Automobile Components Research Articles

Investigation of the statistical data on the technical condition of brake equipment components of passenger carriages in operation

In the article, a study was conducted on improving the efficiency of braking systems in railway transport. Based on the information obtained regarding the maintenance of passenger cars, an analysis of brake equipment component failures was performed. Key trends and factors determining brake equipment component failures were identified from the collected statistical data for the reporting period. The main brake equipment components with the highest number of failures were highlighted. It was established that a significant portion of transport incidents due to brake failures in passenger cars occurs during operation, negatively impacting traffic safety. The collected data on the failures of electro-pneumatic distributors in passenger cars were systematized using the specialized software package "STATISTIKA". The research established that seasonal factors influence the total number of failures. A diagnostic system for the braking equipment of passenger trains was proposed to ensure traffic safety. The diagnostic system allows for the construction and analysis of mathematical models that describe the operational state of the braking equipment of passenger rolling stock as a whole. The diagnostic system enhances the reliability of brake operation, monitors air pressure in the brake line, ensures the inter-repair periods of passenger car components, and reduces the time required to detect failures both en route and during train stops. Recommendations for diagnosing the braking system of passenger trains were developed for continuous inspection and timely identification of failures. Their application will enable the prediction of the lifespan of pneumatic and electro-pneumatic system components in passenger cars, ensure the inter-repair operational periods of cars based on their current technical condition, and enhance traffic safety in railway transport.

A Novel Light-Weight Machine Learning Classifier for Intrusion Detection in Controller Area Network in Smart Cars

The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles.

A Multi‐Trip Vehicle Routing Problem With Release Dates and Interrelated Periods

ABSTRACTThis article proposes a new multi‐trip vehicle routing problem variant, originally motivated by a practical application, namely a car components distribution to repair centers (warehouses). The problem is named the multi‐trip vehicle routing with release dates and interrelated periods (MTVRP‐RDIP). The routes may start at different pre‐defined periods, called departure periods, and may have different durations. Thus, two routes that start at different periods may be active at the same period, leading to the so‐called interrelated periods. Moreover, a route may only start at a period if a vehicle is available, and the availability of the vehicle depends not only on the existing vehicles but also on the active routes at that time period. The clients are classified according to their importance and represent warehouses that require car components that are made available throughout the time horizon, thus leading to the consideration of release dates. Delays in satisfying client orders and not serving orders in the time horizon result in penalty costs that must be minimized. The objective to minimize also includes routing costs and vehicle utilization costs. Two metrics are defined to compute an order's delay, each leading to a different mixed integer linear model. Computational results showed that one model clearly outperforms the other and even this one is only suited to address the smallest instances. A matheuristic based on a rolling‐horizon process that iteratively solves the better‐performing model with fewer periods was designed to tackle the largest instances. The matheuristic can provide feasible solutions for all test instances in an efficient manner that are, on average, better than the ones provided by the model in most of the compared key performance indicators.

Oновлення парку рухомого складу українських залізниць з орієнтацією на інтеграцію в європейські транспортні мережі

The strategic aim of further development of the Ukrainian railways is rolling stock and infrastructure renewal to meet the demands of the Ukrainian economy, including a rapid integration into the European transport service. The paper presents the results of recent studies conducted at the Department of Statistical Dynamics and Multidimensional Mechanical System Dynamics, the Institute of Technical Mechanics of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, with the aim to solve important problems in the development of the Ukrainian railway transport. The studies were aimed at developing scientifically substantiated decisions on Ukrainian rolling stock renewal oriented at integration into the European railway networks. To do this, a cycle of theoretical and experimental studies was conducted at the department. Consideration was given to the problem of wheel–rail contact pair improvement by identifying a wheel profile that would offer an acceptable car ride performance and acceptable conditions of car–rail interaction both on the Ukrainian and the European railways and reduce vehicle and rail wear. The car ride performance of an articulated passenger train was simulated mathematically for its motion at different speeds along a track of arbitrary alignment and profile, and the effect of different car and track parameters on the ride performance was estimated. Energy-absorbing devices were developed to increase the passive safety of a speedy locomotive-hauled passenger train and a multiple-unit train in emergency collisions with obstacles according to the Ukrainian Standard DSTU EN 15227. Service loads on the load-bearing components of swap-body freight cars were estimated, and recommendations were developed on fasteners that would provide safe transportation of various freights in swap-body cars. Based on the results of the studies, a number of design solutions were proposed for Ukrainian railway vehicle components. Their implementation will offer a sizeable economic benefit, increase the train speed and safety, improve the vehicle–track interaction, and facilitate the formation of an up-to-date home railway complex and its integration into the European railway networks.

Investigating Electromagnetic Interference Induced Risks for Autonomous Driving

Abstract. The results of immunity tests on components of self-driving automobiles to radiated high power electromagnetic (HPEM) pulses are presented in this work. It is of particular interest to investigate such automobiles' resilience towards deliberate attacks with electromagnetic interference (EMI). Different types of HPEM capabilities at the Bundeswehr Research Institute for Protective Technologies and CBRN Protection (WIS) in Munster are applied for this investigation. Two types of automotive components have been tested, a two-axis acceleration sensor and an electronic power steering unit. Statistics of errors from the devices under test (DUTs), as well as a characterization of the applied pulses are presented. A correlation can be drawn between the pulse repetition rate or the amplitude of the applied pulses and the severity of the effect on the DUTs.

A BIONIC-INSPIRED DRAGONFLY ALGORITHM FOR PARAMETRIC OPTIMIZATION AND DAMAGE INVESTIGATION DURING MACHINING OF MODIFIED POLYMER NANOCOMPOSITE

Polymer nanocomposite is commonly used to develop structural components of space, aircraft, biomedical, sensor, automobile, and battery sector applications. It remarkably substitutes the heavyweight metallic and nonmetallic engineering materials. The machining principles of polymer nanocomposites are intensely different and complex from traditional metals and alloys. The nonhomogeneity, abrasive, and anisotropic nature differs its machining aspect from conventional metallic materials. This investigation aims to execute the CNC drilling of modified nanocomposite using Graphene–carbon (G-C) @ epoxy matrix. The process constraints, namely, cutting speed (S), feed (F), and wt.% of graphene oxide (GO) vary up to three levels and are designed according to the response surface methodology (RSM) array. The nonlinear model is created to predict surface roughness (Ra) and delamination (Fd) on regression analysis. It has been found that the average error for Ra is 0.94% and for Fd it is 3.27%, which is acceptable in model predictions. The metaheuristics-based evolutionary Dragonfly algorithm (DA) evaluated the optimal parametric condition. The optimal setting prediction for the DA is observed as cutting speed (S)-37.68[Formula: see text]m/min, feed (F)-80[Formula: see text]mm/min, and wt.% of graphene oxide (GO)-1%. This algorithm demonstrates a higher application potential than the previous efforts in controlling Ra and Fd values. Both the drilling response values are found to be minimized when the cutting speed increases and the feed decreases. The best fitness value for the DA is 1.626 for surface roughness and 5.086 for delamination. This study agreed with the prediction model’s outcomes and the process parameters’ optimal condition. The defects generated during the sample drilling, such as fiber pull out, uncut/burr, and fiber breakage, were examined using FE-SEM analysis. The optimal findings of the DA module significantly controlled the damages during machining.

Frequency–Time Domain Analysis Based on Electrochemical Noise of Dual-Phase (DP) and Ferrite–Bainite (FB) Steels in Chloride Solutions for Automotive Applications

The automotive industry uses high-strength (HS), low-alloy (HSLA) steels and advanced high-strength steels (AHSSs) to manufacture front and rear rails and safety posts, as well as the car body, suspension, and chassis components of cars. These steels can be exposed to corrosive environments, such as in countries where de-icing salts are used. This research aims to characterize the corrosion behavior of AHSSs based on electrochemical noise (EN) [dual-phase (DP) and ferrite–bainite (FB)]. At room temperature, the steels were immersed in NaCl, CaCl2, and MgCl2 solutions and were studied by frequency–time domain analysis using wavelet decomposition, Hilbert–Huang analysis, and recurrence plots (RPs) related to the corrosion process and noise impedance (Zn). Optical microscopy (OM) was used to observe the microstructure of the tested samples. The results generally indicated that the main corrosion process is related to uniform corrosion. The corrosion behavior of AHSSs exposed to a NaCl solution could be related to the morphology of the phase constituents that are exposed to solutions with chlorides. The Zn results showed that DP780 presented a higher corrosion resistance with 918 Ω·cm2; meanwhile, FB780 presented 409 Ω·cm2 when exposed to NaCl. Also, the corrosion mechanism of materials begins with a localized corrosion process spreading to all the surfaces, generating a uniform corrosion process after some exposition time.

Characterization of the natural fibers extracted from the aninga's stem and development of a unidirectional polymeric sheet.

A unidirectional sheet was made with oriented fibers in an epoxy matrix. Natural fibers were extracted from the stem of Montrichardia linifera (Arruda) Schott., traditionally known as aninga, characterized and used to produce a unidirectional polymeric sheet. FTIR, XRD, SEM, TG, and DTG analyses were performed to characterize these cellulose fibers. Peaks observed at 1024cm- 1, 1600cm- 1, and 3328cm- 1 revealed the stretching vibration of the O-H bond, the stretching of the carbonyl in hemicellulose, and the vibration of aromatic rings, respectively. XRD analysis demonstrated a crystallinity index of 62.21%. Morphological analysis revealed the microstructural quality of the fiber surface, with grooves for mechanical anchoring, as well as its interior, which is composed of microfibrils. EDS analysis confirmed the presence of the main elements composing natural fibers, with carbon being the major component (70%). The thermal stability of aninga fibers was up to 450 ºC for the degradation of 50% of their initial mass. The mechanical properties of untreated aninga fibers showed a tensile strength of 332MPa and an elastic modulus of 13.000MPa. The unidirectional sheet had a tensile strength of 4.5MPa and an elasticity modulus of 332.9MPa. These outcomes ensured that aninga fiber is considered high-performance (> 200MPa) and can be used for internal automobile components.

Enhancing energy capacity of the car’s hood door under excitation frequency via functionally graded triply periodic minimal surface material: Application of machine learning and mathematical simulation

This study investigates the nonlinear dynamic response and vibration characteristics of a car’s hood door, focusing on its energy capacity under various excitation frequencies. The hood door is modeled using a functionally graded triply periodic minimal surface (FG-TPMS) material, which offers superior mechanical properties and lightweight design. The analysis is conducted using a higher-order shear deformation theory (HSDT), which accounts for shear deformations more accurately than classical theories. The incorporation of von Karman nonlinear terms captures the geometric nonlinearity due to large deformations, providing a realistic simulation of the hood door’s behavior under dynamic loads. To solve the complex equations of motion derived from the HSDT and von Karman terms, a fourth-order Runge–Kutta method is employed. This numerical method ensures accurate time integration and stability of the solution. The dynamic response is further analyzed to determine the energy absorption capacity of the hood door material, crucial for safety and durability in automotive applications. Validation of the numerical model is performed using previous published articles and a hybrid machine learning algorithm, which combines data-driven approaches with traditional physics-based models. This hybrid validation enhances the accuracy and reliability of the predicted responses, ensuring that the proposed model can be effectively used in the design and optimization of car components. The results demonstrate the potential of FG-TPMS materials in automotive applications, offering improved energy absorption and vibration control, which are essential for enhancing vehicle safety and performance.

The improved viscoelastic properties of long‐length carbon nanotubes reinforced polyamide‐6 composites

AbstractThe semi‐crystallinity, thermoplasticity, high toughness, and light weight of polyamide‐6 (PA6) when reinforced with carbon nanotubes (CNTs) provide outstanding physical properties to the nanocomposites by combining the physical properties of both components. The processing difficulties arising due to the high toughness and abrasion resistance of PA6 and the agglomeration of in‐house synthesized long‐length CNTs can be tackled by melt‐mixing the components inside a twin‐screw extruder with a back‐flow channel. The 0.1–7 parts per hundred ratios (phr) of CNTs reinforced PA6 composites were characterized for their viscoelastic properties through oscillatory rheometry and dynamic mechanical analysis (DMA) at fixed operating conditions. The outcomes showed continuous shiftings in storage and loss modulus values with increasing reinforcements along with a viscoelastic transition at 3 phr CNTs reinforcements observed in DMA. A 19°C rise in glass transition temperature (Tg) was observed in DMA with 0.1 phr CNTs reinforcement, which showed further improvements with increasing CNTs content. The interactions among PA6 and CNTs were further confirmed by X‐ray diffraction (XRD) and Raman spectroscopy curves. These nanocomposites promise mechanical applications in the equipments of automobiles, aerospace, defense, biomedical, bio‐sensors, etc.Highlights Viscoelastic properties study for CNTs/PA6 composites Uniform intermixing of CNTs within PA6 via extrusion with back‐flow channel Detailed analysis of rheometry and DMA of the composites CNTs‐PA6 interactions estimated from rising intensity peaks in Raman spectra and X‐ray diffraction (XRD) Potential candidates for components in automobiles, aircraft, biomedicals, and sports industry

The impact of active components wear on psychoacoustic parameters of noise in a car cabin

The automotive industry is undergoing an electromobility revolution. Changing a car's drive unit influences the vibroacoustics of a car's interior. Drive-train noise at low car speeds and in the low-frequency range is no longer dominant. There is no masking effect due to the noise of the combustion engine, and a passenger paradoxically may perceive the noise of a car as less pleasant. Moreover, existing vibroacoustic requirements have to some extent taken into account the change in the noise of components with the usage time, but there are still areas that require further research. This paper focuses on the study of electric adjustable seats, power windows and power exterior mirrors in different wear states, to determine the significant psychoacoustics changes of the selected car component's noises. The study is part of extensive research that aims to revise the current vibroacoustic requirements for new components and to determine how they could be tested and evaluated.

A study on the improvement of sound insulation performance of aluminum chassis

In recent years, the automobile industry has rapidly shifted towards electric vehicles, with an increasing number of vehicles using aluminum instead of steel for a significant portion or the entire or part of the vehicle body. Aluminum body panels reduce sound insulation performance compared to steel panels because they depend on surface density. This situation causes uncomfortable sound environment for vehicle passengers. To address the sound insulation issues associated with lightweight panels, it is necessary to research how to optimize the thickness and weight of interior and exterior car components in order to enhance sound insulation performance. In this study, the acoustic characteristics were initially confirmed by measuring indoor noise while driving a vehicle constructed from two different materials: steel and aluminum, under the same conditions as other parts. In addition, to select a sound absorbing material suitable for the characteristics of the vehicle body material, steel and aluminum panels were installed between the reverberation chamber and the anechoic chamber, respectively, and sound transmission loss was evaluated by combining the various thickness and weight. Based on these results, we decided on a proposed specification (aluminum panel with weight/thickness added) above the basic specification (steel panel with existing sound insulation parts).

An experimental analysis of mechanical properties for a dissimilar pattern structure in the 3-D printing of a PLA5F filament using the Taguchi method

Abstract Many automobile components and machine parts can be fabricated using the Fused Deposition Modeling (FDM) process with materials such as Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PET-G), and polymeric composite materials (e.g., PLA with carbon fiber, PLA with glass fiber). In this study, a new polymeric composite material was fabricated using Polylactic Acid and natural flax fiber was analysed for tensile stress, elongation, and impact load resistance using Taguchi Analysis. This analysis optimized the printing parameters, including layer thickness (0.15, 0.25, 0.35 mm), nozzle movement speed (80, 100, 120 mm s−1), filling structure (Lines - a, Triangular - b, and Octet - c), and occupancy rate (20%, 40%, 60%). The American Society for Testing and Materials (ASTM) standards for tensile strength (ASTM D638) and impact strength (ASTM D256) were used for evaluation. As a result, layer thickness was found to be the most effective variable for improving tensile characteristics, compared to extruder temperature, occupancy rate, or filling structure pattern. Mechanical properties including a layer thickness of 0.25 mm, an occupancy ratio of 20% for the bottom of the 2nd layer and 40% for the top of the 4th layer, triangular and octet filling structures, a nozzle speed of 100 mm s−1, and an extruder temperature of 200 °C are considered the most appropriate parameters for producing automotive parts in Three Dimensional (3D) Printing. Due to its tensile properties and impact strength resistance, these settings can be utilized in potential application in a wide variety of machine parts and vehicle components.

Evaluation of mechanical properties of natural fiber based polymer composite

Natural fiber based polymer composites are eco-friendly alternatives to synthetic materials, with greater mechanical properties, biodegradability, availability, ease of access, and affordability. Jute fiber is widely recognized as one of the most important and beneficial natural fibers due to its strength, durability, and biodegradability. In this study, the jute composite is designed and fabricated using a 5-layer jute and epoxy resin, utilizing the manual hand lay-up technique. The combination of 52.5 % jute and 47.5 % of epoxy resin and harder is found optimized to achieve the goals of improving the tensile strength and flexural strength, reducing the cost of epoxy resin, and promoting eco-friendliness and sustainability. Tensile testing was performed on a universal testing machine, while flexural testing was done with a three-point bending test. Experimentally, the composites reinforced with jute and epoxy resin were capable of achieving the required levels of tensile strength (42.91 MPa) and bending strength (69.30 MPa). To validate and visualize specimens, numerical analysis was performed on the ABAQUS simulation software. The numerical simulation utilized ASTM D3039 and ASTM D7264 as the specified requirements for tensile and flexural behavior. For validation, these tensile and flexural test results were then numerically analyzed and compared to the experimental data. Finally, composite design, fabrication, and optimization can improve mechanical properties, reduce composite weight, lower resin cost, and increase sustainability. The proposed design and composition can be implemented to achieve lightweight properties in various applications, such as car components, door handle sheets, bicycle seat backs, and luggage covers.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

An advanced dual-layered framework for sustainable supply chain performance

Purpose This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP). Design/methodology/approach Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators. Findings The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders. Research limitations/implications Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries. Originality/value The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

Bending Analysis of Multiwall Carbon Nanotube Reinforced Functionally Graded Composite Cylindrical Shell

The curved structural components of aircraft, automobiles, and marine vessels, such as cylindrical panels, are primarily exposed to loading, which can result in bending failure, particularly in lightweight structures. The bending analysis of cylindrical panels made of functionally graded multiwall carbon nanotube (FG-MWCNT) epoxy composites is subjective in this study. In the first section, FG-MWCNT composite cylindrical panels are simulated in the ANSYS software. There are three methods for distributing uniaxially aligned reinforcements. The material properties of the carbon nanotubes uniformly distributed and functionally graded over the thickness of the shell structure are estimated using an extended rule of mixture micromechanical model that incorporates certain useful characteristics. To demonstrate the effectiveness and applicability of the proposed finite element approach, deflection data are presented. The analysis of the shell structure's bending includes an investigation of the impact of CNT volume fraction, boundary condition, lengthto-thickness ratio, and other geometrical factors

Experimental analysis on the mechanical properties of Al6061 based hybrid composite reinforced with silicon carbide, bagasse fly ash and aloe vera ash

The demand for low-density automobile components for lightweight automobiles that have less energy consumption has initiated the production of chassis, wheels, bumpers, brakes, and steering using composite materials. This research aimed to fabricate an Al6061-based hybrid composite for automobile components reinforced with 10 wt% silicon carbide and varying aloe vera and bagasse ashes using the stir-casting method. The effects of the composition of reinforcements on the microstructure, hardness and compressive strength of the hybrid composites were assessed. It was found that an increase in bagasse ash and aloe vera ash contents improved the compressive strength. The hybrid composite with 10 wt% bagasse ash and 11 wt% aloe vera ash demonstrated maximum compressive strength of 376.3 MPa. The highest hardness of 91.6 HV was achieved with the hybrid composite having 10 wt% bagasse ash and 11 wt% aloe vera ash. The hardness value of the hybrid composite was increased by 16.27% when compared to a single reinforced Al6061/SiC composite. It was concluded that adding bagasse and aloe vera ashes greatly increased the hardness and compressive strength of Al6061/SiC composite materials. The findings of this research would be useful for automobile component manufacturers.

WITHDRAWN: An Experimental and Numerical Approach to Evaluate Mechanical Properties of Jute Fiber-Based Polymer Composites

One of the best natural fibers is jute fiber because of its availability, ease of access, and affordability. It has some highly intriguing qualities, including biodegradability and high strength. In this study, the jute composite is designed and fabricated using a 5-layer jute and epoxy resin, utilizing the manual hand lay-up technique. The combination of 52.5% jute and 47.5% of epoxy resin and harder is found optimized to achieve the goals of improving the tensile strength and flexural strength, reducing the weight of the composite, reducing the cost of epoxy resin, and promoting eco-friendliness and sustainability. Tensile testing was performed on a universal machine, while flexural testing was done with a three-point bending test. Experimentally, the composites reinforced with jute and epoxy resin were capable of achieving the required levels of tensile strength (42.91 MPa) and bending strength (69.30 MPa). To validate and visualize specimens, numerical analysis was performed on the ABAQUS simulation software. The numerical simulation utilized ASTM D3039 and ASTM D7264 as the specified requirements for tensile and flexural behavior. For validation, the results of these tensile and flexural tests were then numerically analyzed and compared to the experimental data. Finally, the composite design, fabrication, and optimization can improve mechanical properties, reduce composite weight, lower resin cost, and increase sustainability. The proposed design and composition will be implemented to achieve lightweight properties in various applications such as car components, door handle sheets, bicycle seat backs, and luggage covers.

Microstructural and wear properties of iron slag reinforced aluminum alloy (LM30) based composite prepared through a stir casting method

Aluminum alloys are widely used in various industries due to their as low density, high strength-to-weight ratio, and good corrosion resistance. However, their wear resistance is often inadequate for certain applications. Utilization of industrial waste materials, such as iron slag, as reinforcement in aluminum alloy matrix composites offers a sustainable approach to material development and waste management. The utilization of industrial waste materials for aluminum alloy matrix composite fabrication offers a waste utilization to material development. The loading of this reinforcement varied from 0 to 15 wt.% and different particle size range (220-140, 140-70, and 70-0 µm). A microscopic analysis indicated that the iron slag particles are spread uniformly inside the metallic matrix. There is also a reduction in the size of primary silicon, as well as morphological changes (acicular to globular shape). The wear behavior was calculated using a pin-on-disk wear set up in accordance with ASTM G99 standard. The composites were employed to dry sliding wear test under various operating conditions such as applied pressure (0.2–1.4 MPa), and sliding distance (0–3000 m). The 15F composite outperformed all other composite samples in terms of wear rate under all working conditions. When compared to the base alloy, it demonstrated a remarkable 67% drop in steady state wear rate. The enhancements in wear performance for the 15F composite were attributed to the effects of Fe slag reinforcement. The inclusion of iron slag particles induced strong interfacial bonding between matrix and reinforcement particles improving the durability of the mechanical mixed layer developed during relative motion. Importantly, the wear rate parameters of the 15F composite were similar to those of the brake drum material used in commercial applications. This emphasizes the composite suitability for usage in a variety of automobile components.