Machine Configuration Research Articles

Comparison of Flux-Trapped Superconducting Machine Configurations

Comparison of Flux-Trapped Superconducting Machine Configurations

Performance evaluation of the developed combined lines seeder through the forward speeds and seeding rates

The field experiment was conducted in one of the fields of Maysan Governorate / Al-Batira region in a silty clay soil during the 2023 winter season, with the aim of developing and evaluating the efficiency of the performance of a combined and advanced machine used for planting on lines with equal distances and appropriate depths, with covering the seeds and appropriate disintegration of the soil using spring needles, fertilizing at the same time, and opening the furrows. Using separators after attaching them to the seeder. The experiment was carried out using two machine configurations: the seeder alone (B) and the seeder with the furrow opener (BZ), which is equivalent to basin irrigation system and furrows, and two tractor speeds (s1, s2) (4 and 6 km/h), with the aim of knowing its effect on (moisture content and field efficiency). The same two formulations and speeds above were also used with three barley seed rates (T1, T2, T3) kg/dunum in order to determine their effect on (plant height and grain yield). The (W2) composition gave a decrease in field efficiency, as it was recorded (86.338%). The (W2) composition gave a decrease in moisture content (15,203%), plant height (109.47) cm, and grain yield (3,523) tons/ha. The (W1) composition gave the highest average of field efficiency, reaching (88.805%). The composition (W1) resulted in a decrease in average plant height, reaching 113.36 cm, moisture content (22,648%), and grain yield (4,028) tons/ha. The third seed rate (T3) gave the highest average of grain yield, reaching (4,168) tons/ha, and plant height (113.58) cm. The second speed (S2) gave the highest average of field efficiency (88,643%), moisture content (19,173%), plant height (110,778) cm, and grain yield (3,774) tons/ha. The interaction treatment (w1t3) recorded the highest plant height. The interaction treatment (s1t3) recorded the highest grain yield, reaching 4,317 kg/ha

Advancing high-resolution musculoskeletal ultrasound: a histology- and anatomy-driven approach for enhanced shoulder imaging. Part 2: Anterior and lateral shoulder.

Ultrasonography is a reliable imaging technique for the accurate diagnosis and evaluation of musculoskeletal disorders. Recent developments in ultrasound technology have significantly increased image resolution, making it possible to see anatomical features at almost microscopic dimensions. Current standards for standardized shoulder ultrasonography mostly depend on outdated machine types and configurations that may not fully utilize these high-resolution imaging capabilities. In this article, we give a clear and comprehensive introduction to high-resolution shoulder sonography, using histological and anatomical images from cadavers for comparison. Images collected using contemporary technology are shown, and international standard practices are considered. The examination and normal results are presented in a methodical manner, beginning posteriorly, moving frontally, then more anteriorly, and concluding with a lateral and optional axillary examination. This article focuses on the anterior and lateral shoulder.

Experimental study of the surface finishing of CNC magnetic abrasive finishing based on ANN

ABSTRACT CNC Magnetic Abrasive Finishing (CNC MAF) is famous for aluminium sheet processing. This study used an artificial neural network (ANN) analytical model with a Bayesian regularisation algorithm (BR) to examine how machine parameters (rotation speed, feed rate, depth of cut, and several passes) affect surface roughness during CNC MAF of aluminium sheet. Experimental and quantitative data proved its prediction ability. ANN model to select the best machine parameters for low surface roughness. The perfect numerical solution was experimentally tested, and surface morphology and roughness were used to evaluate processing quality under optimal machine parameters. The ANN model is also compared to Taguchi L16 for prediction and optimisation. Compared to ANN, prediction and optimal surface finishing mean absolute error are 96% lower. The results show that the ANN model is better at studying machine configuration during CNC MAF of Al sheet. SEM and 3D Optical profilometer were also used to analyze the Al sheet. The UV-VIS Spectrophotometer measures Al polished sample reflectance at 200–700 nm.

Configuration and multistep switching of machines in dynamic hybrid MTS/MTO production systems

This study proposes the configuration of MTS-/MTO-dedicated and hybrid (switchable) machines under a multistep switching policy where each hybrid machine can be switched stepwise and compares it to the existing model with MTS-dedicated and hybrid machines under a group switching policy that makes all hybrid machines switch together. Since companies have been required to satisfy wide varieties of customers' needs in recent years, the dynamic hybrid make-to-stock (MTS) / make-to-order (MTO) production system–one of the flexible production strategies with superior efficiency and productivity–is widely adopted in the garment industry. This system contains MTS and MTO streams in a shared facility and holds dedicated and hybrid machines that can be switched between MTS and MTO production. The machine configuration and switching policy are essential factors to increase the effectiveness of this system; hence, the features of such components must be investigated. Numerical experiments demonstrate that the cost and the number of MTO customers waiting can be reduced by appropriately setting the multistep switching policy and the machine configuration. Effective operating rules can also be achieved using our proposed search heuristics. These results contribute to the improvement of the existing dynamic hybrid system and the expansion of its possible applications.

An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system

Man-Machine Phased-Mission System (MMPMS) usually demands the cooperation of operators with different responsibilities and machines to accomplish multi-phase missions. Its machine configuration and human organization structure may change across phases, and phase dependencies of machine failures and human errors may exist. In current studies, the safety of man-machine system is usually analyzed qualitatively by System Theoretic Process Analysis (STPA) and assessed quantitatively by the integration of STPA with Bayesian Networks (BN). These studies only focus on single-phase systems and conduct single-phase BN while cannot address the features of MMPMS. In this paper, a qualitative analysis and quantitative assessment method for phase dependencies is proposed and integrated into the method that combines STPA and BN. Firstly, four types of phase dependencies in MMPMS are identified. Secondly, new mapping rules for phase dependencies are proposed to integrate single-phase BN into a multi-phase BN. Thirdly, the quantitative assessment method for phase dependencies considering the effects of human organization structure changes are proposed to quantify the parameters of multi-phase BN. Fourthly, the safety of MMPMS can be assessed through multi-phase BN. Finally, an Unmanned Aerial Vehicle system with three-phase missions is presented as a case study to demonstrate the effectiveness of the proposed method.

CaloFlux: a tool to estimate fluxes in calorimeters at colliders

In high-granularity calorimetry, as proposed for detectors at future electron-positron Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especially via the cooling and data acquisition systems. Estimating the data rate is a difficult task due to the very large number of channels, the complexity of the calorimeter system's geometry, and the intricacies of the readout system. The CaloFlux software package presented here eases this task by establishing the typical fluxes-deposited energy, number of cells above the electronics threshold, power consumption, and associated data-in rate histograms for selections of components, from fully simulated physics and background events by scaling with proper rates. The ILD calorimeter system is taken as a specific example, upon which different histograms are obtained for representative parts of the calorimeter and for various machine configurations. Examples of histograms are shown, along with details of the data used and the simulation.

Influence of device configuration and noise on a machine learning predictor for the selection of nanoparticle small-angle X-ray scattering models.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202-212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

Vibration Performance Analysis of a Yokeless Stator Axial Flux PM Motor with Distributed Winding for Electric Vehicle Application

This article presents a detailed analysis of the electromagnetic force and vibration behavior of a new axial flux permanent magnet (AFPM) machine with a yokeless stator and interior PM rotor. Firstly, the configuration of an AFPM machine with a dual rotor and a sandwiched stator is introduced, including the structural design, fixation of the yokeless stator and segmented skew rotor structure. Then, the influence of anisotropic material and a fixed structure on stator modes is analyzed, including elastic modulus, shear model, the skew angle of slot and the thickness of stator yoke. Furthermore, a new non-equally segmented skew rotor structure is proposed and calculated for the reduction in vibration based on the multiphysics model. Three different segmented skew rotor schemes are compared to illustrate the influence of reducing vibration and noise. The predicted results show that the effect of the non-equally segmented skew rotor on reducing vibration is better than the other two schemes. Finally, a 120 kW AFPM motor is experimented with and the result matches well with the predicted data. The vibration performance of the AFPM motor with a dual rotor and sandwiched yokeless stator is revealed comprehensively.

Impact of machine parameters and wall thickness on microstructural characteristics and microhardness of laser powder bed fusion Inconel 718 parts following heat-treatment

This study investigates a series of geometric feature build plates manufactured by multiple laser powder bed fusion (L-PBF) machine configurations, examining seven different wall thicknesses ranging from 0.1 to 2.0 mm. These build plates and thin wall specimens completed a full heat treatment cycle: stress relief (SR), HIP, solution, and aging per standards. The fully heat-treated (FHT) Inconel 718 wall specimens were sectioned from sixteen different geometric feature build plates built across fifteen different L-PBF machines. They were characterized by optical microscopy and EBSD image mapping. The microstructural evolution from the As-built sample, SR, HIP to FHT wall specimens from a single machine configuration was also obtained along with cooling rate simulations for 0.1 mm, 0.2 mm, 0.5 mm, and 0.8 mm wall thicknesses. The difference in cooling rates between the thick and thin walls was simulated to provide an understanding of microstructural evolution and evaluate the computer simulation as a tool to predict microstructural features. For FHT wall specimens, results indicate mostly equiaxed grain structures containing annealing twins, ranging in size from ∼21 μm to 93 μm parallel and perpendicular to the build direction. For most of the samples, the grain size was shown to increase with the increasing wall thickness due to slower solidification rates. The double aging composing the fully heat-treated thin walls also fully age-hardened the grain structures with gamma double-prime precipitates. This precipitation produced a median Vickers (HV) hardness for nominal section thicknesses >0.6 mm of ∼ HV 472; consistent with commercially heat-treated and optimized Inconel 718 products. Feature sizes >0.6 mm were reproducible for all L-PBF machine configurations.

Evaluating the potential of disaggregated memory systems for HPC applications

SummaryDisaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared across a data center. Cloud platforms have deployed such systems to improve overall system memory utilization, but performance can vary across workloads. High‐performance computing (HPC) is crucial in scientific and engineering applications, where HPC machines also face the issue of underutilized memory. As a result, improving system memory utilization while understanding workload performance is essential for HPC operators. Therefore, learning the potential of a disaggregated memory system before deployment is a critical step. This paper proposes a methodology for exploring the design space of a disaggregated memory system. It incorporates key metrics that affect performance on disaggregated memory systems: memory capacity, local and remote memory access ratio, injection bandwidth, and bisection bandwidth, providing an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our methodology to analyze thirteen diverse workloads, including AI training, data analysis, genomics, protein, fusion, atomic nuclei, and traditional HPC bookends. Our methodology demonstrates the ability to comprehend the potential and pitfalls of a disaggregated memory system and provides motivation for machine configurations. Our results show that eleven of our thirteen applications can leverage injection bandwidth disaggregated memory without affecting performance, while one pays a rack bisection bandwidth penalty and two pay the system‐wide bisection bandwidth penalty. In addition, we also show that intra‐rack memory disaggregation would meet the application's memory requirement and provide enough remote memory bandwidth.

Electrical Machine Winding Performance Optimization by Multi-Objective Particle Swarm Algorithm

The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented.

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Online Measurement for Parameter Discovery in Fused Filament Fabrication

To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.

Diminishing Safety Margins of Telescoping-Boom Aerial Lifts

Abstract As the height of telescoping-boom aerial lifts increase, the severity of tip-over accidents obviously increases. To reduce the probability of tip-over accidents, manufacturers use countermeasures such as outriggers and wheel axels that expand in width to provide a more stable base, counterweights to offset the moments generated by the telescoping boom, and controllers that limit the machine configurations to within stable envelopes. The size of stability margins is determined by industry standards that set the approved load capacity of the machine to less than the load that would induce tip-over. This paper investigates the effectiveness of such load-based safety margins for very tall aerial lifts with telescoping-booms. The results indicate that the industry standards result in both inconsistent and often low safety margins.

Simulating Dynamics of Feed Grain Vibration Grinder

The paper notes the feasibility of a vibration-based method for feed grain grinding. However, preference should be placed on dynamic machine configurations that enhance energy efficiency and increase the overall structural reliability. In this regard, it is worth considering the vibration grinder proposed in Patent RU 2688424C1. (Research purpose) The study aims to improve the technical capabilities of feed grain vibration grinders through the utilization of self-synchronization effects in vibration exciters and anti-resonance in working components. (Materials and methods) The authors developed a mathematical model for the dynamics of the working bodies in this type of a grain vibration grinder, taking into consideration the design of these bodies and their interaction with the technological environment. (Results and discussion) The findings show that effective execution of the technological process requires counter-rotating unbalance shafts and fine-tuning the working bodies to operate in an anti-resonance mode. The experiment confirms stable self-synchronization of vibration exciters in the anti-resonance mode of the working bodies, although the phasing of the unbalance shafts deviates slightly from the theoretical 180-degree mark, measuring between 168 and 170 degrees. This deviation does not have an adverse impact on the grinding process. As a result, the initial hypothesis combining the effects of self-synchronization in vibration exciters and anti-resonance in working bodies has received both theoretical and experimental confirmation. (Conclusions) It has been established that effective implementation of the technological process necessitates the counter-rotation of unbalance shafts, resulting in the self-synchronization of vibration exciters, and adjustment of the working bodies to an anti-resonance mode.

Machining performance optimization of graphene carbon fiber hybrid composite using TOPSIS-Taguchi approach

AbstractOptimization of process factors plays a significant role in process efficiency and effectiveness. In this context, an attempt has been made to access the optimized machining factors for polymer nanocomposites including Graphene oxide (GO)/Carbon fiber (CF). To do this, graphene concentration (wt%), feed rate (FR), and spindle speed (SS) have been chosen as governing factors and their performances have been characterized by delamination value (DV) and thrust force (TF). After defining the levels for these factors, the Taguchi experiment design method was used to obtain the experimental trial series. A TiAlN SiC-coated 06 mm drill bit was used in a CNC machine configuration to drill holes. Their corresponding performance values were noted down as DV and TF. TOPSIS method has been incorporated for accessing the measured performance dataset and relative closeness values have been calculated. These relative closeness values have been further subjected to Taguchi’s signal-to-noise ratio (S/N ratio) leading to the evaluation of an optimized parametric combination. 2 wt% of graphene, 100 mm/min of feed rate (FR), and 2100 rpm of spindle speed (SS) make up the ideal machining configuration. The mean response table indicated the SS as the most influential governing contrariant on the TF and DV. In addition, an assessment was conducted to determine the suitability of the model, and it was determined that the stated model does not exhibit any deficiencies or complications.

In-plane compressive responses and failure behaviors of composite sandwich plates with resin reinforced foam core

The paper presented an experimental study on the effect of the resin reinforced core configuration and core thickness on in-plane compressive responses and failure behaviors of composite sandwich specimens. Two resin reinforced core machining configurations were designed with three core thickness along. In-plane compressive load, displacement, strains on both sides, and failure morphology were closely monitored during the loading process. Meanwhile, the theoretical method also was supplementary to forecast the failures of sandwich structures. It was found that the enhancement of grooved, perforated holes and contour cut (GPC) core was better than double-side grooved and perforated hole (DGP) core to improve the in-plane compressive capacity of sandwich specimens for all thick cores. The core fracture or skin/core debonding failure of sandwich specimens resulted in an instant drop of in-plane compressive load, and the global buckling led to a slower reduction. The failure mode changed from global buckling to skin/core debonding at both sides as the core thickness increased for the Plain core sandwich specimen; switched from global buckling to a combined failure of core fracture and skin/core debonding at both sides, and then to skin/core debonding at both sides for the DGP core sandwich specimen; the skin/core debonding at the shallow side occurred for all GPC core specimens. The slight buckling trace of strains before the peak load probably triggered the skin/core debonding of sandwich specimens. The theoretical method could well forecast failure loads and corresponding failure modes of sandwich specimens with the 15 mm thick core, and reasonably predict failure loads for sandwich specimens with 30 mm and 45 mm thick cores.

Reconfigurable open architecture control system with integrated digital twin for 3-axis woodworking milling machine

ABSTRACT This study proposes a reconfigurable open architecture control system for the Computer Numerical Control (CNC) woodworking educational machine tool called “EMCO F1 CNC”. On this machine physical hardware reconfiguration (spindle position can be vertical and horizontal) is possible. The idea was to develop a control system that would follow the configuration changes of the machine tool. The educational character of the machine and its reconfigurability demand possibilities for machining programme simulations in order to determine optimal workpiece position in the workspace and programme verification. Those are the reasons for developing a virtual machine tool integrated with the control system as a digital twin. The study presents a novel methodology for the development of control systems, merging the concept of “virtual commissioning” with iterative error-driven processes. The objective was to develop a dynamic control system capable of promptly adjusting to kinematics changes in real time. In a wood machining experiment, a test workpiece for machine tools for both machine configurations verified the operation of the developed control system.

A hybrid method to solve reliability-cost-oriented bi-objective machine configuration problem for a flow shop system

Machine configuration is a crucial strategic decision in designing a flow shop system (FSS) and directly affects its performance. This involves selecting device suppliers and determining the number of machines to be configured. This study addresses a bi-objective optimization problem for an FSS that considers repair actions and aims to determine the most suitable machine configuration that balances the production reliability and purchase cost. A nondominated sorting genetic algorithm II (NSGA-II) is used to determine all the Pareto solutions. The technique for order preference by similarity to an ideal solution is then used to identify a compromise alternative. It is necessary to assess the production reliability of any machine configuration identified by the NSGA-II. The FSS under the machine configuration is modeled as a multistate flow shop network, and Absorbing Markov Chain and Recursive Sum of Disjoint Products are integrated into the NSGA-II for reliability evaluation. The experimental results of solar cell manufacturing demonstrate the applicability of the proposed hybrid method and validate the efficiency of the NSGA-II compared with an improved strength Pareto evolutionary algorithm.