Engineers are taught to create designs that meet customer specifications. When creating these designs, the focus is usually on the nominal values rather than variation. Robustness refers to creating designs that are insensitive to variability in the inputs. Much of the literature on robustness is dedicated to experimental techniques, particularly Taguchi techniques, which advocate using experiments with replications to estimate variation. This course presents mathematical formulas based on derivatives to determine system variation based on input variation and knowledge of the engineering function.
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the challenges of determining the optimal balance for testing automated driving systems (ADS). Three main issues are outlined that merit immediate interest: First, determining what kind of testing an ADS needs before it is ready to go on the road; Second, the current, optimal, and realistic balance of simulation testing and real-world testing; Third, the challenges of sharing data in the industry. SAE EDGE™ Research Reports are preliminary investigations of new technologies. The three technical issues identified in this report need to be discussed in greater depth with the aims of, first, clarifying the scope of the industry-wide alignment needed, second, prioritizing the issues requiring resolution, and, third, creating a plan to generate the necessary frameworks, practices, and protocols.
The Federal Aviation Administration (FAA) and the Department of Transportations' (DOT's) National Highway Traffic Safety Administration (NHTSA) face similar challenges regarding the regulation of autonomous systems powered by artificial intelligence (AI) algorithms that replace the human factor in the decision-making process. The validation and verification (V&V) processes contribute to the implementation of the correct system requirements. The V&V process is one of the steps of a development lifecycle starting with the definition of regulatory, marketing, operational, performance, and safety requirements. They define what a product is, and they flow down into lower level requirements defining control architectures, hardware, and software. The industry is attempting to define regulatory requirements and a framework to gain safety clearance of such products.
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the challenges of achieving optimal model fidelity for developing, validating, and verifying vehicles capable of automated driving. Three main issues are outlined that merit immediate interest: First, assuring that simulation models represent their real-world counterparts, how to quantify simulation model fidelity, and how to assess system risk; Second, developing a universal sensor model interface and language for verifying, simulating, and calibrating automated driving sensor; Third, characterizing and determining the different requirements for sensor, vehicle, environment, and human driver models. SAE EDGE™ Research Reports are preliminary investigations of new technologies.
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).
The front of a car, though susceptible to the biggest impacts in terms of magnitude, has space and additional reinforcement to incorporate various safety measures. The rear has considerable amount of space to contain a proper crash box. The side of the car, though, doesn’t have this flexibility in design, the main limiting parameter being space. Any intrusion into the passenger cabin can result in serious injury or even death. The objective of this work is to improve the crashworthiness of a vehicle’s side so as to reduce intrusion into the passenger cabin. The work is focused on optimizing the door and B pillar. The optimized side panel is compared with the baseline model as per standard. ANSYS solver is used for the simulation. The optimized design applied to the door and B pillar will significantly improve crashworthiness of the vehicle side panel as a whole.
Tyre Traction Trailer is a device designed to find the Peak Brake co-efficient of C2 and C3 tyre as per ECE R117. The trailer is towed by the truck and is braked suddenly to evaluate braking co-efficient of specimen tyre. It is a single wheel trailer equipped with load cell to capture tire loads (Normal and longitudinal)while braking. Traction Trailer is modelled in MSC Adams and rigid body simulation is carried out for static stability of the system. Dynamic simulations were performed to understand locking of wheels during braking. Body frame was further modelled as flex body to perform structural analysis of the frame. The paper contains stress and deformation plots of trailer Structure under various loading conditions, change in Centre of gravity, weight transfer and forces on springs during braking and cornering, plots of tractive and normal load on tyre during braking.
RESEARCH OBJECTIVE: Automobile Industry has driven through the ages with continuous development with innovative technologies and frugal engineering. Expectation of customer is also increasing through the generations. To meet the customer demand for performance and be best in market, OEM needs to deliver best performance of vehicle with cost effective and short development process. Steering and Handling of vehicle is one of major customer touchpoints and needs to be tuned to achieve various conflicting requirements. The objective of this research is to optimize the steering and handling using correlation between three major methods of evaluation. METHODOLOGY: Methodology for optimization of steering and handling performance using correlation between subjective evaluation, objective measurement and multi-body-dynamic simulation is presented.