Finite Element Analysis (FEA) is a powerful and well recognized tool used in the analysis of heat transfer problems. However, FEA can only analyze solid bodies and, by necessity thermal analysis with FEA is limited to conductive heat transfer. The other two types of heat transfer: convection and radiation must by approximated by boundary conditions. Modeling all three mechanisms of heat transfer without arbitrary assumption requires a combined use of FEA and Computational Fluid Dynamics (CFD).
SAE J3061 sets out a recommended cybersecurity engineering process framework for organizations developing cyber physical systems. One of the recommendations of this framework is to carry out a threat analysis and risk assessment early in the product development. A threat analysis identifies and models the relevant threats against assets, and a risk assessment classifies the impact and likelihood associated with each threat. The approach enables the prioritization of risks and appropriate risk treatment measures to be determined in subsequent development phases.
The skills and knowledge gained in this workshop will enable students to carry out regulatory responsibilities related to the administration of the Aircraft Certification and Continued Operational Safety. This course content provides the Civil Aviation Safety Engineers (Systems - Electrical) with the knowledge and skills to conduct oversight of aviation safety, aircraft certification and Continued Operational Safety.
Finite Element Analysis (FEA) has been used by engineers as a design tool in new product development since the early 1990's. Until recently, most FEA applications have been limited to static analysis due to the cost and complexity of advanced types of analyses. Progress in the commercial FEA software and in computing hardware has now made it practical to use advanced types as an everyday design tool of design engineers. In addition, competitive pressures and quality requirements demand a more in-depth understanding of product behavior under real life loading conditions.
We are currently in the age of developing Autonomous Vehicles (AV). Never before in history, the environment has been as conducive as today for these developments to come together to deliver a mass produced autonomous car for use by general public on the roads. Several enhancements in hardware, software, standards and even business models are paving the way for rapid development of AVs, bringing them closer to production reality. Safety is an indispensable consideration when it comes to transportation products, and ground vehicle development is no different. We have several established standards. When it comes to Autonomous Vehicle development, an important consideration is ISO 26262 for, Automotive Functional Safety. Going from generic frameworks such as Failure Mode and Effects Analyses (FMEA) and Hazard and operability study (HAZOP) to Functional Safety, Safety of Intended Functionality, and Automotive Safety Integrity Levels specific is a natural progression.
Rapidly enhancing engineering techniques to manufacture components in quick turnaround time have gained importance in recent time. Manufacturing strategies like Additive Manufacturing (AM) are a key enabler for achieving them. Unlike traditional manufacturing techniques such as injection molding, casting etc., AM unites advanced materials, machines, and software which will be critical for Industry 4.0. Successful application of AM involves a specific combination and understanding of these three key elements. In this paper the AM approach used is Fused Deposition Modelling (FDM). Since material costs contribute to 60% of the overall FDM costs, it becomes a necessity to optimize the material consumption of the produced parts. This paper reports case studies of 3D printed parts used in an Automobile plant’s production aids, which utilize computational methods(CAE), topology optimization and FDM constrains (build directions) to manufacture the part in the most optimal way.
Research and/or Engineering Questing/Objectives: Safety of the occupant in passenger cars is one of the regulatory requirements in many developed countries. This includes upper interior head impact load case of the unbelted occupant during crash (FMVSS 201U) as one of them. During a crash event the occupant head can collide with the interior parts of the vehicle, such as a headliner, pillar trim and other subsequent components in the loading direction. Injury on the head is quantified in terms of the Head Injury Criterion of a crash test dummy (HIC(d)) value which should be less than 1000 per standard. Several ways can be adopted to reduce the HIC(d) value. These include a change in the design of ribs in the safety plastic components, headliner profile change, use of countermeasure foam between headliner and the exterior sheet metal parts, or a combination of any of these to absorb the energy of impact.
Research and/or Engineering Questions/Objective Plastic automotive fuel tanks made up of blow molded, multi-layered, high-density polyethylene (HDPE) material can take complex shapes with varying thickness. Accidental drop of fuel tank from a height during handling can lead to development of cracks. Damage can also occur due to an impact during a crash. This can be catastrophic due to flammability of the fuel. The objective of this work is to characterize and develop a failure model for the fuel tank material to simulate damage and enhance predictive capability of CAE for chassis and safety load cases. Methodology Different aspects were considered to develop a characterization and modelling strategy for the HDPE fuel tank. Material properties can be influenced by factors such as, service temperature, rate of deformation, state of stress etc.
RESEARCH OBJECTIVE Accelerated artificial weathering performance has been always observed as critical and most important factor for durability prediction of colour and resin for a coating system. Photo oxidation of resin is the phenomenon behind coating’s ageing. Though accelerated weathering tests protocols are widely used in industry, they are very costly and still very time consuming. One automotive grade accelerated testing can go as long as 8 months duration. METHODOLOGY (maximum 150 words) Photo oxidation value (POV) is proportionate to the degradation of the resin material used in coating. During the accelerated weathering POV is measured for the coating at stipulated interval during initial phase and trend is plotted for deterioration verses weathering test duration. POV can be analysed with the help of FTIR analysis to observe bond absorption energy and bond separation energy in the resin system. This trend can be extrapolated to predict the weathering performance of coating.
Automotive safety is the primary concern in the current world. In order to develop safe and crashworthy vehicles, phenomena behind the energy absorption characteristics of every automotive component must be known. Steering wheel is one of the key players which could cause severe injuries to the driver if sufficient safety measures are not considered. This research focuses on the crash performance of commercial vehicle steering as per head form and body block test prescribed in ECE R12. Detailed FE (Finite Element) model of the steering wheel including armature, horn pad was developed using nonlinear material properties. The model was first validated using the test results. Comparisons between experimental results and finite element analysis results were conducted and correlated using load versus displacement profiles over the duration of impact. A good relationship between test and FE results was found which allows for investigation into the energy analysis of the steering components.
Objective: In ground vehicle industry, strain life approach is commonly used for predicting fatigue life. This approach requires use of fatigue material properties such as fatigue strength coefficient (σf'), fatigue strength exponent (b), fatigue ductility coefficient (εf'), fatigue ductility exponent (c), cyclic strength coefficient (K′) and cyclic strain hardening exponent (n′). These properties are obtained from stable hysteresis loop of constant amplitude strain-controlled uniaxial fatigue tests. Usually fatigue material properties represent 50th percentile experimental data and doesn't account possible material variation in the fatigue life calculation. However, for robust design of vehicle components, variation in material properties need to be taken into account. In this paper, methodology to develop 5th percentile (B5), 10th percentile (B10) and 20th percentile (B20) fatigue material properties are discussed.
These days backhoe loader have become main part of construction equipment vehicles. The main function of backhoe is to dig ditches to lay pipes and underground cable, set up foundations for buildings and create drainage systems. During these operations, many failures are observed in backhoe loader structure/parts. With the help of Accelerated structural durability testing, life of backhoe loader & its part can be estimated; through which we can understand different failure modes. The real time data was collected during various operations which includes pit digging, duck walk, ditch climbing, levelling, dozing, piling, truck loading etc. We have used software based approach to process the strain, displacement and other data collected during real time operation to create the duty cycle. The same duty cycle was simulated in the lab condition using servo hydraulic actuators.
As we transition towards Internet of Things (IoT) - humans are connected to each other & outside world through the smartphone. Customers tend to use smartphones for varied purposes ranging from communication to entertainment. However, the concern of distraction exists due to poor visibility & accessibility of the phone's screen in driving condition. One of the repercussion of being connected to smartphone particularly in driving condition includes higher number of road accidents due to distraction. This paper explains one of the key initiatives taken by Maruti Suzuki India Limited to address the same.
Digital Twins for Prognostic Profiling Authors: Sreeram Mohan*, Painuri Thukaram**, Panduranga Rao*** Objective / Question: Ability to have least failures in products on the field with minimum effort from the manufacturers is a major area of focus driven by Industry 4.0 initiatives. Amidst traditional methods of performing system / subsystem level tests often does not enable the complete coverage of a machine health performance predictions. This paper highlights a workable workflow that could be used as a template while considering system design especially employing Digital Twins that help in mimicking real-life scenarios early in the design cycle to increase product’s reliability as well as tend to near zero defects. Methodology: With currently available disruptive technologies , systems are integrated multi-domain 'mechatronics' systems operating in closed-loop/close-interaction.
The SRM is gaining much interest for EVs due to its rare-earth-free characteristic and excellent performance. SRM possess several advantages such as low cost, high efficiency, high power density, fault-tolerant and it can produce extended constant power region, and this makes SRM as viable alternative over conventional PM drives. Objective: The objective of this paper is to establish proof of theoretical concepts related to SRM. The key to achieve an effective SRM modeling is to use a methodology that allow the nonlinearity of its magnetic characteristics to be represented while maximizing the simulation speed. This paper represents how magnetization data obtained from FEA in the form of look up tables is most appropriate way to represent SRM model. In this paper, performance analysis of SRM is done with the help of Open loop and Closed loop MATLAB simulations. These dynamic simulations of SRM will assist in understanding behavior of SRM in various loading and speed conditions.
Title Development of a Graphical User Interface (GUI) Based Tool for Vehicle Dynamics Evaluation Authors Mr. Shubham Kedia, Dr. Divyanshu Joshi, Dr. Muthiah Saravanan Mahindra Research Valley, Mahindra & Mahindra, Chennai Objective Objective metrics for evaluation of major vehicle dynamics performance attributes i.e. ride, handling and steering are required to compare, validate and optimize dynamic behavior of vehicles. Some of these objective metrics are recommended and defined by ISO and SAE, which involve data processing, statistical analysis and complex mathematical operations on acquired data, through simulations or experimental testing. Due to the complexity of operations and volume of data, evaluation is often time consuming and tedious. Process automation using existing tools such as MS Excel, nCode, Siemens LMS, etc. includes several limitations and challenges, which make it cumbersome to implement.
The present work involves Machine Learning (ML) based Multi-objective Multidisciplinary Design Optimization (MMDO) for lightweighting the automotive structures. The challenge in deployment of MMDO algorithms in solving real-world automotive structural design problems is the enormous time involved in solving full vehicle finite element models that involve large number of design variables and multiple performance constraints pertaining to vehicle dynamics, durability, crash and NVH domains. With the availability of powerful workstations and using the advanced Computer Aided Engineering (CAE) tools, it has become possible to generate huge sets of simulation data pertaining to multiple domains.
In the current commercial vehicles market, ride-comfort and handling are crucial parameters for the customer and end user. There are various aspects which determine the vehicle behaviour. One of aspects is the structural rigidity of the vehicle, which has its own effect on vehicle dynamics. To meet the required stiffness of the main structural component of the vehicle i.e. chassis frame, FEA analysis has to be done in current methodology. The number of iterations have to be done to build an appropriate model with low weight, which can meet the design requirements. At first, conceptual design mock-up unit is to be developed then FEA (CAE) analysis to be done on it. If any design criteria are not met, then this cycle repeats again until it fulfils the required stiffness. Today, the direct stiffness procedure is the basic principle of almost every FEA software package.