Automobile Wheel Alignment and Wheel Balancing

With over thirty years of experience in the design, development, and patenting of some products in the field of wheel alignment, the author shares his knowledge on the importance of wheel maintenance to the overall performance of a vehicle. From the ancient bullock carts to chariots to automobiles, wheels have undergone many changes to serve the purpose of mankind's mobility. Mobility is inevitable in today's life. A world without wheels is unimaginable. Every vehicle owner expects his tires to last longer and perform better. But improper wheel alignment and wheel balancing can drastically impact the wear and tear on a tire. This book walks the readers through the basics to techniques for wheel alignment on light vehicles, commercial vehicles, and trailers. In addition, illustrations present various types of tire wear and the cause of each. Finally, the author delves into tire safety from understanding how air pressure effects a tire to the importance of tire rotation.

Markovian city-scale modelling and mitigation of micro-particles from tires

The recent uptake in popularity in vehicles with zero tailpipe emissions is a welcome development in the fight against traffic induced airborne pollutants. As vehicle fleets become electrified, and tailpipe emissions become less prevalent, non-tailpipe emissions (from tires and brake disks) will become the dominant source of traffic related emissions, and will in all likelihood become a major concern for human health. This trend is likely to be exacerbated by the heavier weight of electric vehicles, their increased power, and their increased torque capabilities, when compared with traditional vehicles. While the problem of emissions from tire wear is well-known, issues around the process of tire abrasion, its impact on the environment, and modelling and mitigation measures, remain relatively unexplored. Work on this topic has proceeded in several discrete directions including: on-vehicle collection methods; vehicle tire-wear abatement algorithms and controlling the ride characteristics of a vehicle, all with a view to abating tire emissions. Additional approaches include access control mechanisms to manage aggregate tire emissions in a geofenced area with other notable work focussing on understanding the particle size distribution of tire generated PM, the degree to which particles become airborne, and the health impacts of tire emissions. While such efforts are already underway, the problem of developing models to predict the aggregate picture of a network of vehicles at the scale of a city, has yet to be considered. Our objective in this paper is to present one such model, built using ideas from Markov chains. Applications of our modelling approach are given toward the end of this note, both to illustrate the utility of the proposed method, and to illustrate its application as part of a method to collect tire dust particles.

Assessing Wear Out of Tyre using Opencv & Convolutional Neural Networks

This work proposes a process to detect the wear and tear of car tires. Tire is the only part of the road that does not interact with the road. The condition of the wheel should therefore be monitored in a timely manner for safe driving. Tired fatigue occurs due to limitations such as that the tread limit is less than 1.6 cm, the damage to the rubber, where there are pipes around 4 to 5, the affected tire. We look at some of the above limitations of tire wear testing using computer viewing techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are widely used for object detection and image classification. We used these methods and obtained 90.90% accuracy, with which we can predict tire wear to avoid dangerous accidents..

Estimation of Tire Mileage and Wear Using Measurement Data

Tire mileage and wear provide important information for vehicle applications. There are more and more studies discussing intelligent tires, but few focus on the role of tire mileage and wear. The conventional tire pressure monitoring system (TPMS) is one of the intelligent tire applications, but there has been no significant advancement in recent years in this regard. In order to increase the additional functions of intelligent tire applications, we propose a method that estimates the mileage and wear information of tires. The proposed method uses a three-axis sensor and a Hall sensor to implement the function. The proposed method also has a low power design to reduce the power consumption of the Hall sensor. The experimental results show the trend of tire wear status, rendering this method effective. This method also requires more accurate mileage information to support tire wear estimation. This experiment found that the correct rate of the proposed mileage estimation method is 99.4% and provides sufficient and correct mileage information for tire wear methods. If this method is used in autonomous vehicle applications, the autonomous control strategy algorithm has more conditions to plan the control strategy. The strategy system processes more meticulous control that increases the safety of autonomous vehicles.

Insights into Elemental Composition and Sources of Fine and Coarse Particulate Matter in Dense Traffic Areas in Toronto and Vancouver, Canada

Traffic is a significant pollution source in cities and has caused various health and environmental concerns worldwide. Therefore, an improved understanding of traffic impacts on particle concentrations and their components could help mitigate air pollution. In this study, the characteristics and sources of trace elements in PM2.5 (fine), and PM10-2.5 (coarse), were investigated in dense traffic areas in Toronto and Vancouver, Canada, from 2015–2017. At nearby urban background sites, 24-h integrated PM samples were also concurrently collected. The PM2.5 and PM10-2.5 masses, and a number of elements (i.e., Fe, Ba, Cu, Sb, Zn, Cr), showed clear increases at each near-road site, related to the traffic emissions resulting from resuspension and/or abrasion sources. The trace elements showed a clear partitioning trend between PM2.5 and PM10-2.5, thus reflecting the origin of some of these elements. The application of positive matrix factorization (PMF) to the combined fine and coarse metal data (86 total), with 24 observations at each site, was used to determine the contribution of different sources to the total metal concentrations in fine and coarse PM. Four major sources were identified by the PMF model, including two traffic non-exhaust (crustal/road dust, brake/tire wear) sources, along with regional and local industrial sources. Source apportionment indicated that the resuspended crustal/road dust factor was the dominant contributor to the total coarse-bound trace element (i.e., Fe, Ti, Ba, Cu, Zn, Sb, Cr) concentrations produced by vehicular exhaust and non-exhaust traffic-related processes that have been deposited onto the surface. The second non-exhaust factor related to brake/tire wear abrasion accounted for a considerable portion of the fine and coarse elemental (i.e., Ba, Fe, Cu, Zn, Sb) mass at both near-road sites. Regional and local industry contributed mostly to the fine elemental (i.e., S, As, Se, Cd, Pb) concentrations. Overall, the results show that non-exhaust traffic-related processes were major contributors to the various redox-active metal species (i.e., Fe, Cu) in both PM fractions. In addition, a substantial proportion of these metals in PM2.5 was water-soluble, which is an important contributor to the formation of reactive oxygen species and, thus, may lead to oxidative damage to cells in the human body. It appears that controlling traffic non-exhaust-related metals emissions, in the absence of significant point sources in the area, could have a pronounced effect on the redox activity of PM, with broad implications for the protection of public health.

Characterization of PM-Bound Heavy Metal at Road Environment in Tianjin: Size Distribution and Source Identification

To determine the size distribution and source identification of PM-bound heavy metals in roadside environments, four different particle size (<0.2 μm, 0.2–0.5 μm, 0.5–1.0 μm and 1.0–2.5 μm) samples were collected and analyzed from four different types of roads during the summer of 2015 in Tianjin. The results showed that the concentrations of PM-bound heavy metal from the roadside environment sampling sites were 597 ± 251 ng/m3 (BD), 546 ± 316 ng/m3 (FK), 518 ± 310 ng/m3 (JY) and 640 ± 237 ng/m3 (WH). There were differences in the concentrations of the heavy metal elements in the four different particle size fractions. The concentrations of Cu, Zn, Cd, Sn and Pb were the highest in the larger particle size fraction (0.5–2.5 μm). Cd, Cu, Zn and Pb were the elements that indicated emissions from tire wear and brake pad wear. The concentrations of Cr, Co and Ni were the highest in the smallest particle size fraction (<0.5 μm), indicating that motor vehicle exhaust was their main source. The correlation analysis results showed that there are differences in the concentration, distribution and correlation of different PM-bound heavy metals in different particle size fractions. The PCA results show that the accumulative interpretation variances of PM0.2, PM0.2–0.5, PM0.5–1.0 and PM1.0–2.5 reached 80.29%, 79.56%, 79.57% and 71.42%, respectively. Vehicle exhaust was the primary source of PM-bound heavy metal collected from the roadside sampling sites, while brake pad wear and tire wear were the second most common sources of the heavy metal.