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The limitations of commonly used materials such as steel in withstanding high temperatures led to exploring alternative alloys. For instance, Inconel 825 is a nickel-based alloy known for its exceptional corrosion resistance. Thus, the Inconel 825 is used in various applications, including aerospace, marine propulsion, and missiles. Though it has many advantages, machining this alloy at high temperatures could be challenging due to its inadequate heat conductivity, increased strain hardening propensity, and extreme dynamic shear strength. The resultant hardened chips generated during high-speed machining exhibit elevated temperatures, leading to tool wear and surface damage, extending into the subsurface. This work investigated the influence of varying process settings on the machinability of Inconel 825 metal, using both uncoated and coated tools.

Aluminum alloys are employed in agricultural equipment, aerospace sectors, medical instruments, machinery, automobiles, etc. due to their physical and mechanical characteristics. The geometrical shape and size of the parts are modified in turning operation by using a single-point cutting tool. A356 aluminum alloy is widely used in various engineering sectors, hence there is a necessity to produce A-356 components with quality. The inappropriate cutting parameters used in turning operation entail high production costs and reduce tool life. Box–Behnken design (BBD) based on response surface methodology (RSM) was used to design the experiments such that the experiment trials were conducted by varying cutting parameters like N-spindle speed (rpm), f-feed rate (mm/rev), and d-depth of cut (mm). The multi-objective responses, such as surface roughness (SR) and metal removal rate (MRR) were analyzed with the desirability method.

Abstract TOC

Materials play a key role in our day to day life and have shaped the industrial revolution to a great extent. Right selection of material for meeting a particular objective is the key to success in today’s world where the cost as well as sustainability of any equipment or a system have assumed greater significance than ever before. In automotive industry, materials have a definitive role as far as the mobility and safety is concerned. Materials that can absorb the required energy or impact can be manufactured through different manufacturing as well as metallurgical processes which involves appropriate heat treatment and bringing correct chemical compositions etc. However, they can also be formed by simpler methods such as combining certain materials together in the form of layered combinations to form light weight composites.

Fuel tank is considered as safety component in the vehicle, and it has to be tested to meet the safety requirements as per AIS 095. Earlier, fuel tanks were manufactured by using Hot dipped cold rolled steel material and the weld zones are applied with Anti-corrosive coating. Few fuel tanks were reported with Corrosion problems. The root cause analysis was carried out considering the raw material, manufacturing process, transpiration, storage and usage. As an improvement, the new fuel tank is designed to eliminate the limitations of the existing fuel tank. 3D modeling was done to check space and mounting requirement in the layout and used for volume calculations. FE analysis was performed to check structural stability. Emphasis given on Interchange-ability to cater the new fuel tanks in place of old as spares requirement. The fuel tank has developed with Alumina steel material.

Abstract This article describes the research work taken to compare the effect of air blast and surface-buried mine blast loading on an armored fighting vehicle (AFV) escape hatch, using the coupled Eulerian-Lagrangian (CEL) technique. Two types of escape hatch were considered for the study, namely, the flat plate version and double-side curved-plate version. To evaluate the research methodology used in this investigation, initially, a published experimental work on a circular plate subjected to air blast was chosen and a benchmark simulation was carried out using the CEL technique to establish the simulation procedure. Then the established procedure was utilized for further analysis. It was observed that the variation in the deformation between the published literature and the simulation work was well within the acceptable engineering limits.

Abstract To assist in initializing the conceptual design of hypersonic aircraft, we outline a new, systematic framework based on historical aircraft data and primarily composed of design data and regression models. It is a rapid, low-fidelity analysis to provide a starting point for the conceptual design process by (1) assessing the performance capabilities of four types of high-speed aircraft, (2) providing initial estimates for weights and geometry with uncertainty, and (3) exploring how changes in these affect performance within design spaces. Using this framework, an initial set of reasonable aircraft configurations is obtained based on speed, altitude, and payload requirements, which can serve to accelerate the design process and avoid unforeseen problems later in the design cycle. An example is provided to demonstrate the application of the framework to launch the conceptual design of a new hypersonic aircraft with a given set of mission requirements.

Abstract Letter from the Guest Editors

Engineering Events staff at SAE International in Warrendale, Pennsylvania, have extended the call for abstracts through September 21 for the organization’s AeroTech aerospace and defense technology conference, which will take place at the Fort Worth Convention Center in Fort Worth, Texas, March 14-16, 2023. Visit the AeroTech call for abstracts page for more information and to get started.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

Abstract Fighter pilots must study models of aircraft dynamics before learning complex maneuvers and tactics. Similarly, autonomous fighter aircraft applications may benefit from a model-based learning approach. Instead of using a preexisting physics model of a given aircraft, a machine learning system can learn a predictive model of the aircraft physics from training data. Furthermore, it can model interactions between multiple friendly aircraft, enemy aircraft, and the environment. Such a system can also learn to represent state variables that are not directly observable, as well as dynamics that are not hard coded. Existing model-based methods use a deep neural network that takes observable state information and agent actions as input and provides predictions of future observations as output. The proposed method builds upon this approach by adding a residual feedforward skip connection from some of the inputs to all of the outputs of the deep neural network.

The gear lubricants covered by this standard exceed American Petroleum Institute (API) Service Classification API GL-5 and are intended for hypoid-type, automotive gear units, operating under conditions of high-speed/shock load and low-speed/high-torque. These lubricants may be appropriate for other gear applications where the position of the shafts relative to each other and the type of gear flank contact involve a large percentage of sliding contact. Such applications typically require extreme pressure (EP) additives to prevent the adhesion and subsequent tearing away of material from the loaded gear flanks. These lubricants are not appropriate for the lubrication of worm gears. Appendix A is a mandatory part of this standard. The information contained in Appendix A is intended for the demonstration of compliance with the requirements of this standard and for listing on the Qualified Products List (QPL) administered by the Lubricant Review Institute (LRI).

A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations.

In the early days, there were significant limitations to the build size of laser powder bed fusion (L-PBF) additive manufacturing (AM) machines. However, machine builders have addressed that drawback by introducing larger L-PBF machines with expansive build volumes. As these machines grow, their size capability approaches that of directed energy deposition (DED) machines. Concurrently, DED machines have gained additional axes of motion which enable increasingly complex part geometries—resulting in near-overlap in capabilities at the large end of the L-PBF build size. Additionally, competing technologies, such as binder jet AM and metal material extrusion, have also increased in capability, albeit with different starting points. As a result, the lines of demarcation between different processes are becoming blurred.

SAE International is inviting global participation in its AeroTech® aerospace and defense technology conference and exhibition, which is for the first time co-located with ASM International’s AeroMat, at the Pasadena Convention Center in Pasadena, California, March 15 through 17, 2022.

The gear lubricants covered by this standard exceed American Petroleum Institute (API) Service Classification API GL-5 and are intended for hypoid-type, automotive gear units, operating under conditions of high-speed/shock load and low-speed/high-torque. These lubricants may be appropriate for other gear applications where the position of the shafts relative to each other and the type of gear flank contact involve a large percentage of sliding contact. Such applications typically require extreme pressure (EP) additives to prevent the adhesion and subsequent tearing away of material from the loaded gear flanks. These lubricants are not appropriate for the lubrication of worm gears. Appendix A is a mandatory part of this standard. The information contained in Appendix A is intended for the demonstration of compliance with the requirements of this standard and for listing on the Qualified Products List (QPL) administered by the Lubricant Review Institute (LRI).

Abstract Autonomy has the potential to make the most radical impact by significantly reducing the number of soldiers in harm’s way and changing the military paradigm. Benefits of autonomy to improve the Army’s mission capabilities and the rapid evolution of military systems exerts pressure to develop these systems quickly. Since the associated technological development is highly fast paced and stochastic, approaches that develop systems for stochastic future scenarios are required. In this article we present a vision for the autonomous high-mobility military systems for that future. We discuss the ramifications of autonomy in five areas: (1) fleet organization, (2) physical attributes of high-mobility military systems, (3) individual behaviors of autonomous assets, (4) interactions between humans and autonomous systems, and (5) operation and teaming strategies. We present the future vision, implications, requirements, and technological challenges for each of the five areas.

Game-changing opportunities abound for the application of vehicle health management (VHM) across multiple transportation-related sectors, but key unresolved issues continue to impede progress. VHM technology is based upon the broader field of advanced analytics. Much of traditional analytics efforts to date have been largely descriptive in nature and offer somewhat limited value for large-scale enterprises. Analytics technology becomes increasingly valuable when it offers predictive results or, even better, prescriptive results, which can be used to identify specific courses of action. It is this focus on action which takes analytics to a higher level of impact, and which imbues it with the potential to materially impact the success of the enterprise. Artificial intelligence (AI), specifically machine learning technology, shows future promise in the VHM space, but it is not currently adequate by itself for high-accuracy analytics.