Design, Fabrication, and Optimization of a Printed Ag Nanoparticle-Based Flexible Capacitive Sensor for Automotive IVI Bezel Display Applications.

Since printed capacitive sensors provide better sensing performance, they can be used in automotive bezel applications. It is necessary to fabricate such sensors and apply an optimization approach for choosing the optimal sensor pattern. In the present work, an effort was made to formulate interdigitated pattern-printed Silver (Ag) electrode flexible sensors and adopt the Taguchi Grey Relational (TGR)-based optimization approach to enhance the flexible sensor's panel for enhanced automobile infotainment applications. The optimization technique was performed to derive better design considerations and analyze the influence of the sensor's parameters on change in capacitance when touched and production cost. The fabricated flexible printed sensors can provide better sensing properties. A design pattern which integrates an overlap of 15 mm, an electrode line width of 0.8 mm, and an electrode gap 0.8 mm can produce a higher change in capacitance and achieve a lower weight. The overlap has a greater influence on sensor performance owing to its optimization of spatial interpolation.

Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator-Fuzzy Logic Algorithms and Sensor Fusion.

A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator-fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road's roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot's path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.

Predicting Mechanical Properties of Polymer Materials Using Rate-Dependent Material Models: Finite Element Analysis of Bespoke Upper Limb Orthoses.

Three-dimensional printing-especially with fused deposition modeling (FDM)-is widely used in the medical field as it enables customization. FDM is versatile owing to the availability of various materials, but selecting the appropriate material for a certain application can be challenging. Understanding materials' mechanical behaviors, particularly those of polymeric materials, is vital to determining their suitability for a given application. Physical testing with universal testing machines is the most used method for determining the mechanical behaviors of polymers. This method is resource-intensive and requires cylinders for compression testing and unique dumbbell-shaped specimens for tensile testing. Thus, a specialized fixture must be designed to conduct mechanical testing for the customized orthosis, which is costly and time-consuming. Finite element (FE) analysis using an appropriate material model must be performed to identify the mechanical behaviors of a customized shape (e.g., an orthosis). This study analyzed three material models, namely the Bergström-Boyce (BB), three-network (TN), and three-network viscoplastic (TNV) models, to determine the mechanical behaviors of polymer materials for personalized upper limb orthoses and examined three polymer materials: PLA, ABS, and PETG. The models were first calibrated for each material using experimental data. Once the models were calibrated and found to fit the data appropriately, they were employed to examine the customized orthosis's mechanical behaviors through FE analysis. This approach is innovative in that it predicts the mechanical characteristics of a personalized orthosis by combining theoretical and experimental investigations.

Evaluation of zirconia ceramics fabricated through DLP 3d printing process for dental applications.

Zirconia ceramics are versatile materials with remarkable properties such as a high thermal resistance, high fracture strength, and low thermal conductivity. They are chemically inert and highly wear- and corrosion-resistant, making them ideal for a wide range of applications in the aerospace, automotive, and biomedical fields. In dentistry, zirconia ceramics are used for veneers, crowns, bridges, and implants because of their biocompatibility. Despite the various benefits of zirconia ceramics, they are difficult to process because of their high hardness and brittleness. Additive manufacturing (AM) has proven to be a viable alternative to conventional fabrication processes, particularly for the processing of difficult-to-cut materials. AM of ceramics has gained significant attention in recent years because of its flexibility and ability to produce customized geometries rapidly and economically. In this study, the digital light processing (DLP) technique was employed to 3D print yttria-stabilized zirconia. The fabricated zirconia was evaluated and characterized for use in dental applications. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) were performed on the green body to assess the decomposition of the additives in the slurry and determine the debinding temperatures. The as-built parts were subjected to debinding and sintering to obtain fully dense zirconia parts. The parts tended to shrink after sintering; therefore, the shrinkage ratios were evaluated and found to be 1.2817, 1.2900, and 1.3388 in the x-, y-, and z-directions, respectively. The average density after sintering was 6.031 g/cc. The flexural strength determined using four-point bending tests was 451.876 MPa, and the tensile and compressive strengths were 143 MPa and 298.4 MPa, respectively.

Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.

The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19's future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which includes Long Short-Term Memory (LSTM) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group's membership over time. To increase the SIRVD model's accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R 2, root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized root mean square error (NRMSE). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.

A comprehensive review of navigation systems for visually impaired individuals.

Background: This review explores the evolutionary trajectory of navigation assistance tools tailored for the visually impaired, spanning from traditional aids like white canes to contemporary electronic devices. It underlines their pivotal role in fostering safe mobility for visually impaired individuals. Objectives: The primary aim is to categorize and assess the plethora of navigation assistance solutions available. Emphasis is placed on technological advancements, particularly in electronic systems employing sensors, AI, and feedback mechanisms. Furthermore, the review underscores the emerging influence of smartphone-based solutions and navigation satellite systems in augmenting independence and quality of life for the visually impaired. Methods: Navigation assistance solutions are segmented into four key categories: Visual Imagery Systems, Non-Visual Data Systems, Map-Based Solutions, and 3D Sound Systems. The integration of diverse sensors like Ultrasonic Sensors and LiDAR for obstacle detection and real-time feedback is scrutinized. Additionally, the fusion of smartphone technology with sensors to deliver location-based assistance is explored. The review also evaluates the functionality, efficacy, and cost-efficiency of navigation satellite systems. Results: Results indicate a significant evolution in navigation aids, with modern electronic systems proving highly effective in aiding obstacle detection and safe navigation. The convenience and portability of smartphone-based solutions are underscored, along with the potential of navigation satellite systems to enhance navigation assistance. Conclusions: In conclusion, the review advocates for continued innovation and technological integration in navigation tools to empower visually impaired individuals with increased independence and safe access to their surroundings. It accentuates the imperative of ongoing efforts to enhance the quality of life for those with visual impairments through futuristic technological solutions.

An Insight into the Characteristics of 3D Printed Polymer Materials for Orthoses Applications: Experimental Study.

Knee orthoses assist patients with impaired gait through the amendment of knee abnormalities, restoration of mobility, alleviation of pain, shielding, and immobilization. The inevitable issues with laborious traditional plaster molding procedures for orthoses can be resolved with 3D printing. However, a number of challenges have limited the adoption of 3D printing, the most significant of which is the proper material selection for orthoses. This is so because the material used to make an orthosis affects its strength, adaptability, longevity, weight, moisture response, etc. This study intends to examine the mechanical, physical, and dimensional characteristics of three-dimensional (3D) printing materials (PLA, ABS, PETG, TPU, and PP). The aim of this investigation is to gain knowledge about these materials' potential for usage as knee orthosis materials. Tensile testing, Olympus microscope imaging, water absorption studies, and coordinate measuring machine-based dimension analysis are used to characterize the various 3D printing materials. Based on the investigation, PLA outperforms all other materials in terms of yield strength (25.98 MPa), tensile strength (30.89 MPa), and shrinkage (0.46%). PP is the least water absorbent (0.15%) and most flexible (407.99%); however, it is the most difficult to fabricate using 3D printing. When producing knee orthoses with 3D printing, PLA can be used for the orthosis frame and other structural elements, PLA or ABS for moving parts like hinges, PP for padding, and TPU or PP for the straps. This study provides useful information for scientists and medical professionals who are intrigued about various polymer materials for 3D printing and their effective utilization to fabricate knee orthoses.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application.

Objective: The objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. Methods: It integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. Results: The findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. Conclusions: To ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Finite Element Analysis of Upper Limb Splint Designs and Materials for 3D Printing.

Three-dimensional (3D) printed splints must be lightweight and adequately ventilated to maximize the patient's convenience while maintaining requisite strength. The ensuing loss of strength has a substantial impact on the transformation of a solid splint model into a perforated or porous model. Thus, two methods for making perforations-standard approach and topological optimization-are investigated in this study. The objective of this research is to ascertain the impact of different perforation shapes and their distribution as well as topology optimization on the customized splint model. The solid splint models made of various materials have been transformed into porous designs to evaluate their strength by utilizing Finite Element (FE) simulation. This study will have a substantial effect on the designing concept for medical devices as well as other industries such as automobiles and aerospace. The novelty of the research refers to creating the perforations as well as applying topology optimization and 3D printing in practice. According to the comparison of the various materials, PLA had the least amount of deformation and the highest safety factor for all loading directions. Additionally, it was shown that all perforation shapes behave similarly, implying that the perforation shape's effect is not notably pronounced. However, square perforations seemed to perform the best out of all the perforation shape types. It was also obvious that the topology-optimized hand splint outperformed that with square perforations. The topology-optimized hand splint weighs 26% less than the solid splint, whereas the square-perforated hand splint weighs roughly 12% less. Nevertheless, the user must choose which strategy (standard perforations or topology optimization) to employ based on the available tools and prerequisites.

Design, Analysis, and 3D Printing of a Patient-Specific Polyetheretherketone Implant for the Reconstruction of Zygomatic Deformities.

The reconstruction of craniomaxillofacial deformities, especially zygomatic bone repair, can be exigent due to the complex anatomical structure and the sensitivity of the crucial organs involved. The need to reconstruct the zygomatic bone in the most precise way is of crucial importance for enhancing the patient outcomes and health care-related quality of life (HRQL). Autogenous bone grafts, despite being the gold standard, do not match bone forms, have limited donor sites and bone volume, and can induce substantial surgical site morbidity, which may lead to adverse outcomes. The goal of this study is to provide an integrated approach that includes various processes, from patient scanning to implant manufacture, for the restoration of zygomatic bone abnormalities utilizing Polyetheretherketone (PEEK) material, while retaining adequate aesthetic and facial symmetry. This study takes an integrated approach, including computer-aided implant design using the mirror reconstruction technique, investigating the biomechanical behavior of the implant under loading conditions, and carrying out a fitting accuracy analysis of the PEEK implant fabricated using state-of-the-art additive manufacturing technology. The findings of the biomechanical analysis results reveal the largest stress of approximately 0.89 MPa, which is relatively low in contrast to the material's yield strength and tensile strength. A high degree of sturdiness in the implant design is provided by the maximum value of strain and deformation, which is also relatively low at roughly 2.2 × 10-4 and 14 µm. This emphasizes the implant's capability for load resistance and safety under heavy loading. The 3D-printed PEEK implant observed a maximum deviation of 0.4810 mm in the outside direction, suggesting that the aesthetic result or the fitting precision is adequate. The 3D-printed PEEK implant has the potential to supplant the zygoma bone in cases of severe zygomatic reconstructive deformities, while improving the fit, stability, and strength of the implant.

Polyether-Ether-Ketone (PEEK) and Its 3D-Printed Quantitate Assessment in Cranial Reconstruction.

Three-dimensional (3D) printing, medical imaging, and implant design have all advanced significantly in recent years, and these developments may change how modern craniomaxillofacial surgeons use patient data to create tailored treatments. Polyether-ether-ketone (PEEK) is often seen as an attractive option over metal biomaterials in medical uses, but a solid PEEK implant often leads to poor osseointegration and clinical failure. Therefore, the objective of this study is to demonstrate the quantitative assessment of a custom porous PEEK implant for cranial reconstruction and to evaluate its fitting accuracy. The research proposes an efficient process for designing, fabricating, simulating, and inspecting a customized porous PEEK implant. In this study, a CT scan is utilized in conjunction with a mirrored reconstruction technique to produce a skull implant. In order to foster cell proliferation, the implant is modified into a porous structure. The implant's strength and stability are examined using finite element analysis. Fused filament fabrication (FFF) is utilized to fabricate the porous PEEK implants, and 3D scanning is used to test its fitting accuracy. The results of the biomechanical analysis indicate that the highest stress observed was approximately 61.92 MPa, which is comparatively low when compared with the yield strength and tensile strength of the material. The implant fitting analysis demonstrates that the implant's variance from the normal skull is less than 0.4436 mm, which is rather low given the delicate anatomy of the area. The results of the study demonstrate the implant's endurance while also increasing the patient's cosmetic value.

Adaptive Mechanism for Designing a Personalized Cranial Implant and Its 3D Printing Using PEEK.

The rehabilitation of the skull's bones is a difficult process that poses a challenge to the surgical team. Due to the range of design methods and the availability of materials, the main concerns are the implant design and material selection. Mirror-image reconstruction is one of the widely used implant reconstruction techniques, but it is not a feasible option in asymmetrical regions. The ideal design approach and material should result in an implant outcome that is compact, easy to fit, resilient, and provides the perfect aesthetic and functional outcomes irrespective of the location. The design technique for the making of the personalized implant must be easy to use and independent of the defect's position on the skull. As a result, this article proposes a hybrid system that incorporates computer tomography acquisition, an adaptive design (or modeling) scheme, computational analysis, and accuracy assessment. The newly developed hybrid approach aims to obtain ideal cranial implants that are unique to each patient and defect. Polyetheretherketone (PEEK) is chosen to fabricate the implant because it is a viable alternative to titanium implants for personalized implants, and because it is simpler to use, lighter, and sturdy enough to shield the brain. The aesthetic result or the fitting accuracy is adequate, with a maximum deviation of 0.59 mm in the outside direction. The results of the biomechanical analysis demonstrate that the maximum Von Mises stress (8.15 MPa), Von Mises strain (0.002), and deformation (0.18 mm) are all extremely low, and the factor of safety is reasonably high, highlighting the implant's load resistance potential and safety under high loading. Moreover, the time it takes to develop an implant model for any cranial defect using the proposed modeling scheme is very fast, at around one hour. This study illustrates that the utilized 3D reconstruction method and PEEK material would minimize time-consuming alterations while also improving the implant's fit, stability, and strength.

Multiobjective Optimization of Heat-Treated Copper Tool Electrode on EMM Process Using Artificial Bee Colony (ABC) Algorithm.

Electrochemical micromachining (EMM) is a plausible method for manufacturing high accuracy and precision microscale components in a broad range of materials. EMM is commonly utilized to manufacture turbine blades for automobiles and aircrafts. In this present study, the EMM process was performed with a heat-treated copper tool electrode on aluminum 8011 alloy. The process parameters such as voltage, concentration of electrolyte, frequency, and duty factor were varied to analyze the effect of a heat-treated electrode on material removal rate (MRR), overcut, conicity, and circularity. It was observed that high MRR was obtained with lower overcut with an annealed electrode. The better conicity and circularity were obtained with a quenched electrode compared to other heat-treated and untreated tool electrodes. The artificial bee's colony (ABC) algorithm was used to identify the optimum parameters and, finally, the confirmation test was carried out to evaluate the error difference on the machining process. The optimum combination of input process parameters found using TOPSIS and ABC algorithm for the EMM process are voltage (14 V), electrolyte concentration (30 g/L), frequency (60 Hz), and duty cycle (33%) for the annealed tool electrode and voltage (14 V), electrolyte concentration (20 g/L), frequency (70 Hz), and duty cycle (33%) for the quenched tool electrode. It was confirmed that 95% of accurate response values were proven under the optimum parameter combination.

Precise Drilling of Holes in Alumina Ceramic (Al2O3) by Rotary Ultrasonic Drilling and its Parameter Optimization using MOGA-II.

Alumina is an advanced ceramic with applications in dental and medical sciences. Since ceramics are hard and brittle, their conventional machining is expensive, arduous, and time-consuming. As rotary ultrasonic machining is among the most adequate and proficient processing techniques for brittle materials like ceramics. Therefore, in this study, rotary ultrasonic drilling (RUD) has been utilized to drill holes on alumina ceramic (Al2O3). This study investigates the effect of key RUD process variables, namely vibration frequency, vibration amplitude, spindle speed, and feed rate on the dimensional accuracy of the drilled holes. A four-variable three-level central composite design (thirty experiments on three sample plates) is utilized to examine the comparative significance of different RUD process variables. The multi-objective genetic algorithm is employed to determine the optimal parametric conditions. The findings revealed that material removal rates depend on feed rate, while the cylindricity of the holes is mostly controlled by the speed and feed rate of the spindles. The optimal parametric combination attained for drilling quality holes is speed = 4000 rpm, feed rate = 1.5 (mm/min), amplitude = 20 (µm), and frequency = 23 (kHz). The validation tests were also conducted to confirm the quality of drilled holes at the optimized process parameters.

Multi-Response Optimization of Processing Parameters for Micro-Pockets on Alumina Bioceramic Using Rotary Ultrasonic Machining.

The machining of ceramic materials is challenging and often impossible to realize with conventional machining tools. In various manufacturing applications, rotary ultrasonic milling (RUM) shows strengths, in particular for the development of high-quality micro-features in ceramic materials. The main variables that influence the performance and price of the product are surface roughness, edge chipping (EC), and material removal rate (MRR) during the processing of ceramics. RUM has been considered in this research for the milling of micro-pockets in bioceramic alumina (Al2O3). Response surface methodology in the context of a central composite design (CCD) is being used to plan the experiments. The impacts of important RUM input parameters concerning cutting speed, feed rate, depth of cut, frequency, and amplitude have been explored on the surface roughness in terms of arithmetic mean value (Ra), the EC, and the MRR of the machined pockets. The main effect and the interaction effect of the implemented RUM parameters show that by providing a lower feed rate and cutting depth levels and elevated frequency and cutting speed, the Ra and the EC can be minimized. At greater levels of feed rate and cutting depth, higher MRR can be obtained. The influence of RUM input parameters on the surface morphology was also recorded and analyzed using scanning electron microscopic (SEM) images. The study of the energy dispersive spectroscopy (EDS) shows that there is no modification in the alumina bioceramic material. Additionally, a multi-response optimization method has been applied by employing a desirability approach with the core objectives of minimizing the EC and Ra and maximizing the MRR of the milled pockets. The obtained experimental values for Ra, EC, and MRR at an optimized parametric setting were 0.301 µm, 12.45 µm, and 0.873 mm3/min respectively with a combined desirability index value of 0.73.

Micromachining of Biolox Forte Ceramic Utilizing Combined Laser/Ultrasonic Processes.

Micromachining has gained considerable interest across a wide range of applications. It ensures the production of microfeatures such as microchannels, micropockets, etc. Typically, the manufacturing of microchannels in bioceramics is a demanding task. The ubiquitous technologies, laser beam machining (LBM) and rotary ultrasonic machining (RUM), have tremendous potential. However, again, these machining methods do have inherent problems. LBM has issues concerning thermal damage, high surface roughness, and vulnerable dimensional accuracy. Likewise, RUM is associated with high machining costs and low material-removal rates. To overcome their limits, a synthesis of LBM and RUM processes known as laser rotary ultrasonic machining (LRUM) has been conceived. The bioceramic known as biolox forte was utilized in this investigation. The approach encompasses the exploratory study of the effects of fundamental input process parameters of LBM and RUM on the surface quality, machining time, and dimensional accuracy of the manufactured microchannels. The performance of LRUM was analyzed and the mechanism of LRUM tool wear was also investigated. The results revealed that the surface roughness, depth error, and width error is decreased by 88%, 70%, and 80% respectively in the LRUM process. Moreover, the machining time of LRUM is reduced by 85%.

Investigation on Effect of Strain Rate and Heat Generation on Traverse Force in FSW of Dissimilar Aerospace Grade Aluminium Alloys.

The emergence of the aerospace sector requires efficient joining of aerospace grade aluminium alloys. For large-scale industrial practices, achievement of optimum friction stir welding (FSW) parameters is chiefly aimed at obtaining maximum strain rate in deforming material with least application of traverse force on the tool pin. Exact computation of strain rate is not possible due to complex and unexposed material flow kinematics. Estimation using micro-structural evolution serves as one of the very few methods applicable to analyze the yet unmapped interdependence of strain rate and traverse force. Therefore, the present work assessed strain rate in the stir zone using Zener Holloman parameter. The maximum and minimum strain rates of 6.95 and 0.31 s-1 were obtained for highest and least traverse force, respectively.