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The Selective Laser Melting (SLM) process is employed in high-precision layer-by-layer Additive Manufacturing (AM) on powder bed and aims to fabricate high-quality structural components. To gain a comprehensive understanding of the process and its optimization, both modeling and simulation in conjunction with extensive experimental studies along with laser calibration studies have been attempted. Multiscale and multi-physics-based simulations have the potential to bring out a new level of insight into the complex interaction of laser melting, solidification, and defect formation in the SLM parts. SLM process encompasses various physical phenomena during the formation of metal parts, starting with laser beam incidence and heat generation, heat transfer, melt/fluid flow, phase transition, and microstructure solidification. To effectively model this Multiphysics problem, it is imperative to consider different scales and compatible boundary conditions in the simulations.

Selective Laser Melting (SLM) has gained widespread usage in aviation, aerospace, and die manufacturing due to its exceptional capacity for producing intricate metal components of highly complex geometries. Nevertheless, the instability inherent in the SLM process frequently results in irregularities in the quality of the fabricated components. As a result, this hinders the continuous progress and wider acceptance of SLM technology. Addressing these challenges, in-process quality control strategies during SLM operations have emerged as effective remedies for mitigating the quality inconsistencies found in the final components. This study focuses on utilizing optical emission spectroscopy and IR thermography to continuously monitor and analyze the SLM process within the powder bed, with the aim of strengthening process control and minimizing defects.

Abstract: The present study discusses about the effect of installation torque on the surface and subsurface deformations for thin walled 7075 aluminum alloy used in Aerospace applications. A FE model was constructed to predict the effect of torque induced stresses on thin walled geometry followed with an experimentation. A detailed surface analysis was performed on 7075 aluminum in terms of superficial discontinuities, residual stresses, and grain deformations. The localized strain hardening resulting from increased dislocation density and its effect on surface microhardness was further studied using EBSD and micro indentation. The predicted surface level plastic strain of .25% was further validated with grain deformations measured using optical and scanning electron microscopy.

Abstract: Hydraulic systems in aircrafts largely comprise of metallic components with high strength to weight ratios which comprise of 2024 Aluminum and Titanium Ti-6AL-4V. The selection of material is based on low and high pressure applications respectively. For aircraft fluid conveyance products, hydraulic conduits are fabricated by axisymmetric turning to support flow conditions. The hydraulic conduits further carries groves within for placement of elastomeric sealing components. This article presents a systematic study carried out on common loads experienced by fluid carrying conduits and the failure modes induced. The critical failure locations on fluid carrying conduits of 2024-T351 Aluminum was identified, and the Scanning Electron Microscope (SEM) analysis was carried out to identify the characteristic footprints of failure surfaces and crack initiation. Through this analysis, a load to failure mode correlation is established.

Aerospace structural components grapple with the pressing issue of high-cycle fatigue-induced micro-crack initiation, especially in high-performance alloys like Titanium and super alloys. These materials find critical use in aero-engine components, facing a challenging combination of thermo-mechanical loads and vibrations that lead to gradual dislocations and plastic strain accumulation around stress-concentrated areas. The consequential vibration or overload instances can trigger minor cracks from these plastic zones, often expanding unpredictably before detection during subsequent inspections, posing substantial risks. Effectively addressing this challenge demands the capability to anticipate the consequences of operational life and aging on these components. It necessitates assessing the likelihood of crack initiation due to observed in-flight vibration or overload events.

Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.

Landing of spacecraft on Lunar or Martian surfaces is the last and critical step in inter planetary space missions. The atmosphere on earth is thick enough to slow down the craft but Moon or Mars does not provide a similar atmosphere. Moreover, other factors such as lunar dust, availability of precise onboard navigational aids etc would impact decision making. Soft landing meaning controlling the velocity of the craft from over 6000km/h to zero. If the craft’s velocity is not controlled, it might crash. Various onboard sensors and onboard computing power play a critical role in estimating and hence controlling the velocity, in the absence of GPS-like navigational aids. In this paper, an attempt is made using visual onboard sensor to estimate the velocity of the object. The precise estimation of an object's velocity is a vital component in the trajectory planning of space vehicles, particularly those designed for descent onto lunar or Martian terrains, such as orbiters or landers.

Tire forces and moments play an important role in vehicle dynamics and safety. X-by-wire chassis components including active suspension, electronic powered steering, by-wire braking, etc can take the tire forces as inputs to improve vehicle’s dynamic performance. In order to measure the accurate dynamic wheel load, most of the researches focused on the kinematic parameters such as body longitudinal and lateral acceleration, load transfer and etc. In this paper, the authors focus on the suspension system, avoiding the dependence on accurate mass and aerodynamics model of the whole vehicle. The geometry of the suspension is equated by the spatial parallel mechanism model (RSSR model), which improves the calculation speed while ensuring the accuracy. A suspension force observer is created, which contains parameters including spring damper compression length, push rod force, knuckle accelerations, etc., combing the kinematic and dynamic characteristic of the vehicle.

The soft and rough terrain on the planet's surface significantly affects the ride and safety of rovers during high-speed driving, which imposes high requirements for the control of the suspension system of planet rovers. To ensure good ride comfort of the planet rover during operation in the low-gravity environment of the planet's surface, this study develops an active suspension control strategy for torsion spring and torsional damper suspension systems for planet rovers. Firstly, an equivalent dynamic model of the suspension system is derived. Based on fractal principles, a road model of planetary surface is established. Then, a fuzzy-PID based control strategy aimed at improving ride comfort for the planet rover suspension is established and validated on both flat and rough terrains.

When the aircraft towing operations are carried out in narrow areas such as the hangars or parking aprons, it has a high safety risk for aircraft that the wingtips may collide with the surrounding aircraft or the airport facility. A real-time trajectory prediction method for the towbarless aircraft taxiing system (TLATS) is proposed to evaluate the collision risk based on image recognition. The Yolov7 module is utilized to detect objects and extract the corresponding features. By obtaining information about the configuration of the airplane wing and obstacles in a narrow region, a Long Short-Term Memory (LSTM) encoder-decoder model is utilized to predict future motion trends. In addition, a video dataset containing the motions of various airplane wings in real traction scenarios is constructed for training and testing.

In the racing world, speed is everything, and the Formula Student cars are no different. As one of the key means to improve the speed of the car, lightweight plays an important role in the racing world. The weight reduction of unsprung metal parts can not only improve the driving speed, but also effectively optimize the dynamic of the car, so the lightweight design of unsprung parts has attracted much attention. In the traditional Formula Student racing car, the hub and spoke are two independent parts, they are fixed by four hub bolts or a central locking nut, the material of these fasteners is usually steel, so it brings a lot of weight burden. In order to achieve unsprung lightweight, a new type of wheel part design of Formula Student racing car is proposed in this paper. The hub and spoke are designed as integrated aluminum alloy parts, effectively eliminating the mass of hub bolts or central locking nuts.

This paper presents a numerical methodology to generate lookup tables that provide d- and q-axis stator current references for the control of electric motors. The main novelty with respect to other literature references is the introduction of the iron power losses in the equivalent-circuit electric motor model implemented in the optimization routine. The lookup tables generation algorithm discretizes the motor operating domain and, given proper constraints on maximum stator current and magnetic flux, solves a numerical optimization problem for each possible operating point to determine the combination of d- and q- axis stator currents that minimizes the imposed objective function while generating the desired torque. To demonstrate the versatility of the proposed approach, two different variants of this numerical interpretation of the motor control problem are proposed: Maximum Torque Per Ampere and Minimum Electromagnetic Power Loss.

Although there have been significant advancements in vision-based localization techniques over recent years, there are still problems that need to be addressed. One of these problems is localization in dynamically illuminated environments, like one might find when a small unmanned aerial system (sUAS) equipped with a lighting payload attempts to autonomously navigate inside a dark, damaged structure. When visual odometry (VO) methods are implemented in a dynamically illuminated environment, the accuracy of the state estimation degrades because the shadows are improperly identified as features and these shadow-features move in a different manner than static objects in the environment. As a result, sUAS pose estimates often accumulate errors without bound. This work will examine the merits and demerits inherent in conventional or prevailing sUAS self-localization techniques in dark environments.

Previously given Paper 09ATC-0232 delivered at the SAE Aerotech conference in Seattle in 2009 reports on the E6000 machine installing slug rivets with the EMR. Paper 2015-01-2491given at the SAE conference in Seattle in 2015 reports on index head rivets being installed with screw driven squeeze process. This paper reports on the screw driven squeeze process installing unheaded slug rivet which is a more complex process. We also report on improvements to the fixture automation.

Large-scale aerostructures are commonly constructed using multiple layers of stacked material which are fastened together using mechanical methods. Ensuring the interface gaps between these materials are kept within engineering tolerances is of utmost importance to the structural integrity of the aircraft over its service life. Manual, right angle feeler gauges are the traditional method for measurement of interface gaps, but this method is tedious and mechanic dependent. A portable hand tool utilizing low-coherence interferometry has been developed to address these issues. The tool uses a right-angle probe tip which is inserted into a previously drilled hole and driven through the depth of the material. A line scan of data is collected and analyzed for the presence of interface gaps. To measure the consistency of the gap around the circumference of the hole, the tool is rotated by the operator and additional scans are collected.

Friction stir welding (FSW) is a method of welding that creates a weld trail by pressing a non-consumable rotating tool with a profiled pin on the adjacent surfaces while moving transversely along the welding direction. The method was initially used with metals and alloys, but more recently, thermoplastic polymers have also been included in its application. Investigations on FSW of thermoplastic polymers made of nylon and High-density polythene (HDPE) are presented here. Weld characteristics that are like those of the base materials are attempted to be achieved. Because of their unique nature and thermal conductivity, thermoplastics FSW differs from that of metals. The use of thermoplastic materials with conventional FSW procedures presents numerous difficulties and is currently ineffective. On the weld characteristics of nylon and HDPE, statistical methods were utilized to study the impact of temperature, rotational speed, and transverse speed.

Robotic arms are widely known to fall short in achieving the tolerances required when it comes to the metal machining industry, especially for the aerospace sector. Broadly speaking, two of the main reasons for that are a lack of stiffness and a lack of accuracy. Robotic arm manufacturers have responded to the lack of stiffness challenge by producing bigger robots, capable of holding high payloads (e.g., Fanuc M-2000iA/2300) or symmetric robots (e.g., ABB IRB6660). Previous research proved that depending on the application and the material being machined, lack of stiffness will still be an issue, even for structurally bigger robotic arms, due to their serial nature. The accuracy issue has been addressed to a certain extent by using secondary encoders on the robotic arm joints. The encoder enhanced robotic arm solutions tend to be expensive and prior knowledge proves that there are still limitations when it comes to achieved accuracy.

The aerospace industry's unceasing quest for lightweight materials with exceptional mechanical properties has led to groundbreaking advancements in material technology. Historically, aluminum alloys and their composites have held the throne in aerospace applications owing to their remarkable strength-to-weight ratio. However, recent developments have catapulted magnesium and its alloys into the spotlight. Magnesium possesses two-thirds of aluminum's density, making it a tantalizing option for applications with regard to weight-sensitive aerospace components. To further enhance magnesium's mechanical properties, researchers have delved into the realm of metal matrix composites (MMCs), using reinforcements such as Alumina, Silicon carbide, Boron carbide and Titanium carbide.

In the aerospace industry, large aircrafts employ composite materials for making complex structures which not only reduces weight and cost but also reduces the number of joints. Irrespective of that joining of structures cannot be avoided and for that mechanical fasteners such as rivets and bolts are employed along with adhesive bonding. Further, in recent years natural fibers have been studied extensively for their numerous advantages and have already been made into several automotive applications. Keeping these current trends in mind an attempt is made to investigate the joining behavior of natural fiber composites experimentally. So in this study, the ultimate failure load, bearing strength and the dominating failure mode of jute-hemp fabric-reinforced polymeric composites joined using single and double-bolted configurations are studied.

A case study of an application of Shape optimization techniques in the design of a mass simulator has been presented. A simple mass Simulator is to be designed as a replacement for a Telescope Baffle Mass for testing purposes. The simulator is made of simple plate structures like flat plates and cylindrical plates joined together. The overall mass, location of center of gravity and first few modes of the simulator need to be close to the Telescope Baffle, it is replacing. This ensures that the Simulator is a good replacement for the Telescope Baffle both in statics and dynamics performance. Shape Optimization techniques using approximate direct linearization method of MSC/Nastran software have been used to fine-tune the baseline Simulator design to achieve target properties of mass, cg, frequencies, etc.