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The Process FMEA and Control Plan program introduces the basic concepts behind this important tool and provides training in how to conduct an effective PFMEA. First, the course explains what a PFMEA is and how it improves the long-term performance of your products, services and related processes by addressing process related failures. The role of the PFMEA in the overall framework of Quality Management System Requirements is explained as well as the role of the PFMEA in the Advanced Product Quality Planning (APQP) process. Additionally, the differences and relationships between the DFMEA and PFMEA are well defined.

During this DFMEA Overview and Application course, participants will be introduced to important FMEA concepts, the basic theory behind the concepts, then discuss how these concepts can be applied to the customer's design FMEA activities. Participant activities include: reading assignments, group discussions, exercises, building Block Diagrams as a group, and beginning a DFMEA on a customer’s product.

The concept of a Quality System’s approach to business has been employed successfully and sometimes not so successfully for several decades. The International Organization for Standardization (ISO) has been supplying standards that list the key elements/clauses and requirements for building and implementing Quality Systems for over 30 years. These standards are based on the relatively simple concepts of Total Quality Management (TQM), essential principals of management, and a “Process” approach. These standards have been revised several times over the years to make them more realistic and user friendly.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). Currently in the industry, especially within China, product requirement development is more of an experience-based process rather than a scientific methodology. This course addresses this issue and provides a more process-driven method for better requirement development through the Quality Function Deployment (QFD) methodology.  Real industrial examples are used to demonstrate how to systematically convert the voice of the customer data to engineering specifications using QFD.

This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. Advanced product quality planning (APQP) is essential to improving the way companies develop products and services.  It is a standardized, universally accepted fundamental business strategy. This strategy is applicable to all types of organizations including manufacturing and service companies, schools, hospitals, and governmental agencies. The aim of APQP is to enable the organization to produce products and provide services focused on satisfying customer’s needs, wants, and expectations.  

Design of Experiments is a statistically based, structured approach to product or process improvement that will quickly yield significant increases in product quality and subsequent decreases in cost.  Products and processes can be designed to function with less variation and with less sensitivity to environmental factors or customer usage. While still maintaining high quality from a customer's viewpoint, products and processes can utilize lower cost materials and methods.  Specifications can be opened-up with wider tolerances while still maintaining high quality for customers.  

This class will provide the student with the skills, knowledge, and abilities to interpret, analyze and apply HVEDR data in real-world applications. This course has been designed to build on the concepts presented in the SAE course Accessing and Interpreting Heavy Vehicle Event Data Recorders (ID# C1022). Advanced topics will include associating HVEDR data with collision events through timestamps, odometer logs, and data signatures, validating HVEDR speed data using specified vehicle parameters, performing time and distance analyses using HVEDR data, and correlating HVEDR data to physical evidence from the vehicle and roadway.

Billions of dollars have been invested in AV trucking. It is no longer a matter of IF, it is a matter of When, Where, Who and How? This will be the most disruptive event to happen in our supply chains in more than 4 decades. Are you ready to help your company usher in the most disruptive technology? This class will help you prepare and understand what you will need to do to become part of the ecosystem. You will learn how to identify what needs to start, stop, and change for you to adopt, integrate, and scale. Join us to learn the answers to key questions like the following: 1)How will maintenance change in the AV trucking ecosystem?

Design of Experiments (DOE) is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings. This course utilizes hands-on activities to help you learn the criteria for running a DOE, the requirements and pre-work necessary prior to DOE execution, and how to select the appropriate designed experiment type to run.

RMS (Reliability-Maintainability-Safety-Supportability) engineering is emerging as the newest discipline in product development due to new credible, accurate, quantitative methods. Weibull Analysis is foremost among these new tools. New and advanced Weibull techniques are a significant improvement over the original Weibull approach. This workshop, originally developed by Dr. Bob Abernethy, presents special methods developed for these data problems, such as Weibayes, with actual case studies in addition to the latest techniques in SuperSMITH® Weibull for risk forecasts with renewal and optimal component replacement.

Battery Electric Vehicles and Extended Range Electric Vehicles, like the Chevrolet Volt, can use electrical energy from the Grid to meet the majority of a driver�s transportation needs. This has the positive societal effects of displace petroleum consumption and associated pollutants from combustion on a well to wheels basis, as well as reduced energy costs for the driver. CO2 may also be lower, but this depends upon the nature of the grid energy generation. There is a mix of sources � coal-fired, gas -fired, nuclear or renewables, like hydro, solar, wind or biomass for grid electrical energy. This mix changes by region, and also on the weather and time of day. By monitoring the grid mix and communicating it to drivers (or to their vehicles) in real-time, electrically driven vehicles may be recharged to take advantage of the lowest CO2, and potentially lower cost charging opportunities.

Driver assistance systems (e.g. the emergency brake assist Active Brake Assist2, or ABA2 for short, in the Mercedes-Benz Actros) are becoming increasingly common in heavy-duty commercial vehicles. Due to the close interconnection with drivetrain and suspension control systems, the integration and validation of the functions make the most exacting demands on processes and tools involved in mechatronics development. Presenter Thomas Bardelang, Daimler AG

Abstract Truck platooning comprises a number of trucks equipped with automated lateral and longitudinal vehicle control technology, which allows them to move in tight formation with short following distances. This study is an initial step toward developing an understanding of the occupant injury risks associated with the multiple sequential impacts between truck platoons and roadside safety barriers, regardless of whether the crash is associated with a malfunction of automated control or human operation. Full-scale crash impacts of a tractor-trailer platoon into a concrete bridge guardrail were simulated for a specific Test Level condition according to the Manual for Assessing Safety Hardware (MASH) standards. The model of the bridge barrier was developed based on its drawings, and material properties were assigned according to literature data.

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract The development, analysis, and optimization of battery electric class-7 heavy-duty trucks equipped with multi-speed transmissions are discussed in this paper. The designs of five new traction motors-fractional-slot, concentrated winding machines-are proposed for use in heavy-duty electric trucks. The procedure for gear-ratio range selection is outlined and ranges of gear ratios for three-to six-speed transmission powertrains are calculated for each of the proposed electric traction motors. The simulation and gear-ratio optimization tasks for class-7 battery electric trucks are formulated. The energy consumption of the e-truck with the twenty possible powertrain combinations is minimized over the four driving cycles and the most efficient powertrain layouts that meet the performance criteria are recommended.

Abstract Aiming to improve the handling performance of heavy tractor semi-trailer during turning or changing lanes at high speed, a hierarchical structure controller is proposed and a hardware-in-the-loop (HIL) test bench of the electronic pneumatic braking system is developed to validate the proposed controller. In the upper controller, a Kalman filter observer based on the heavy tractor semi-trailer dynamic model is used to estimate the yaw rates and sideslip angles of the tractor and trailer. Simultaneously, a sliding mode direct yaw moment controller is developed, which takes the estimated yaw rates and sideslip angles and the reference values calculated by the three-degrees-of-freedom dynamic model of the heavy tractor semi-trailer as the control inputs. In the lower controller, the additional yaw moments of tractor and trailer are transformed into corresponding wheel braking forces according to the current steering characteristics.

Abstract A kinematic path-following model is developed based on an existing modeling framework established by the authors [1, 2] for prediction of the paths of towing vehicle systems. The presented path-following model determines the path of the towing vehicle using the vehicle’s speed and acceleration data collected by an inertial measurement unit (IMU). An Ackerman steering model was presented to calculate instantaneous directional angles and radii for each towed vehicle based on its geometric data and steering angle. In that model the off-tracking effect is properly captured. A 1:4 scale model for a towing vehicle system was built to test the developed steering model, and it was found that the angles and radii of the towing vehicle and each towed unit calculated using the Ackerman steering model agreed very well with those measured from the scale model.

Abstract Improving the aerodynamics of heavy trucks is an important consideration in the strive for more energy-efficient vehicles. Cooling drag is one part of the total aerodynamic resistance acting on a vehicle, which arises as a consequence of air flowing through the grille area, the heat exchangers, and the irregular under-hood area. Today cooling packages of heavy trucks are dimensioned for a critical cooling case, typically when the vehicle is driving fully laden, at low speed up a steep hill. However, for long-haul trucks, mostly operating at highway speeds on mostly level roads, it may not be necessary to have all the cooling airflow from an open-grille configuration. It can therefore be desirable for fuel consumption purposes, to shut off the entire cooling airflow, or a portion of it, under certain driving conditions dictated by the cooling demands. In Europe, most trucks operating on the roads are of cab-over-engine type, as a consequence of the length legislations present.

Abstract Reliability analysis of a large-scale system under random dynamic loads can be a very time-consuming task since it requires repeated studies of the system. In many engineering problems, for example, wave loads on an offshore platform, the excitation loads are defined using a power spectral density (PSD) function. For a given PSD function, one needs to generate many time histories to make sure the excitation load is modeled accurately. Global and local approximation methods are available to predict the system response efficiently. Each way has their advantages and shortcomings. The combined approximations (CA) method is an efficient method, which combines the advantages of local and global approximations. This work demonstrates two methodologies that utilize CA to reduce the cost of crude or separable Monte Carlo simulation (MCS) of linear dynamic systems when the excitation loads are defined using PSD functions.

Abstract Recently, electric heavy-duty tractor-trailers (EHDTTs) have assumed significance as they present an immediate solution to decarbonize the transportation sector. Hence, to illustrate the economic viability of electrifying the freight industry, a detailed numerical model to estimate the battery capacity for an EHDTT is proposed for a route between Washington, DC, to Knoxville, TN. This model incorporates the effects of the terrain, climate, vehicular forces, auxiliary loads, and payload in order to select the appropriate motor and optimize the battery capacity. Additionally, current and near-future battery chemistries are simulated in the model. Along with equations describing vehicular forces based on Newton’s second law of motion, the model utilizes the Hausmann and Depcik correlation to estimate the losses caused by the capacity offset of the batteries. Here, a Newton-Raphson iterative scheme determines the minimum battery capacity for the required state of charge.