Search Results

Abstract The development of electric commercial vehicles brought up novel challenges in the design of efficient and reliable air brake systems. The compressor is one of the critical components of the air brake system and is responsible for supplying pressurized air to the brake system. In this study, we aimed to gather essential information regarding the pressure and flow rate requirements for the compressor in the air brake system of electric commercial vehicles. We extensively analyzed the existing air brake systems utilized in conventional commercial vehicles. We examined the performance characteristics of reciprocating compressors traditionally employed in these systems. Recognizing the need for novel compressor designs tailored to electric commercial vehicles, we focused on identifying the specifics such as efficiency, performance characteristics, reliability, and cost of the compressor.

Abstract Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy.

Abstract This research looks into how abrasive water jet machining (AWJM) can be used on carbon fiber-reinforced polymer (CFRP) materials, specifically how the kerf characteristics change with respect to change in process parameters. We carefully looked into four important process parameters: stand-off distance (SOD), water pressure (WP), traverse rate (TR), and abrasive mass flow rate (AMFR). The results showed that as SOD goes up, the kerf taper angle goes up because of jet dispersion, but as WP goes up, the angle goes down because jet kinetic energy goes up. The TR was directly related to the kerf taper angle, but it made the process less stable. The kerf drop angle was not greatly changed by AMFR. When it came to kerf top width, SOD made it wider, WP made it narrower, TR made it narrower, and AMFR made it a little wider. When the settings (SOD: 1 mm, WP: 210 MPa, TR: 150 mm/min, AMFR: 200 g/min) were optimized, the kerf taper angle and kerf top width were lowered.

Abstract This article presents a strategy for the virtual calibration of a large-scale model representing a self-piercing rivet (SPR) connection. The connection is formed between a stack of three AA6016-T4 aluminum sheets and one SPR. The calibration process involves material characterization, a detailed riveting process simulation, virtual joint unit tests, and the final large-scale model calibration. The virtual tests were simulated by detailed solid element FE models of the joint unit. These detailed models were validated using experimental tests, namely peeling, single-lap joint, and cross-tests. The virtual parameter calibration was compared to the experimental calibration and finally applied to component test simulations. The article contains both experiments and numerical models to characterize the mechanical behavior of the SPR connection under large deformation and failure.

Abstract This study aims to explore the wear characteristics of fused deposition modeling (FDM) printed automotive parts and techniques to improve wear performance. The surface roughness of the parts printed from this widely used additive manufacturing technology requires more attention to reduce surface roughness further and subsequently the mechanical strength of the printed geometries. The main aspect of this study is to examine the effect of process parameters and annealing on the surface roughness and the wear rate of FDM printed acrylonitrile butadiene styrene (ABS) parts to diminish the issue mentioned above. American Society for Testing and Materials (ASTM) G99 specified test specimens were fabricated for the investigations. The parameters considered in this study were nozzle temperature, infill density, printing velocity, and top/bottom pattern.

Abstract Automotive subframe is a critical chassis component as it connects with the suspension, drive units, and vehicle body. All the vibration from the uneven road profile and drive units are passed through the subframe to the vehicle body. OEMs usually have specific component-level drive point dynamic stiffness (DPDS) requirements for subframe suppliers to achieve their full vehicle NVH goals. Traditionally, the DPDS improvement for subframes welded with multiple stamping pieces is done by thickness and shape optimization. The thickness optimization usually ends up with a huge mass penalty since the stamping panel thickness has to be changed uniformly not locally. Structure shape and section changes normally only work for small improvements due to the layout limitations. Tuned rubber mass damper (TRMD) has been widely used in the automotive industry to improve the vehicle NVH performance thanks to the minimum mass it adds to the original structure.

Abstract With the rapid development and the growing deployment of autonomous ground vehicles (AGVs) worldwide, there is an increasing need to design reliable, efficient, robust, and scalable motion-planning algorithms. These algorithms are crucial for fulfilling the desired goals of safety, comfort, efficiency, and accessibility. To design optimal motion-planning algorithms, it is beneficial to explore existing techniques and make improvements by addressing the limitations of associated techniques, utilizing hybrid algorithms, or developing novel strategies. This article categorizes and overviews numerous motion-planning algorithms for AGVs, shedding light on their strengths and weaknesses for a comprehensive understanding.

Abstract With the continuous progress of society, people have higher and higher requirements for the quality of life. Energy is an important substance base for human existence and development. In the construction industry, construction project construction period cost control has become particularly important. The existing engineering projects have not significantly optimized the quality of construction period cost, leading to waste of resources. Therefore, it is particularly important to establish an efficient, reasonable, and perfect system to ensure the scientific use of construction project construction period cost.

Abstract After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus® database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.”

Abstract A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract During mining material hauling, the chassis frame structure of rear dump trucks is subjected to fatigue loading due to uneven road conditions. This loading often leads to crack propagation in the frame rails, necessitating the determination of stresses in the critical zone during the design stage to ensure structural integrity. In this study, a computer-aided engineering (CAE) methodology is employed to size and select the rectangular profile cross section of the chassis frame rail. A detailed design investigation of the chassis frame is conducted to assess its load resistance, structural flexibility, and weld joint fatigue life under critical stresses arising from combined bending and torsion loads. The optimization process aims to determine the optimal rail size and material thickness, striking a balance between minimizing mass and maximizing structural reliability.

Abstract With the use of the stepped surface of the friction pairs of the stepped bearings (SB) in the high-speed centrifugal pumps, its liquid film thickness is suddenly changed and it was discontinuously distributed in the direction of motion of pump. To ensure the continuity of the liquid film thickness and enhance the lubrication efficiency of the pump, based on the lubrication model of the SB, two other structures of the inclined surfaces [inclined bearings (IB)] and curved surfaces [curved bearings (CB)] used to replace stepped surfaces of the SB are investigated, respectively. Under the same conditions of the minimum thickness of the liquid film and initial dimensions of the sliding friction pairs, the influence of both the thickness ratio (α) of the liquid film and dimension ratio (β) in the direction of motion of SB, IB, and CB on the bearing capacity and friction coefficient of the liquid film are simulated and analyzed, respectively.

Abstract In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN).

Abstract This study underscores the benefits of refining the intralogistics process for small- to medium-sized manufacturing businesses (SMEs) in the engineer-to-order (ETO) sector, which relies heavily on manual tasks. Based on industrial visits and primary data from six SMEs, a new intralogistics concept and process was formulated. This approach enhances the value-added time of manufacturing workers while also facilitating complete digital integration as well as improving transparency and traceability. A practical application of this method in a company lead to cutting its lead time by roughly 11.3%. Additionally, improved oversight pinpointed excess inventory, resulting in advantages such as reduced capital needs and storage requirements. Anticipated future enhancements include better efficiency from more experienced warehouse staff and streamlined picking methods. Further, digital advancements hold promise for cost reductions in administrative and supportive roles.

Abstract Being an engineer-to-order (ETO) operating industry, the control cabinet industry faces difficulties in process and workplace optimizations due to changing requirements and lot size one combined with volatile orders. To optimize workplaces for employees, current literature is focusing on ergonomic designs, providing frameworks to analyze workplaces, leaving out the optimal design for productivity. This work thus utilizes a Kano analysis, collecting empirical data to identify essential design requirements for assembly workplaces, incorporating input from switchgear manufacturing employees. The results emphasize the need for a balance between ergonomics and efficiency in workplace design. Surprisingly, few participants agree on the correlation between improved processes and workspaces having a positive impact on their well-being and product quality.

Abstract In view of the combustion efficiency and emission performance, various new clean combustion modes put forward higher requirements for the performance of the fuel injection system, and the cavitating two-phase flow characteristics in the injector nozzle have a significant impact on the spray atomization and combustion performance. This article comprehensively discusses and summarizes the factors that affect cavitation and the effectiveness of cavitation, and presents the research status and existent problems under each factor. Among them, viscosity factors are a hot research topic that researchers are passionate about, and physical properties factors still have the value of further in-depth research. However, the importance of material surface factors ranks last since the nozzle material was determined. Establishing a more comprehensive cavitation–atomization model considering various factors is the focus of research on cavitation phenomena.

Abstract Electric motors constitute a critical component of an electric vehicle powertrain. An improved motor design can help improve the overall performance of the drivetrain of an electric vehicle making it more compact and power dense. In this article, the electromagnetic torque output of a double V-shaped traction IPMSM is maximized by geometry optimization, while considering overall material cost minimization as the second objective. A robust and flexible parametric model of the IPMSM is developed in ANSYS Maxwell 2D. Various parameters are defined in the rotor and stator geometries to perform an effective multi-objective parametric design optimization. Advanced sensitivity analysis, surrogate modeling, and optimization capabilities of ANSYS optiSlang software are leveraged in the optimization. Furthermore, a demagnetization analysis is performed to evaluate the robustness of the optimized design.

Abstract The exponentially growing electrification market is driving demand for lithium-ion batteries (LIBs) with high performance. However, LIB thermal runaway events are one of the unresolved safety concerns. Thermal runaway of an individual LIB can cause a chain reaction of runaway events in nearby cells, or thermal propagation, potentially causing significant battery fires and explosions. Such a safety issue of LIBs raises a huge concern for a variety of applications including electric vehicles (EVs). With increasingly higher energy-density battery technologies being implemented in EVs to enable a longer driving mileage per charge, LIB safety enhancement is becoming critical for customers. This comprehensive review offers an encompassing overview of prevalent abuse conditions, the thermal event processes and mechanisms associated with LIBs, and various strategies for suppression, prevention, and mitigation.

Abstract The cooperative platoon of multiple trucks with definite proximity has the potential to enhance traffic safety, improve roadway capacity, and reduce fuel consumption of the platoon. To investigate the truck platooning performance in a real-world environment, two Peterbilt class-8 trucks equipped with cooperative truck platooning systems (CTPS) were deployed to conduct the first-of-its-kind on-road commercial trial in Canada. A total of 41 CTPS trips were carried out on Alberta Highway 2 between Calgary and Edmonton during the winter season in 2022, 25 of which were platooning trips with 3 to 5 sec time gaps. The platooning trips were performed at ambient temperatures from −24 to 8°C, and the total truck weights ranged from 16 to 39 tons. The experimental results show that the average time gap error was 0.8 sec for all the platooning trips, and the trips with the commanded time gap of 5 sec generally had the highest variations.