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Laser cladding is a method of material deposition through which a powdered or wire feedstock material is melted and consolidated by use of a laser to coat part of a substrate. Determining the parameters to fabricate the desired clad bead geometry for various configurations is problematic as it involves a significant investment of raw materials and time resources, and is challenging to develop a predictive model. The goal of this research is to develop an experimental methodology that minimizes the amount of data to be collected, and to develop a predictive model that is accurate, adaptable, and expandable. To develop the predictive model of the clad bead geometry, an integrated five-step approach is presented. From the experimental data, an artificial neural network model is developed along with multiple regression equations.

Automotive Open System Architecture (AUTOSAR) is a system-level standard that is formed by the worldwide partnership of the automotive manufacturers and suppliers who are working together to develop a standardized Electrical and Electronic (E/E) framework and architecture for automobiles. The AUTOSAR methodology has two main activities: system configuration and the Electronic Control Unit (ECU) configuration. The system configuration is the mapping of the software components to the ECUs based on the system requirements. The ECU configuration process is an important part of the ECU software integration and generation. ECU specific information is extracted from the system configuration description and all the necessary information for the implementation such as tasks, scheduling, assignments of the runnables to tasks and configuration of the Basic Software (BSW) modules, are performed. The ECU configuration process involves configuring every single module of the AUTOSAR architecture.

The ‘boundary of space’ model representing all possible positions which may be occupied by a mechanism during its normal range of motion (for all positions and orientations) is called the work envelope. In the robotic domain, it is also known as the robot operating envelope or workspace. Several researchers have investigated workspace boundaries for different degrees of freedom (DOF), joint types and kinematic structures utilizing many approaches. The work envelope provides essential boundary information, which is critical for safety and layout concerns, but the work envelope information does not by itself determine the reach feasibility of a desired configuration. The effect of orientation is not captured as well as the coupling related to operational parameters. Included in this are spatial occupancy concerns due to linking multiple kinematic chains, which is an issue with multi-tasking machine tools, and manufacturing cells.

Contemporary manufacturing systems are still evolving. The system elements, layouts, and integration methods are changing continuously, and ‘collaborative robots’ (CoBots) are now being considered as practical industrial solutions. CoBots, unlike traditional CoBots, are safe and flexible enough to work with humans. Although CoBots have the potential to become standard in production systems, there is no strong foundation for systems design and development. The focus of this research is to provide a foundation and four tier framework to facilitate the design, development and integration of CoBots. The framework consists of the system level, work-cell level, machine level, and worker level. Sixty-five percent of traditional robots are installed in the automobile industry and it takes 200 hours to program (and reprogram) them.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

The paper describes a ‘virtual motorsports’ event developed by the University of Windsor Vehicle Dynamics and Control Research Group. The event was a competitive project-based component of a Vehicle Dynamics course offered by the University's Department of Mechanical, Automotive, & Materials Engineering. The simulated race was developed to provide fourth year automotive engineering students with design and race experience, similar to that found in Formula SAE®or SAE Baja®, but within the confines of a single academic semester. The project, named ‘Formula463’, was conducted entirely within a virtual environment, and encompassed design, testing, and racing of hi-fidelity virtual vehicle models. The efficacy of the Formula463 program to provide students with a design experience using model based simulation tools and methods has been shown over the past two years. All of the software has been released under a General Public License and is freely available on the authors website.

Light-weighting fiber composite materials introduced to reduce vehicle mass and known as innovative materials research activities since they provide high specific stiffness and strength compared to contemporary engineering materials. Nonetheless, there are issues related automation strategies and handling methods. Material handling of flexible textile/fiber components is a process bottleneck and it is currently being performed by setting up multi-stage manual operations for hand layups. Consequently, the long-term research objective is to develop semi-automated pick and place processes for flexible materials utilizing collaborative robots within the process. The immediate research is to experimentally validate innovatively designed grippers for efficient material pick and place tasks.

End-of-life is the least studied phase of the vehicle life-cycle. Dismantling and shredding are the principal processes used for vehicle end-of-life (VEOL) management in Canada and the U.S. and are typically perceived as distinct processes, each one having its own unique challenges. Dismantling typically precedes shredding, with vehicle parts and materials removed for direct reuse, for remanufacturing and reuse, or for recycling. Dismantling may be perceived as a non-preferred alternative, compared to shredding, because it is principally a manual process which can be cost prohibitive in the North America/western labour market. However, there has been no exhaustive assessment of the dismantling process. Because of the complexity in automobiles, significantly more needs to be known about dismantling, its benefits and impacts, its efficiencies and inefficiencies, and its relation to other ELV management processes.

A Mg-5.0wt.%Al-2.0wt.%Ca alloy (AC52) was cast at different cooling rates varying from 0.5 to 65 °C/s. The dendrites was characterized by determining the secondary dendrite arm spacing (SDAS) and the volume fraction of secondary eutectic phases with the linear intercept and point counting methods, respectively. The SDAS decreases significantly with increasing cooling rates, while the volume fraction of the eutectic phase increases from 10.8 ± 1.44 vol.% at 0.5 °C/s to 20.4 ± 1.52 vol.% at 20 °C/s. However, a further increase in cooling rate beyond 20 °C/s has limited influence on the volume fraction of eutectic phases. A large number of dispersed eutectic phases were observed in the primary α-Mg of the alloys cast at low cooling rates. Although, at the microscale, there were no dispersed eutectic phases in alloys cast at a high cooling rate of 30 °C/s, nanoscale eutectic phases were found by TEM observation.

One critical aspect of design-for-environment efforts is to increase the effectiveness of materials recovery from end-of-life vehicles. Recovery itself depends on both the amount of material recovered and the purity of the material stream. Shredding, and screening are often used to separate recyclable materials from wastes. However, with the increasing amount of composite components, particularly those made from plastics, separation processes may be inadequate. Instead, liberation processes, which reduce the physical joints between materials, are also important. In this research, samples of ABS and PVC plastics were assembled into various configurations, ground up, and then characterized by their size distributions and degrees of liberation. Two primary fastening methods - adhesive and riveting - were used to simulate how plastic components would be actually attached together.

Laser cladding is a novel process of surface coating, and researchers in both academia and industry are developing additive manufacturing solutions for large, metallic components. There are many interlinked process parameters associated with laser cladding, which may have an impact on the resultant microhardness profile throughout the bead zone. A set of single bead laser cladding experiments were done using a 4 kW fiber laser coupled with a 6-axis robotic arm for 420 martensitic stainless steel powder. A design of experiments approach was taken to explore a wide range of process parameter settings. The goal of this research is to determine whether robust predictive models for hardness can be developed, and if there are predictive trends that can be employed to optimize the process settings for a given set of process parameters and microhardness requirements.

Modularity in product architecture and its significance in product development have become an important product design topics in the last few decades. Several Product Modularity definitions and methodologies were developed by many researchers; however, most of the definitions and concepts have proliferated to the extent that it is difficult to apply one universal definition for modular product architecture and in product development. Automotive seat modular strategy and key factors for consideration towards modular seat design and assemblies are the main focus of this work. The primary objectives are focused on the most “natural segmentation” of the seat elements (i.e., cushions, backs, trims, plastics, head restraints, etc.) to enable the greatest ease of final assembly and greatest flexibility for scalable feature offerings around common assembly “hard-points.”

Thermal comfort in automotive seating has been studied and discussed for a long time. The available research, because it is focused on the components, has not produced a model that provides insight into the human-seat system interaction. This work, which represents the beginning of an extensive research program, aims to establish the foundation for such a model. This paper will discuss the key physiological, psychological, and biomechanical factors related to perceptions of thermal comfort in automotive seats. The methodology to establish perceived thermal comfort requirements will also be presented and discussed.

This study provides an overview of a novel method for evaluating in-vehicle speech intelligibility using the Speech Intelligibility Index (SII). The approach presented is based on a measured speech signal evaluated at the sentence Speech Reception Threshold (sSRT) in a simulated driving environment. In this context, the impact of different band importance functions in the evaluation of the SII using the Hearing in Noise Test (HINT) in a driving simulator is investigated.

Automotive Open System Architecture (AUTOSAR) is a system-level standard that is formed by the worldwide partnership of automotive manufacturers and suppliers who are working together to develop a standardized Electrical and Electronic (E/E) framework and architecture for automobiles. The AUTOSAR methodology has two main activities: system configuration and the Electronic Control Unit (ECU) configuration. The system configuration is the mapping of the software components to the ECUs based on the system requirements. The ECU configuration (EC) process is an important part of the ECU software integration and generation. ECU-specific information is extracted from the system configuration description, and all the necessary information for the implementation such as tasks, scheduling, and assignments of the runnables to tasks and configuration of the Basic Software (BSW) modules are performed. The EC process involves configuring every single module of the AUTOSAR.

Neural networks are flexible modeling tools that can be used in conjunction with multi-body dynamics models to better predict nonlinear behaviour of components. This paper focuses on a process that incorporates a neural network model of a nonlinear damping force into a single degree of freedom mass-spring-damper model. Software tools and their interaction are specified. The verification of this process is the focal point of this paper and is a necessary step before further correlation studies can be performed on more complex component representations.

Performance of the ignition system becomes more important than ever, because of the extensively used EGR in modern spark-ignition engines. Future lean burn SI and SACI combustion modes demand even stronger ignition capability for robust ignition control. For spark-based ignition systems, extensive research has been carried out to investigate the discharge characteristics of the ignition process, including discharge current amplitude, discharge duration, spark energy, and plasma stretching. The correlation between the spark stretch and the discharge energy, as well as the impact of discharge current level on this correlation, are important with respect to both ignition performance, and ignition system design. In this paper, a constant volume combustion chamber is applied to study the impact of plasma stretch on the spark energy release process with cross-flow speed from 0 m/s up to 70 m/s.

Gantry robots are mainly employed for applications requiring large workspace, with limited higher manipulability in one direction than the others. The Gantries offer very good mechanical stiffness and constant positioning accuracy, but low dexterity. Common gantries are CNC machines with three translational joints XYZ (3DOF) and usually with an attached wrist (+3DOF). The translational joints are used to move the tool in any position in the 3D workspace. The wrist is used to orient the tool by rotation about X, Y and Z axis. This standard kinematic structure (3T3R) produces a rectangular workspace. In this paper a full kinematic model for a 6DOF general CNC (gantry) machine is presented, along with the Jacobian matrix and singularity analysis. Using Denavit-Hartenberg convention, firstly, the general kinematic structure is presented, in order to assign frames at each link. The forward kinematic problem is solved using Maple 17 software.

Advanced ignition systems with enhanced discharge current have been extensively investigated in research, since they are highly regarded as having the potential to overcome challenges that arise when spark-ignition engines are running under lean or EGR diluted conditions. Local flow field is also of particular importance to improve the ignitability of the air-fuel mixture in SI engines as the spark plasma channel can be stretched by the flow across the spark gap, leading to longer plasma length, thus more thermal spark energy distributed to the air-fuel mixture in the vicinity of the spark plug. Research results have shown that a constantly high discharge current is effective to maintain a stable spark plasma channel with less restrikes and longer plasma holding period.