Engineering reports : open access最新文献

Grinding in conventional air atmospheres is affected by the formation of oxide and passivation layers, which alter friction, material removal behavior, and surface integrity. This study investigates the influence of an oxygen-free atmosphere on surface grinding by eliminating atmospheric oxygen through argon purging and the introduction of an Ar/SiH₄ gas mixture, achieving an extremely low oxygen partial pressure. Four materials with different oxygen affinities (Ti-6Al-4 V, AlSi10Mg, C45 steel, K40-UF) were machined under both air and oxygen-free conditions. Process forces, residual stresses, and surface roughness were evaluated to identify atmosphere-dependent effects. The oxygen-free atmosphere led to reduced normal grinding forces, most notably for the cemented carbide K40-UF, while tangential forces remained largely unchanged. Residual stresses shifted toward more favorable compressive levels for all materials except AlSi10Mg. Surface roughness parameters were mostly unaffected, with measurable changes in Svk and Sk only for Ti-6Al-4 V and minor variations for C45. The results indicate that oxygen suppression reduces friction and modifies surface interaction mechanisms, particularly under higher thermal loads. This study provides a systematic assessment of atmospheric oxygen as an influential process variable in grinding and highlights the material-dependent sensitivity of grinding mechanisms to oxygen-free conditions.

The establishment efficiency of the surrogate model is often affected by the multi-output problem during the establishment process. It is an urgent issue to solve how to establish a multi-output joint surrogate model more quickly while ensuring a certain level of accuracy. In recent years, the advancement of artificial intelligence technology has provided a more efficient measure for establishing a multi-output joint surrogate model. Multilayer perceptron (MLP) is one of the most widely employed deep learning models and is commonly used to establish the surrogate model. How to establish a reasonable MLP surrogate model is the presumption and basis of establishing a surrogate model. Based on a review of the pertinent literature pertaining to MLP as a surrogate model, this paper examines the techniques and methods of MLP establishment. This paper proposes a framework for the establishment of a multi-output MLP joint surrogate model based on the aforementioned techniques and methods, as well as the existing problems associated with its establishment. On the basis of this framework, a surrogate model for the behavior of dam structural is developed. By confirming the model evaluation index, the performance of the surrogate model for dam structural behavior can be determined to be satisfactory. In addition, the feasibility of this framework is demonstrated by comparing it with independent models that establish surrogate models one by one for multi-output.

The variable and unpredictable output from distributed generation (DG) like wind and solar creates new reliability concerns for distribution networks. Integrating DG on a large scale can unbalance the power supply and compromise quality, making accurate reliability assessment essential. This paper puts forward a new assessment method using a Transformer network. The proposed framework integrates physical modeling with deep learning. First, an improved minimum path algorithm is employed to theoretically evaluate system reliability, specifically modeling the load restoration capability of islanded microgrids. The resulting reliability indices are then discretized into specific intervals to construct a labeled dataset. Subsequently, the Transformer network is innovatively applied to learn the mapping between the stochastic output characteristics of DG and these reliability intervals. By transforming the difficult prediction challenge into a classification task, this method effectively overcomes the problem of non-smoothness in reliability data caused by discrete load restoration. We demonstrate the method's effectiveness on Feeder 4 of the IEEE RBTS 6-node test system. The proposed framework achieves fast online prediction, enabling dynamic monitoring, and proactive warnings against operational risks in the grid.

Existing object detection methods remain severely challenged by adverse weather and domain shifts. On the one hand, the significant distribution shift between clean and degraded samples under diverse weather conditions prevents models from robustly learning intrinsic object representations. On the other hand, drones are distant from objects, and even slight degradation may lead to significant loss of details. There is a lack of a unified and effective all-weather detection framework. To this end, a unified object detection method with degradation-aware and domain adaptive modeling is proposed. First, we design a degradation-aware module (DAM) that leverages amplitude characteristics in the frequency domain to explicitly model degradation patterns, enabling the detector to perceive various types of image quality deterioration. Second, we propose a domain-aware attention-based restoration expert system (DA-RES). It disentangles shared and domain-specific representations through a combination of domain-shared and domain-specific encoders, thereby suppressing category-irrelevant information while enhancing domain-specific useful cues. Finally, through embedding the degradation patterns identified by DAM into the target domain encoder, DA-RES performs multiscale feature restoration guided by degradation priors, thereby boosting downstream detection tasks against adverse conditions. Extensive experiments demonstrate that the proposed framework achieves robust detection performance under all-weather conditions, particularly in challenging degraded scenarios.

In the pursuit of lightweight, high-strength materials for automotive and aerospace applications, the improvement of hybrid metal matrix composites (MMCs) has gained significant attention. This work investigates the mechanical and microstructural characteristics of LM26 aluminum alloy reinforced with varying weight percentages (2–8 wt.%) of silicon carbide (SiC) and graphite particles using the stir casting method. The aim is to enhance the performance of conventional aluminum alloys by incorporating the synergistic effects of ceramic (SiC) and solid lubricant (graphite) reinforcements. The mechanical properties, such as hardness, tensile, compressive, and flexural strength, were evaluated. Mechanical testing revealed that the composite with 6 wt.% reinforcement exhibited maximum performance, with tensile strength of approximately 300 MPa, compressive strength around 480 MPa, flexural strength near 310 MPa, and hardness reaching 162 BHN. Unlike prior studies focusing on single reinforcements, this research systematically explores combined SiC–graphite effects on LM26 composites. SEM indicated relatively uniform dispersion of reinforcements with minimal agglomeration, while EDS and XRD confirmed phase and elemental composition without deleterious phases. An artificial neural network (ANN) model was developed to accurately forecast mechanical properties from reinforcement composition, showing strong predictive capability. The findings provide quantitative benchmarks and enhanced understanding crucial for designing advanced LM26/SiC/graphite hybrid composites for structural, automotive, and aerospace applications.

This study presents a mathematical analysis of electroosmotically modulated peristaltic transport of an Eyring-Powell fluid in a two dimensional microchannel. The walls of channel are propagating sinusoidal waves possesses an electric double layer (EDL) characterized by a constant zeta potential. Under the long-wavelength and low-Reynolds-number regime, the governing equations are simplified and solved analytically. The resulting nonlinear dynamical system is examined through a bifurcation analysis to identify critical points and characteristize their behavior under variations in the fluid flow parameters. Stream function plots and bifurcation diagrams reveal how electrokinetic forces govern flow regime transitions including the formation and destruction of trapped boluses. This work offers significant insight into electroosmotic control of complex biofluids in physiological and microscale pumping.

Characterization and testing of 3D-printed robotic compliant systems for lifespan assessment is time-consuming and costly. For this reason, this work introduces a computer vision approach for automated, non-invasive monitoring of grippers and evaluation of failures. The vision system first detects colored fiducial markers placed on key points of the gripper. The detection model was trained using synthetic data to ensure robustness to background, illumination, and gripper color variations. Then, the marker positions across frames are used to train and detect anomalies in the gripper's displacement. This is performed by thresholding the reconstructed signal over temporal analysis windows, using the reconstruction error as an anomaly score. Validation was performed on real 3D-printed grippers under controlled mechanical failures and uncontrolled lighting and background conditions, correctly classifying over 97% of actions corresponding to normal and anomalous gripper performance. The proposed framework offers a scalable and low-cost alternative to embedded sensors for monitoring gripper performance and detecting early failures.

The demand for miniaturized metallic components in electronics, biomedical devices, and aerospace necessitates sustainable micro-forming solutions. Conventional deep-drawing often suffers from stage complexity, excessive die use, and size-effect limitations. This study aims to optimize stage number, limiting drawing ratio (LDR), and diametrical reduction for sustainable fabrication of copper micro cups. Directionally rolled pure copper strips with 250% deformation (strain −3.5) and an initial thickness of 0.1895 mm were used. Finite element analysis (FEA) was performed to design multi-stage deep-drawing die sequences, with validation through experimental trials. Three strategies were investigated: a 4-stage process (30% reduction per stage), a 6-stage process (15% reduction), and an 8-stage process (15%–10% reductions). Experimental punch load, strain distribution, and thickness profiles were compared against simulation. Results showed that while the 4- and 6-stage processes failed due to thinning and fracture from reduced formability, the 8-stage design achieved defect-free cups with uniform wall thickness. Bidirectional rolling (BDR) yielded higher dimensional accuracy and reduced thinning compared to unidirectional rolling (UDR), as confirmed by ISO 24213 criteria. Optimizing stage number and LDR proved critical in controlling flow stress, minimizing die wear, and improving sustainability. The study focused on copper microparts of specified dimensions. Broader validation across alloys, geometries, and rolling conditions is required. The findings provide industries with a framework to reduce energy, material waste, and die consumption while ensuring micropart quality. This is the first integrated study combining grain-size-controlled copper blanks, FEA-driven multistage die design, and experimental validation for sustainable micro-deep drawing.

This study investigates the effects of heat treatment (HT) and severe shot peening (SSP) on the surface integrity of binder jetting (BJ) manufactured 316L stainless steel. While HT step was chosen for its proven effectiveness in relieving residual stresses in PBF-LB built 316L, it was observed to increase porosity in BJ samples from 2.5% to 7.5%. SSP alone, however, effectively enhanced surface hardness from 145 to 504 HV, introduced beneficial compressive residual stresses reaching −995 MPa at a depth of 91 μm (remaining compressive up to 300 μm), and reduced surface porosity to 0.45%. These improvements indicate a significant enhancement in surface integrity, thus potentially improving wear and fatigue resistance. The findings suggest that SSP is sufficient for optimizing surface properties in BJ components, offering an effective post-processing approach for high-performance applications.

In urban public spaces, maintaining high power quality is very much needed for reliable and efficient energy consumption. This study aims to develop and validate an effective design method focused on improving urban power quality through the integration of renewable wind energy. This research proposes a novel and unique design method for enhancing and improving power quality in urban areas by connecting the wind energy through the utilization of vertical-axis wind turbines (VAWTs). The whole concept of the proposed methods involves a structured methodology comprising system modeling, integration of VAWTs with a Unified Power Quality Conditioner (UPQC), and experimental validation to measure voltage stability, total harmonic distortion (THD) and reactive power performance. The UPQC, an advanced power electronic device, operates by combining series and shunt compensators to address a wide range of power quality disturbances simultaneously. The series compensator handles the whole voltage-related problem and the shunt compensators fully manage and coordinate the current-related issue. This dual compensation approach ensures synchronized mitigation of both voltage and current disturbances, thereby maintaining consistent grid performance. By utilizing wind energy harnessed from VAWTs, the recommended system provides an alternative and renewable source of power, minimizing dependencies on the conventional grids and improving the overall energy efficiencies. The vertical turbines are chosen due to their excellent adaptability and suitability for all urban environments, where space limitations and varying wind directions at all positions face significant challenges. The research contains a detailed analysis of the performance enhancement brought about by the UPQC in parallel with VAWTs, leading on key power quality metrics. Experimental results show a significant minimization in voltage sags and swells, with the normalized sag values improving by up to 75% and swell values by up to 65%. The method improves power stability and promotes sustainability by combining renewable energy with advanced power electronic solutions in urban areas.