Browse Topic: Statistical analysis
During the early phase of vehicle development, one of the key design attributes to consider is the trunk. Trunk is the pillar that is responsible for user’s accommodate their baggage and make into customer needs in engineer metrics. Therefore, it is one of the key requirements to be considered during the vehicle design. Certain internal vehicle trunk characteristics such as the trunk height and length are engineer metrics that influence the occupants’ perception for trunk. One specific characteristic influencing satisfaction is the rear opening width lower for notch back segment, which is the subject of this paper. The objective of this project is to analyze the relationship between the rear opening width lower with the occupant’s satisfaction under real world driving conditions, based on research, statistical data analysis and dynamic clinics
Wire Electrical Discharge Machining (WEDM) is a widely used manufacturing method that is employed to shape complex geometries in conductive materials such as cupronickel, which is highly regarded for its resistance to corrosion and ability to conduct heat. The aspiration of this investigation is to improve the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for cupronickel material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study analyzes the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness, material removal rate. Experimental trials are performed to collect data on these parameters and their corresponding machining characteristics. The TOPSIS optimization method is utilized to determine the most favourable parameter settings by evaluating each parameter combination against the ideal and
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Additive Manufacturing (AM), specifically Fusion Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of complex structures using a wide range of materials. The objective of this study is to enhance the FDM process for Thermoplastic Polyurethane (TPU) material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study examines the influence of FDM parameters, such as layer height, nozzle temperature, and infill density, on important characteristics of the printing process, such as tensile strength, flexibility, and surface finish. The collection of experimental data is achieved by conducting systematic FDM printing trials that cover a variety of parameter combinations. The TOPSIS optimization method is utilized to determine the optimal parameter settings by evaluating each parameter combination against the ideal and anti-ideal solutions. This method determines the optimal parameter
ABSTRACT Predictive analysis of vehicle electrical systems is achievable by combining condition based maintenance (CBM) techniques and testing for statistical significance (TSS). When paired together, these two fundamentally sound sciences quantify the state of health (SOH) for batteries, alternators, starters, and electrical systems. The use of a communication protocol such as SAE J1939 allows for scheduling maintenance based on condition and not a traditional time schedule
ABSTRACT This paper addresses some aspects of an on-going multiyear research project of GP Technologies for US Army TARDEC. The focus of the research project has been the enhancement of the overall vehicle reliability prediction process. This paper describes briefly few selected aspects of the new integrated reliability prediction approach. The integrated approach uses both computational mechanics predictions and experimental test databases for assessing vehicle system reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level. To solve efficiently and accurately the challenges coming from large-size
ABSTRACT In this context, a damage model is a mathematical algorithm that is used to predict if and when in a given loading history a structure will fail by ductile fracture. Increments in a damage parameter are related to strain increments and state of stress. The damage model would operate as part of a numerical simulation, or separately on an output file. A scale effect in ductile fracture is widely recognized from test data, where a large structure tends to fail at lower strain than a smaller structure that is geometrically similar and of the same material. Most damage models are not scale sensitive, and when they are calibrated to data from small laboratory specimens, they will tend to over-predict the performance (i.e., energy absorbing capability) of a larger structure. Another factor is scatter in test results even when specimens are made with care to be as identical as possible. Both of these factors are addressed in the proposed statistics-based damage model. Scale effects
ABSTRACT Implementing Prognostic and Predictive Maintenance (PPMx) for the U.S. Army’s ground vehicle fleet requires the design and integration of on-platform predictive analytics. To support the design process, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) and Applied Research Laboratory (ARL) Penn State researchers are developing a systematic approach that uses reliability modeling in a guiding role. The key steps of the process are building the initial reliability model from available data (e.g., system diagrams and physical layouts), augmenting with information on observed states and failure modes via subject matter experts, and then conducting trades on additional sensors and algorithms to determine a suitable predictive analytics capability. In this paper we provide an example of this process as applied to an Army ground vehicle, first focusing on a simplified sub-problem to demonstrate the technique, then providing statistics on the large scale process. Citation: M
ABSTRACT In this paper, we discuss a neuroimaging experiment that employed a mission-based scenario (MBS) design, a new approach for designing experiments in simulated environments for human subjects [1]. This approach aims to enhance the realism of the Soldier-task-environment interaction by eliminating many of the tightly-scripted elements of a typical laboratory experiment; however, the absence of these elements introduces several challenges for both the experimental design and statistical analysis of the experimental data. Here, we describe an MBS experiment using a simulated, closed-hatch crewstation environment. For each experimental session, two Soldiers participated as a Commander-Driver team to perform six simulated low-threat security patrol missions. We discuss challenges faced while designing and implementing the experiment before addressing analysis approaches appropriate for this type of experimentation. We conclude by highlighting three example transition pathways from
The healthcare industry is evolving and facing two major challenges. First, the rise of chronic diseases. By 2050, chronic diseases such as cardiovascular diseases, cancer, diabetes, and respiratory illnesses could account for 86 percent of the 90 million deaths each year, according to the World Health Organization (WHO) in its 2023 World Health Statistics report. This increase is due to factors such as an aging population, lifestyle changes, and risk factors like high blood pressure, high blood sugar, and air pollution. Consequently, this creates a second challenge: added strain on healthcare resources. To address this, WHO recommends tackling the root causes of chronic diseases, promoting healthier behaviors, and ensuring universal access to healthcare resources
This document defines the steps and documentation required to perform a digital fiber optic link loss budget. This document does not specify how to design a digital fiber optic link. This document does not specify the parameters and data to use in a digital fiber optic link loss budget
This document defines a quantified means of specifying a digital fiber optic link loss budget: Between end users and system integrators Between system integrators and subsystem suppliers Between subsystem suppliers and component vendors The standard specifies methods and the margin required for categories of links
A structural load estimation methodology was developed for RLV-TD HEX-01 hypersonic experimental mission, the maiden winged body technology demonstrator vehicle of ISRO. Primarily the method evaluates time history of station loads considering effects of vehicle dynamics and structural flexibility. Station loads of critical structures are determined by superposition of quasi-static aerodynamic loads, dynamic inertia loads, control surface loads and propulsion loads based on actual physics of the system, improving upon statistical load combination approaches. The technique characterizes atmospheric regime of flight from vehicle loads perspective and ensures adequate structural margin considering atmospheric variations and system level perturbations. Features to estimate change in loads due to wind variability and atmospheric turbulence are incorporated into the load estimation methodology. Augmentation in loads due to structural flexibility is assessed along the trajectory using vehicle
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