Recent Patents on Engineering - Current Issue

Examining the concrete quality in its original location and optimizing machine learning models for precise forecasting of concrete compressive strength (fc) is crucial. Current research advocates the fine tuning of hyperparameters within machine learning methodologies in tandem with non-destructive testing techniques to forecast the compressive strength of concrete.

This patent study aims to incorporate age as a crucial factor by utilizing data spanning from 3 days to 365 days. This approach enhances the study’s applicability for real-time forecasting purposes.

In the methodology of this current research, three machine learning (ML) models—specifically, Multi-Linear Regression (MLR), Decision Tree Regressor (DTR), and Random Forest Regressor (RFR)—are introduced within the context of age as a significant factor influencing measurements obtained from the Rebound Hammer (RN) and Ultra Sonic Pulse Velocity (UPV). These ML models were sequentially applied, followed by a meticulous process of hyperparameter fine-tuning conducted through grid search Cross-Validation (CV). To gain insights into the predictive results, the study also employed SHapley Additive exPlanations (SHAP) for interpretation purposes.

The results of this study reveal the development of an empirical relationship using Multi-Linear Regression, which yielded an R² value of 0.88. Furthermore, the evaluation showed that Random Forest Regression outperformed other models with an R² value of 0.95 in the training and 0.92 in the testing datasets. These models hold promise for facilitating decisions about qualitative analyses based on UPV and Rebound Hammer measurements relative to the age of the concrete. Rigorous validation of the models was conducted through standard cross-validation techniques.

The research has created and validated hyper tunned machine learning models with the help of grid search cross-validation function, with Random Forest Regression being the most effective. These models can potentially guide decisions regarding qualitative analyses using UPV and Rebound Hammer measurements concerning concrete age. They provide a valuable tool for on-site assessments in construction and structural evaluations. The primary objective of the research is to introduce age as a significant feature. To achieve this, data ranging from 3 days to 365 days was integrated. This inclusion aims to enhance real-time decision-making in construction processes, facilitating actions like the prompt removal of formwork in high-speed construction projects.

The patent of cutting operations is carried out with a cutting tool that is fed parallel to or at right angles to the work axis. The main objective of this study is to minimize surface roughness and MRR.

The effect of cutting parameters on surface roughness and material removal rate is investigated using AISI 1045 steel as a workpiece material, and single and double carbide cutting tools are used under dry machining conditions.

The cutting speed, feed rate, and depth of cut are considered input parameters for experimental purposes. Taguchi L9 orthogonal array design of experiments is used for designing the experiments. Parameters are optimized using Taguchi L9 orthogonal design of experiments and Analysis of Variance (ANOVA). MINITAB 17 software is used to solve the coefficients of the regression model.

The result indicates that the cutting speed was the most significant influencing factor that affects the surface roughness, followed by feed rate and depth of cut for both single and double-cutting tools.

The minimum surface finish for the best cutting parameter was 0.95 μm for a single and 0.92 μm for a double-turning tool. The highest material removal rates for single and double turning were 6456 mm³/min and 6603 mm³/min. The result shows that while using double tools, the rate of material removal rate increased and the machining time decreased.

Since the combination of its rapid processing speed and high energy input, laser beam welding is considered advanced and suitable for welding thin and lightweight metals. The residual stresses deposited in the parts as a result of rapid heating and cooling render laser-welded components susceptible to fractures and deformities.

In this patent, the modelling of the laser beam welding process during the joining of Ti-6Al-4V and AA6061 dissimilar metals to analyze the effects of the welding process on residual stress and elastic strain by considering beam radius, beam offset, welding speed, and beam power as input parameters.

The 3D model of the Ti-6Al-4V and AA6061 was developed using CATIA V5R16 software. The beam radius, beam offset, welding speed and beam power are the input parameters considered, and the output parameters are stress and elastic strain. Design Expert is used to design the experiment. ANOVA was used, and a mathematical model was developed to analyze the performance characteristics of the welding process.

The results revealed that increasing the laser power increases residual stress, whereas it decreases with increasing the other parameters. The maximum average equivalent von Mises stress was 288.79 MPa, which is near the yield strength of AA6061. The optimum welding conditions selected for minimum possible residual stress is 1600.003 W, welding speed 0.05 m/s, beam radius 0.014 m.

Based on the current observation during the simulation of joining dissimilar metals, the flow temperature along the weld line and weldment shows uneven distribution due to the dissimilarity of temperature-dependent properties of materials. The increased laser power leads to an increase in residual stress.

In the target detection technology of underwater robots, many patents and papers have aimed to enhance the accuracy of underwater target detection, but limited resources in underwater robots overlook lightweight detection methods.

In this study, we proposed an underwater target detection method using lightweight devices while ensuring high accuracy that could be maintained with limited resources. Our proposed algorithm leveraged the Ghost lightweight network, EMA mechanism, and CARAFE up-sampling technology to enhance YOLOv5s. To validate our method, comparative experiments, visual analysis, and ablation experiments were conducted.

The experimental results showed that our algorithm had a model size of only 9.7 M, with 4.38×10⁶ parameters and a computational volume of 8.4 GFLOPs. Precision, recall, and [email protected] increased by 4.2%, 2.2%, and 2.5%, respectively.

Our improved algorithm provided an efficient and accurate solution for underwater robot target detection technology.

This patent article emphasizes the importance of sustainable materials in energy storage research. These ionic polymer blends show potential as eco-friendly alternatives for practical energy storage systems. We synthesized three distinct compositions of ionic polymer blends (25/15/60, 30/15/55, and 35/15/60) using solution casting techniques. Our focus was on PVDF, PVP, and PVSA—ionic polymers that offer environmental advantages over traditional materials.

To address these issues, we propose the use of ionic polymer membranes. By reducing the demand for synthetic plastics, these membranes contribute to pollution reduction. Moreover, they offer economic and environmental benefits. Ionic polymers can also help limit carbon dioxide emissions.

In our study, we created a blend of polyvinylidene fluoride (PVDF), polyvinylpyrrolidone (PVP), and polyvinyl sulfonic acid (PVSA). This blend was used to fabricate an ionic membrane through the solvent casting method. The resulting membrane acts as a successful dielectric material, sandwiched between copper electrodes. We conducted extensive characterization, including microstructure analysis, crystal structure examination, identification of functional groups, and thermal behavior assessment.

Our findings demonstrate the potential of ionic polymer membranes for capacitor applications. By varying the composition of PVDF/PVP/PVSA, we explored different dielectric properties. Notably, the ionic polymer electrolyte can serve as an effective dielectric material, with copper electrodes providing a practical application.

Our investigation included electrical current density measurements using cyclic voltammetry, elemental composition analysis, morphological studies, and material composition assessments.

Development of ionic membranes by using generic polymers polyvinyl alcohol (PVA) and Polystyrene sulfonic acid (PSSA), which have been implemented as sensing devices. Our health is directly influenced by wearable sensors therefore, we need to enhance its quality. Wearable sensors depend on the sensitivity of the material as well as the phase transition of ions.

The phase transition of ion/charge is analyzed through percolation theory, which is based on the probability model. Percolation displays what are referred to as crucial phenomena.

This typically indicates that the model contains a natural parameter at which the system's behaviour substantially alters. The PVA/PSSA (S2) membrane of 25% composition reveals the minimum percolation threshold of 0.20. Sample S1 (10%), S2 (25%), and S3 (45%) possess phase transitions of 0.525,0.225, and 0.0343, respectively. The robustness of the phase transitions and the sensitivity were examined using the percolation theory. Out of five samples, the three samples have more feasible coordinates for building wearable sensors, which are sample S1 (10%), S2 (25%), and S3 (45%).

The analysis of the pure PSSA polymer research is expected to be done in the upcoming months in order for a patent.

Gas Hydrates, or Clathrate Hydrates, have been the subject of increasing scientific and industrial attention due to their potential as an alternative energy source, their role in climate change, and their association with geohazards. The growth of new indigenous gas supply sources could impart a significant positive ripple effect on a country's economy, ecological balance, and energy landscape. This burgeoning interest has led to a surge in research and development, resulting in numerous patents related to the extraction, processing, and utilization of gas Hydrates.

This review paper aims to provide an up-to-date, comprehensive overview of the properties, formation, detection, production, importance, challenges, and patent landscape of Gas hydrates. The integration of patented technologies into the field underscores the importance of intellectual property in shaping the future of energy, environment, and economic development.

Patented technologies in this field are contributing to making this resource more accessible and commercially viable. Moreover, the development of gas hydrates as an energy source could act as a safeguard for manufacturing jobs that are sensitive to gas prices, with proprietary technologies enhancing the efficiency and sustainability of the production process.

On the environmental front, an uptick in the consumption of natural gas, known for its cleaner combustion, could herald positive change. Patented innovations in clean and efficient extraction and utilization methods for Gas Hydrates are instrumental in reducing the environmental impact. From the standpoint of energy security, a larger domestic slice of the energy pie, complemented by an extensive array of gas supply alternatives, could equip the nation to better navigate the unpredictable terrain of future energy scenarios.

The strategic patenting of key technologies in the exploration, production, and application of Gas Hydrates ensures competitive advantage and fosters innovation, driving forward the energy industry's evolution.

Sleep Apnea (SA) is a sleep-related breathing disorder diagnosed in clinical laboratories. The gold standard is Polysomnography (PSG), a multi-parameter evaluation of a sleep monitoring system that records the biological signals during overnight sleep. Apart from PSG recording, apnea events are recorded by various other bio-signals called Electrocardiogram (ECG), Electroencephalogram (EEG), Oxygen Saturation level (SpO2), etc. Further evaluation of the recorded bio-signals is tedious and time-consuming as experts perform it manually. Aiming to overcome the disadvantage without compromising accuracy, scientists focus on developing robust measurements of SA by using Machine Learning (ML) and Deep Learning (DL) models.

This study aimed to analyze the recent research findings in the field of sleep apnea classification and various machine learning and deep learning methods implemented in detecting SA. This study revealed the best-performing technique considering different types of bio-signals used for analysis and the respective ML or DL models used for automatic detection.

The studies and patents included in this review underwent a precise screening process using PRISMA guidelines. The literature study is comprised of three different analysis tools to showcase the review process and provide evidence for the research findings obtained in the respective publications. The publications considered were limited to the last decade.

This review delivers the key finding that ECG signals-based detection of sleep apnea using deep learning model-based deep neural network classifiers will provide more accurate and robust classification, which will pave the way for possible future research directions.

Heavy-duty hydrostatic bearings of the original parallel friction pair are prone to hydrostatic loss problems during operation.

The aim is to solve the problem of lubrication failure, the original hydrostatic oil pad was designed as a micro-inclined plane, and the dynamic pressure caused by the micro-wedge of the oil pad at a higher speed was used to compensate for the static pressure loss of the bearing.

This study aims at a new type of q1-205 micro bevel double rectangular cavity hydrostatic bearing.

The mathematical model of oil film theoretical analysis of hydrostatic bearing with the micro-inclined surface was established, including the flow equation of a double rectangular cavity with a micro-inclined surface and static and dynamic bearing capacity equation.

In this patent paper, the mechanical properties of oil film with tilt height between 0 and 0.1mm were simulated with variable viscosity, and the distribution law of pressure field under different working conditions was obtained. Through the experimental study, the pressure data of parallel flat pads and tilting pads under different working conditions are measured and compared with the simulation data.

The pressure loss value of the Δh ≈ 70 μm oil pad designed in this paper is relatively small under extreme working conditions. The overall loss rate is about 10% ~ 20%, and the dynamic pressure compensation rate is about 16%. The dynamic pressure generated by the slight inclination Angle can well compensate for the static pressure loss.