%), but the majority are employed by medium-sized enterprises (59.1-79.3%). Due to military operations, the number of active enterprises in the industry has decreased by about 30 units. The trend of reducing the production of tractors, combine harvesters, and some types of tillage machines in Ukraine and the reasons for its occurrence are revealed. The prospects of production of sowing equipment in Ukraine are substantiated. The index of identified comparative advantages of different countries by individual types of machinery is analysed, and it is found that in Ukraine only tillage machinery has a high level of advantages – 1.802 and has optimistic prospects for development. The paper defines a close correlation between the export and production of tractors in Ukraine in 2010-2021 (the corresponding correlation coefficient – 0.8982). Problems that hinder the development of the agricultural machinery industry are identified: inflation, disruption of logistics links and loss of suppliers, high prices for energy resources, etc. Recommendations for overcoming them are proposed: creating favourable investment conditions; implementing protectionist customs and tax policies for industry enterprises; evacuating them from the war zone. The proposed measures will contribute to the restoration, preservation, and progressive innovative development of the potential of agricultural engineering un Ukraine

Electrohydraulic proportional position and pressure loading control utilizing a state perception and processing method

A new multifunctional proportional control triaxial stress loading apparatus is presented in this study, which can apply an output force on coal rock to simulate the conditions of triaxial stress loading, aiming at solving the problems of excessive consideration of static indices in the design and the incompleteness of the simulation and test verification system for the test parts at the performance analysis stage. This apparatus mainly combines the configuration of a triaxial stress loading system which can meet the stress loading requirements under different operating conditions, with the effective integration of multiple pressure loading operations based on the electrohydraulic proportional control method. In this context, a pressure and position combined control strategy based on the sliding mode is proposed to control the vertical and longitudinal loading hydraulic cylinders. Then, a co-simulation mode including the triaxial stress loading hydraulic system is established to verify the control strategy, system response characteristics and selection of the controller parameters. Furthermore, a multifunctional stress loading experimental platform is developed, and the stress loading characteristics with the proposed strategy are tested and analyzed. The results show that the triaxial stress loading hydraulic system can meet the response characteristics, the fluctuating deviation of the constant loading test can be restricted to 8.5%, the tracking error of the variable loading test is small, the minimum response time of instantaneous loading can reach 2.8 s, and the stress loading effect is noticeable. The experimental platform fully indicates that the stress loading system with the state perception and processing method as the core can meet a variety of verification indices of constant, variable and instantaneous loading tests. This research provides technical support for the smooth, synchronous control and intelligent operation of various types of hydraulic actuator machines.

Analysis of a rotary pump with two sectional impellers

The presented study shows aspects related to the fluid flow inside a rotary machine; this type of machine can be utilized as a ventilator, air pump or blower. Relations for computing the driving power of this pump as well as the fluid flow rate of transported are presented. The sealing between the impellers and between the impellers and the machine casing is studied and the advantages of different types of impeller shapes are specified.

The Role of Mass Spectrometry in Hepatocellular Carcinoma Biomarker Discovery.

Hepatocellular carcinoma (HCC) is the main liver malignancy and has a high mortality rate. The discovery of novel biomarkers for early diagnosis, prognosis, and stratification purposes has the potential to alleviate its disease burden. Mass spectrometry (MS) is one of the principal technologies used in metabolomics, with different experimental methods and machine types for different phases of the biomarker discovery process. Here, we review why MS applications are useful for liver cancer, explain the MS technique, and briefly summarise recent findings from metabolomic MS studies on HCC. We also discuss the current challenges and the direction for future research.

Machine learning in marketing: Recent progress and future research directions

Decision-making in marketing has changed dramatically in the past decade. Companies increasingly use algorithms to generate predictions for marketing decisions, such as which consumers to target with which offers. Such algorithmic decision-making promises to make marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Not surprisingly, machine learning is a trending topic for marketing researchers and practitioners. However, machine learning also introduces important challenges to the marketing landscape. We discuss this development by outlining recent progress and future research directions of machine learning in marketing. Specifically, we provide an overview of typical machine learning applications in marketing and present a guiding framework. We position the articles in the Journal of Business Research’s Special Issue on “Machine Learning in Marketing” within this framework and conclude by putting forward a research agenda to further guide future research in this area.

Processability approach for laser powder bed fusion of metallic alloys

Processability refers to the ease of achieving the required component while maintaining mechanical performance and processing schedules, which are critical for determining the cost and efficiency of using a given material, from the raw condition to the final product of any manufacturing process. Components built using the laser powder bed fusion with metallic alloys (LPBF-M) process show variability in their mechanical performance, which can be attributed to a range of process parameters and characteristics of the powder material employed by each type of machine. These variations are currently hindering the adoption of this technology at the industrial level. This paper presents a processability approach that could be applied in the LPBF-M to evaluate the possibility of speeding up productivity and minimising the effect on the mechanical properties and relative density and is defined based on the process parameters and powder material characteristics that generate the melting pool and meet bonding criteria at a specific build rate. A case study is carried out with stainless steel 316 (SS316), although this processability analysis could be applied to any other alloy. The results show that a wide range of process parameters generates a suitable processability interval with different values of the build rate. It is also found that slow build rates give rise to less variability in the mechanical properties, while faster rates produce more variability; this is caused by a fast-growing melt pool due to the use of high scan velocities for the SS316 alloy under study.

Division Linearization Zero-Bias Current Control for AMBs–Rotor System With Uncertainties and Saturation

Traditional constant current sum (CCS) distribution strategy is always employed in the nonlinear active magnetic bearing (AMB) system control for linearization, but the bias current in CCS causes extra power consumption and limits the AMBs' capacity. In this paper, a division linearization zero-bias current (DLZBC) distribution strategy is proposed as a substitution for CCS strategy. Under this strategy, a linear active disturbance rejection controller (LADRC) with a static anti-windup compensator (SAWC) is applied to deal with model uncertainties and current saturation. For different types of AMB-supported machines, a partial normalized nonlinear model is established. Subsequently, the performance of the CCS strategy is analyzed, and a DLZBC strategy is presented to reduce the power consumption and transform the system into a linear plant with a lumped uncertain parameter. Afterward, a LADRC is designed to enhance robustness, whose parameters are chosen according to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu -$</tex-math></inline-formula> synthesis framework and are convenient for adjustment practically. For anti-saturation, a SAWC is developed to help the system escape quickly from current saturation and enlarge the stability domain of the saturated system. Comparative simulations and experiments results show that the proposed control method can significantly reduce power consumption and achieve good response in AMB-rotor system.

UNIFICATION OF A NEW TYPE OF AXIAL PISTON HYDRAULIC MACHINES WITH DOUBLEROW CYLINDER BLOCKS

This article considers several designs of hydraulic machines with double-row inclined cylinder blocks with one and two oscillating units, which comprise a new type of axialpiston hydraulic machines. The hydraulic machines under consideration differ in terms of driving the cylinder blocks in a rotary motion, i.e., through cone rods with elongated pistons and through conic gears fixed to the blocks and drive discs. The former approach allows the working volume of the hydraulic machine to provide for the maximum angle of block inclination of 30°. The latter approach increases the working volume up to 50 % by increasing the block inclination angle to 40°, although implying a more complex design. Another specific feature of the considered designs consists in the symmetry of twin oscillating units in the former case and their non-symmetry in the latter case. This creates the possibility of developing standardized series of such hydraulic machines and unification of the major part of components. This question is esential for the development of series of standard sizes of such hydraulic machines including those with one double-row inclined block.

Analysis of Torque Capability for Permanent Magnet Machines With Magnetic Gear Effect

Permanent magnet (PM) machines have been widely applied in industrial applications due to the advantages of high torque density, high reliability, and high efficiency. In this paper, the magnetic gear effect (MGE) in PM machines is investigated in depth. A comprehensive derivation of the torque-generating process is carried out and a quantitative analysis of the air-gap harmonics contributing to average torque under different configurational freedoms is studied. The improvement of torque capability for the PM machines with MGE is discussed. The electromagnetic properties of typical PM machines with MGE are analyzed based on the magnetic circuit analytical model, and the effectiveness of theoretical analysis is verified by finite element analysis and experimental prototype. It is shown that the PM machines with an independent modulation unit possess the characteristics of high torque density and large PM utilization rate. And the PM machines with tooth-pole modulation unit have strong overload capability.

Pembangunan Pangkalan Data Daya Pemotongan S45C untuk Pemonitoran Status Haus Mata Alat

The process of machining involves the removal of metal from the work material using one or more cutting tool and depending on the type of machine and machining process such as turning, milling, or grinding processes. These are the major machining processes in the manufacturing industry. In this study, a turning process of S45C carbon steel work material was conducted using a carbide cutting tool. Due to its desirable characteristics in term of qualities and affordable for mass production, S45C material is frequently used in the industry to produce automotive components. The focus of this study was to develop a database for monitoring the condition of the cutting edge during the machining operation by referring to the cutting force and the average of flank wear land (VB) at the cutting edge. The machining process was conducted in a dry condition without lubricants or coolants. The machining parameters used were cutting speed, V = 200 m/min, feed rate, f = 0.2 mm/rev, and depth of cut, d = 0.25 mm. The machining condition chosen in this study is similar with the machining condition used in the industry for machining S45C. The machining results found that the tool life was 119 minutes at flank wear, Vb = 0.3 mm. The relationship between cutting force and flank wear has been developed to establish a database based on the 2D I-Kaz coefficient. The 2D I-Kaz coefficient is then used for the purpose of monitoring and predicting the status of the cutting tool condition in the next machining process. The online monitoring of the cutting tool condition is expected to be able to control the quality of the components produced as well as to ensure that only worn-out cutting tool is replaced.

Introducing an Experimental Approach to Predict Spot Scanning Time Parameters for a Superconducting Cyclotron Proton Therapy Machine

Proton pencil beam scanning (PBS) delivery sequence varies a lot among institutions due to the differences in vendors, machine types, and beamline configurations, which impacts PBS interplay effects and treatment delivery time estimation. This study aims to develop an independent experimental approach to predict the spot scanning time parameters for a clinical superconducting cyclotron proton therapy machine. This independent experimental approach employed an open-air parallel-plate detector with a temporal resolution of 0.05ms. A series of spot, energy, and dose rate patterns were designed and delivered, including (1) Spot switching time (SSWT) under different spot spacing for IEC-X, IEC-Y directions and diagonal direction (traveling in both X and Y direction) for three energy layers (110, 170 and 230 MeV); The Wilcoxon test is used to validate the prediction of SSWT along the diagonal direction. (2) Energy layer switching time (ELST) with different descending energy gaps for a fixed initial energy and different initial energies for a fixed descending energy gap. (3) Dose rate (MU/min) are measured for different minimum-MU-per-energy-layer (MMPEL), which are compared with the previous publication. A SSWT jump at 10mm (can be customized) spot spacing is observed because of triggering the machine's "raster mode" threshold. Discontinuous two variable piecewise linear functions were used to fit the SSWT in X/Y for spot spacing and energy. SSWT in X/Y is increasing as spot spacing and energy increase. SSWT in the diagonal direction is determined by the time either in the x-direction or y-direction, whichever takes longer (see Table 1 for one example of validations). ELST is linear depending on descending energy gap. The dose rate dependence on MMPEL is confirmed with previous publications of a similar type of machine. The study provided the first independent quantitative experimental modeling of the beam delivery time parameters without any information from vendors. Such machine-specific delivery sequence models could pave the foundation of precise interplay effect evaluation for clinical decision-making.

DESIGN AND EXPERIMENT TO DETERMINE OPERATING PARAMETERS FOR SUGARCANE PEELER ACCORDING TO THE PRINCIPLE OF CIRCULAR BRUSH

This paper presents a two-stage experimental process to determine the reasonable operating parameters of the sugarcane peeling machine based on the principle of circular brush. The first stage is to qualitatively determine the quality of sugarcane peeling to determine the speed range of the brush disk. The second stage specifies the value of rotational speed and cane push speed to be able to clean sugarcane with a diameter of the cane stem about 20-40 mm. Based on a reasonable set of parameters, the authors designed and fabricated a prototype of a sugarcane peeling machine with a spinning speed of 750 rpm and a cane pushing speed of 15-20 s/m. The machine has a productivity of 70 canes/hour and wears away an average thickness of about 0.767 mm, satisfying the condition of cleaning sugarcane. The research results are the basis for the mass production of sugarcane peeling machines according to the principle of circular brush with lower cost and higher productivity than other types of sugarcane peeling machines according to the principle of knife.

Reinforcement Learning Powered Station Parameter Optimized Radiation Therapy (SPORT): A Novel Treatment Planning and Beam Delivery Technique

Conventional intensity modulated radiation therapy (IMRT) with a typical 5-20 fixed beams often does not provide sufficient angular sampling required for conformal dose shaping, whereas current volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points without considering the differential need for intensity modulation of different angles, leading to undersampling at some angles while oversampling at some other angles. Our goal is to develop a node or station parameter optimized radiation therapy (SPORT) strategy with simultaneously optimized angular sampling and beam modulation by leveraging state-of-the-art reinforcement learning and the unique capability of modern digital LINACs in dose delivery through programmable nodal points. We developed a SPORT optimization framework, in which, the process of programming control points (or station parameters) was formulated as a stochastic dynamic programming problem, which was solved by a reinforcement learning-based algorithm. On-policy reinforcement learning method, namely, state-action-reward-state-action (SARSA) was integrated with deep convolutional neural network to predict station parameters by utilizing the patient's anatomical structures meanwhile considering the delivery capability of a typical digital LINAC machine. Here, the deep convolutional neural network estimated the state-action value by using the quality of the plan with current station parameters when a next potential station parameter was selected. The state-action value was then updated by SARSA learning. The quality of the plan was quantified by dosimetry constraints. The model was assessed by a retrospective study on a cohort of patients underwent head-and-neck radiation therapy. Dosimetric analysis and delivery efficiency comparisons were used to evaluate the performance of the proposed framework. Our model was used to generate 16 plans unseen in the original training set. All the plans predicted by our model achieved better dose distributions without violating clinical planning constraints. Moreover, instead of using 4 full standard arcs in the original clinically used plans obtained via manual optimization, the predicted plans only used one full standard arc (about 178 control points) plus boost from a few sub-arcs (less than 30 degrees of gantry angles), which significantly improved the efficiency of the beam delivery. We are in the process of integrating the sub-arcs into the full arc by considering the programmable capability of modern LINACs. We demonstrated that a machine learning-based SPORT framework capable of optimizing the spatial sampling and beam modulation simultaneously for modern radiation therapy. The framework not only significantly improves the quality and efficiency of beam delivery, but also has the potential to be incorporated into current clinical workflow to improve the efficiency of dose planning and delivery.

Modeling of the operation of a disc pump with the wall roughness consideration

Background: at present, a small number of studies of disk pumps operating on a low-viscosity liquid have been conducted. In addition, among the existing works, numerical calculations are presented, which have a serious discrepancy with the experiments carried out. This article is devoted to numerical simulation of the operation of a disk pump on water, comparison of the calculation results with experimental data.&#x0D; Aims: to determine the factors affecting the convergence of the main characteristics with experimental data when performing CFD calculations on a low-viscosity liquid.&#x0D; Methods: in this paper, a numerical modeling method based on the solution of discrete analogs of the basic equations of hydrodynamics is used. To compare CFD calculations with the experiment, a test bench was created on which two configurations of the impeller were studied.&#x0D; Results: it is shown that for this type of dynamic machines, it is important to take into account the influence of the roughness of solid walls when modeling their operation on a low-viscosity liquid, since it has a significant effect on the characteristics of the disk pump. The obtained characteristics are compared with experimental data, as well as flow patterns in the flow part.&#x0D; Conclusions: based on the results of the article, it can be argued that taking into account roughness in numerical calculations of a dynamic pump has a positive effect on convergence with experimental data.

Techniques and Applications for Deep Learning: A Review

Deep learning is a branch of machine learning that focuses on the development and refinement of complex neural networks for data analysis, prediction, and decision-making. Deep learning models use numerous layers of artificial neurons to automatically extract important features from raw data, making them superior at many tasks to typical machine learning models. Deep learning models' success in these fields has enhanced state-of-the-art performance and created new research and application prospects. Deep learning has been popular due to its capacity to tackle complicated issues in computer vision, natural language processing, speech recognition, and decision-making. In this study, we discuss deep learning techniques and applications, including recurrent neural networks, long short-term memory, convolutional neural networks, generative adversarial networks, and autoencoders. We also demonstrate deep learning's use in various fields. Deep learning has transformed artificial intelligence by enabling computers to learn from enormous datasets and accomplish complex tasks. As a result, scientists and engineers in fields as diverse as medicine, farming, manufacturing, and transportation have increased their focus on developing deep-learning methods and software. Current research trends and potential future paths in deep learning are also highlighted.