Engineering Research Express最新文献

{"title":"Effect of process variables on microhardness and surface roughness in SPIF of beryllium copper","authors":"Nikesh Verma, Vikas Kumar","doi":"10.1088/2631-8695/ad72ce","DOIUrl":"https://doi.org/10.1088/2631-8695/ad72ce","url":null,"abstract":"Beryllium copper is gaining the popularity among material research community owing to its excellent properties like high strength-to-weight ratio, corrosion resistance, and high electrical conductivity. This work aims to enhance the performance of ‘Single Point Incremental Forming (SPIF)’ of Beryllium copper using desirability function analysis (DFA). Feed rate (FEED), Step size (SS), wall angle (WA), and tool rotation speed (TRPM) are the four vital input parameters that have been chosen. This research investigates the effects of variations in these four factors on the truncated cone’s surface roughness and microhardness. After testing different levels of these parameters, the optimal setting using desirability function analysis was 0.4 mm step size, 200 mm min⁻¹ feed rate, 47° wall angle, and a tool rotation speed of 1500 rpm with desirability of 70.7%. Further, measured data was subjected to Analysis of Variance (ANOVA) tests to determine the significance of all parameters on the responses. Results of the ANOVA analysis showed that the step size and tool rotation speed had the most profound impact on surface roughness and micro-hardness respectively. Feed rate and wall angle were the least significant parameters in affecting the surface roughness and micro-hardness respectively. The confirmation experiments validated fine-tuning with predicted and experimental values.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"9 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pyrometallurgical processing of manganese ore","authors":"A Behera, P Patel, A Ghosh, A Tripathy, S K Sahoo","doi":"10.1088/2631-8695/ad70f2","DOIUrl":"https://doi.org/10.1088/2631-8695/ad70f2","url":null,"abstract":"Manganese ore contains valuable oxides, such as manganese oxide (MnO₂/Mn₂O₃/Mn₃O₄), iron oxide (Fe₂O₃) and silica (SiO₂). In the present study, an attempt has been made to separate these oxides through pyrometallurgical processing routes for the extraction of ferromanganese and silicomanganese. Lean manganese ores were subjected to ball milling followed by carbothermic reduction at a temperature of 1200 °C for 2 h. The reduced ores were then subjected to wet magnetic separation to obtain the magnetic constituents in the reduced ores. It was observed that it is not possible to remove the silica content from the ore. It appeared that the reduced sample had oxides of iron, silicon and manganese after its wet magnetic separation. The magnetic constituents were subjected to metallothermic reduction through pure aluminium powders to recover valuable metals/alloys.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"52 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental investigation of hybrid reinforced A356 aluminium alloy synthesised by stir casting technique","authors":"Niteesh Pawar, Shivprakash Barve, Pralhad Pesode","doi":"10.1088/2631-8695/ad722c","DOIUrl":"https://doi.org/10.1088/2631-8695/ad722c","url":null,"abstract":"One of the most significant alloys to be employed in the automotive, aerospace, and military industries in recent years is A356 aluminium. Because of A356’s excellent compatibility with other metals and nanoparticles, novel hybrid composites may be made using it. The characteristics of these hybrid composites are mostly the result of the additives’ interaction with the A356 alloy’s current elemental composition. Aluminium composites were synthesized through stir casting method by reinforcing 2%, and 4% SiC, 2% and 4% Al₂O₃ and 0.5%, 1%, 1.5%, 2% and 2.5% SiC and Al₂O₃ both. The homogeneous distribution of SiC and Al₂O₃ microparticles in reinforced composite is revealed by scanning electron microscopy (SEM). The addition of SiC and Al₂O₃ reinforcements greatly improved the mechanical characteristics of the synthesised composites; for example, a composite with 4% SiC reinforcement reached its maximum hardness and maximum tensile strength of 165 HV and 257 MPa respectively. Maximum elongation of 6.72% was observed for 0.5% SiC and 0.5% Al₂O₃ reinforced composite. Minimum wear rate is observed for 4% SiC reinforced composite material. This study aims to identify gaps in the potential variations and compatibility of various additives with one another in order to create a brand-new hybrid reinforced alloy suitable for automotive braking system applications: brake rotors made of a disc or a brake pad, depending on the properties of the hybrid reinforced alloy that was made. Hence, the current work presented focuses on the preparation of hybrid reinforcement of A356 with silicon carbide and alumina powders.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"68 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity optimization of monolithic integrated refractive index sensor based on grating LED","authors":"Xumin Gao, Xuehui Yu, Dongmei Wu, Mingyuan Xie, Zheng Shi","doi":"10.1088/2631-8695/ad7197","DOIUrl":"https://doi.org/10.1088/2631-8695/ad7197","url":null,"abstract":"In this study, we present a potential ultra-thin refractive index sensor model that utilizes a monolithically integrated gallium nitride-based light-emitting diode platform. This light-emitting diode not only emits light but also detects changes in refractive index. The sensitivity of this sensor is defined as the response of light extraction efficiency to unit changes in refractive index. We have incorporated a one-dimensional grating on the surface of the light-emitting diode to investigate the effects of the grating’s modulation on the light field and the sensor’s sensitivity.Through strategic optimization of the grating structure, we have significantly enhanced the sensitivity of the sensor. Our results indicate that, compared to a conventional flat light-emitting diode, the optimized grating structure increases the light extraction efficiency by approximately 2 to 3 times. Furthermore, the sensitivity of the sensor has achieved a maximal enhancement of up to 41-fold.The device offers a compact design and demonstrates high levels of light extraction efficiency and sensitivity, making it highly suitable for monolithic integration in optical sensing applications. This advancement provides a substantial contribution to the field of optical sensing, indicating promising potential for future research and application.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"73 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confluence of electronic structure calculations (DFT) and machine learning (ML) for lithium and sodium-ion batteries: a theoretical perspective","authors":"Henu Sharma, Vinay Katari, Kisor K Sahu, Anjali Singh","doi":"10.1088/2631-8695/ad708f","DOIUrl":"https://doi.org/10.1088/2631-8695/ad708f","url":null,"abstract":"The world is rapidly transitioning towards clean energy solutions, and batteries are the key drivers of this transition. With increasing demand for large-scale energy storage systems, the need for cost-effective and sustainable battery storage systems is also increasing. Until now, lithium-ion batteries have completely dominated the commercial rechargeable battery storage space. Due to sodium’s greater affordability and abundance compared to lithium, sodium-ion batteries have drawn interest as a complementary technology to lithium-ion batteries in various applications, like grid storage devices. First-principles studies are often used today to effectively study the key properties of alkali-ion batteries that are difficult to access otherwise, such as the electronic structure effects, ion diffusivity, and quantitative comparison with experiments, to name a few. Understanding the electronic structure of battery materials can help researchers design more efficient and longer-lasting batteries. Recently, machine learning (ML) approaches have emerged as a very attractive tool both for prediction (forward) problems as well as design (or inverse) problems. Dramatic reductions in computational costs, coupled with the rapid development of ML tools in general and deep learning methods in particular, have kindled keen interest. This is so because they can supplement the traditional experimental, theoretical, and computational tools to significantly augment the quest for rapid development and deployment of new products. Furthermore, the integration of electronic structure calculations and ML benefits society by accelerating the development at considerably lower costs for more efficient and sustainable batteries, which can lead to longer-lasting portable devices, cleaner energy storage solutions, and lower environmental impact. This topical review article will focus on how density functional theory (DFT) and ML can facilitate Li-ion and Na-ion battery research via material discovery, rapid screening, and tuning of the electrode properties.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"9 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parametric noise reduction and construction of performance degradation indexes for low-voltage switchgear appliances","authors":"Shuxin Liu, Yankai Li","doi":"10.1088/2631-8695/ad7194","DOIUrl":"https://doi.org/10.1088/2631-8695/ad7194","url":null,"abstract":"Aiming at the problem that there is a lot of noise in the feature parameter data of low-voltage switchgear, which makes it difficult to extract useful information about the feature parameters and unable to construct the performance degradation index reasonably. This paper proposes a noise reduction method through Relative Entropy (KL) optimized Variational Mode Decomposition (VMD) combined with Non-Local Mean (NLM) and a method of constructing performance degradation indexes for low-voltage switching appliances through Convolutional Autoencoder (CAE). Firstly, the voltage and current signals are collected based on the full-life test platform of low-voltage switchgear, and the subset of feature parameters is extracted. Then, the feature parameters are decomposed into different components by relative entropy optimization VMD and the noise-dominant and effective components are selected based on the correlation. Secondly, NLM denoising is performed on the noise-dominated signal, and the noise-reduced signals are obtained by signal reconstruction. Finally, the construction of performance degradation indexes for multi-type switchgear is completed based on CAE. The final results show that the two noise reduction evaluation indicators including Noise Rejection Ratio (<italic toggle=\"yes\">NRR</italic>) and trendiness (<italic toggle=\"yes\">T</italic>), and the three performance degradation evaluation indexes including Goodness of Fit (<italic toggle=\"yes\">R</italic>), Root Mean Square Error (<italic toggle=\"yes\">RMSE</italic>), and Mean Absolute Percentage Error (<italic toggle=\"yes\">MAPE</italic>) all show optimal results, indicating that this paper’s method can better complete the construction of noise reduction and performance degradation indicators for the feature parameters of low-voltage switchgear.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"71 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSO based linear parameter varying-model predictive control for trajectory tracking of autonomous vehicles","authors":"Chala Abdulkadir Kedir, Chala Merga Abdissa","doi":"10.1088/2631-8695/ad722e","DOIUrl":"https://doi.org/10.1088/2631-8695/ad722e","url":null,"abstract":"In this paper, Linear Parameter Varying-Model Predictive Control (LPV-MPC) for trajectory tracking for Autonomous Vehicles (AVs) is proposed. This method is based on the time-varying LPV is the form of the state space representation from the mathematical model of the vehicle. The LPV representation form which uses the dynamic model of the vehicle allows the incorporation of time-varying dynamics, providing a more accurate representation of the vehicle's behavior. The designed LPV-MPC controller for AVs is specifically designed to handle constraints in trajectory tracking. To enhance its performance, Particle Swarm Optimization (PSO) is employed as an optimization technique. PSO is used to tune the weighting matrices of the control parameters, optimizing the system response and improving trajectory tracking performance. To evaluate the effectiveness of the LPV-MPC system, extensive simulations are conducted and results are compared with Linear and Non-Linear MPCs. The main benefit of using the LPV-MPC method is its ability to calculate solutions almost as good as the non-linear MPC version yet significantly reducing the computational cost. The capability of the LPV-MPC controller as compared to the linear version is in its effective tracking, particularly for the non-linear reference trajectories.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"23 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilayer multiband hybrid fractal antenna for public safety and 5G Sub-6 GHz bands","authors":"Saurabh Anand, Ashwini Kumar","doi":"10.1088/2631-8695/ad710a","DOIUrl":"https://doi.org/10.1088/2631-8695/ad710a","url":null,"abstract":"In this work, a multiband hybrid fractal antenna is developed for public safety and 5G Sub-6 GHz bands. Proposed antenna design is designed, analyzed, and optimized using HFSS simulator. Radiating element of the proposed antenna consists of Hybrid Fractal shape which is designed using Koch and Hilbert curve fractal geometry. Hybrid Fractal Antenna (HFA) is designed on a Multilayer FR4 substrate which has an overall size of 0.22 × 0.47 × 0.044 ƛ³. Parametric analysis of the proposed design is done to get the optimum results. Proposed HFA resonates at 2.64 GHz (2.34–2.80 GHz), 3.87, 4.54, 5.02, 5.35 GHz (3.73–5.55 GHz), and 6.42 GHz (5.70–6.72 GHz) with a gain of 4.2, 0.9, 2.9, 3.3, 4.6, and 3.7 dBi respectively. The proposed HFA is useful for 5G bands such as: N7 band, N38 band, N40 band, N41 band, N47 band, N53 band, N79 band, N90 band and N93 band, and also useful for public safety band (4.9 GHz). Prototype of the proposed HFA is fabricated and tested in lab for the verification of simulated results. The results show good performance in terms of reflection coefficient, gain, and bandwidth. Measured results are in good agreement with the simulated results.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing multiple inputs autoregressive models for bearing remaining useful life prediction","authors":"Junliang Wang, Qinghua Zhang, Guanhua Zhu, Guoxi Sun","doi":"10.1088/2631-8695/ad68c9","DOIUrl":"https://doi.org/10.1088/2631-8695/ad68c9","url":null,"abstract":"Accurate prediction of the remaining useful life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns. We introduce a novel multi-input autoregressive model to address this challenge in RUL prediction for bearings. Our approach uniquely integrates vibration signals with previously predicted RUL values, employing feature fusion to output current window RUL values. Through autoregressive iterations, the model attains a global receptive field, effectively overcoming the limitations in generalization. Furthermore, we innovatively incorporate a segmentation method and multiple training iterations to mitigate error accumulation in autoregressive models. Empirical evaluation on the PMH2012 dataset demonstrates that our model, compared to other backbone networks using similar autoregressive approaches, achieves significantly lower root mean square error (RMSE) and Score. Notably, it outperforms traditional autoregressive models that use label values as inputs and non-autoregressive networks, showing superior generalization abilities with a marked lead in RMSE and Score metrics.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"66 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gate mesa terminal with drain-field-plated β-Ga2O3 MOSFET with ultra-high power figure of merit","authors":"Yunfei Zhang, Suzhen Luan, Xuepei Cheng","doi":"10.1088/2631-8695/ad6ff2","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6ff2","url":null,"abstract":"In this article, a novel gate mesa terminal (GMT) device structure incorporating a drain field plate is proposed. This design features mesa terminals with varying bevel angles positioned atop the gate. The objective is to enhance the breakdown voltage (V_br) and reduce the on-resistance (R_on) of the lateral <italic toggle=\"yes\">β</italic>-Ga₂O₃ metal-oxide-semiconductor field-effect transistor (MOSFET). Through the implementation of the GMT structure, the peak electric field within the <italic toggle=\"yes\">β</italic>-Ga₂O₃ MOSFET is redirected towards the passivation layer. This effectively mitigates the electric field in the epitaxial layer, thereby increasing V_br. The optimal values for V_br, specific on-resistance (R_on,sp) and maximum transconductance (g_m) across various GMT structures are 4827 V, 9.9 mΩ·cm² and 15.32 mS/mm, respectively. These metrics represent a 2.63-fold, 0.88-fold, and 1.25-fold improvement compared to the non-GMT structure. Additionally, when the doping concentration of epitaxial layer is 1 × 10¹⁶ cm⁻³, the GMT achieves an enhanced threshold voltage of +0.26 V. By simulating different bevel angles, field plate parameters, epitaxial layer doping concentrations, and mesa thicknesses, an optimal power figure of merit (PFOM) of 1.914 GW cm⁻² is attained. This innovative design introduces a fresh concept for the development of the next generation of high voltage and high-power devices rated above 4 KV.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"51 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}