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Wheel loader is a heavy construction equipment, which is commonly used in construction, mining, transferring material etc. According to off highway research about Construction Equipment analysis in India, Wheel loader market is continuously growing because of road construction and mining [1]. One of the parameters which influences the productivity of the machine, is shape of the cutting edge of loader bucket. Poor design of bucket cutting edge results in poor digging, which ultimately affects machine performance and durability. In a wheel loader working cycle, the trajectory of the bucket structure is complicated and variable, which lead to complex working conditions. Cutting edge is the part of Wheel loader bucket, on which the OEMs put a lot of efforts in improving the penetration into the pile and performance. The maximum applied force on bucket cutting edges depend on working load conditions.

Most of the automobile and off-road vehicles leave the 100% exhaust gases to atmosphere. The temperature of the exhaust gas ranges from 350-400 deg C and the exit velocity of the gas is about 40-100 m/s based on the outlet pipe design. Dump trucks are used to transport mud, sticky waste garbage and sometime ice from one place to dump yard. The paper will describe the approach of partially use the exhaust gases for truck trolley by heating the trolley surfaces from the walls. CFD software is used to evaluate the exhaust system pressure drop and bypass exhaust flow rate requirements for effective heating on trolley wall. The simulation also helped to design the appropriate baffle position for optimum pressure drop and recirculation. Conjugate heat transfer CFD analysis is carried out to predict the flow & temperature behavior of the exhaust pipe.

The field performance of a machine is conventionally analyzed using tools of virtual validation such as physics-based simulation models. Machine performance test data is typically not incorporated for performance evaluation using these tools. The present work aims to demonstrate the use of Data Analytics (DA) as a tool to analyze this data for predictive purposes. It aims at establishing numerical relationships of engineering interest within the data, which would otherwise be complex if done only using physics-based models. Engine operation data spanning over three months, comprising of multiple channels, of an off-highway machine, is used for model development. Machine fuel burn rate is chosen as the dependent variable. Several independent variables such as engine speed, charge air pressure, NOx production level, are chosen based on their correlation with the dependent variable and upon engineering interest.

Thermal management in off road vehicles is critical because it directly or indirectly affects engine performance, fuel economy, safety, and emission. With the introduction of stringent exhaust emission norms such as the EU stage V and EPA Final Tier 4, modern engines use a Diesel Particulate Filter (DPF) to trap the soot particles present in the exhaust gases. These soot particles are burned using a process called regeneration where skin temperature of DPF increases beyond 400 °C. Situation becomes more worst when the vehicle is shutdown just after the regeneration, where the coolant fan is off and there is no active airflow. Hot air gets trapped and start increasing under hood temperature, affecting the performance of other sub systems like air intake, electrical components, aftertreatment sensors etc. There are several ways to manage this heat load. Normally the heat built up in the under-hood compartment escape to the environment by two paths - convection and radiation.

Steering is integral system of any vehicle to achieve direction control. With driver’s inputs, as steering wheel is turned, the steering mechanism is operated to turn the road wheels to route the vehicle on desired path. This Paper studies the different steering architectures and the learning is applied to improve the off-highway vehicles’ steering control. Off-Highway vehicles have traditionally been using Hydrostatic Power Steering (HPS) mechanism with no feedback controls. This mechanism consists of inherent limitations to vehicle efficiency, reliability and control preciseness. In this paper, HPS is modeled using 1D simulation tools from the system parameters and derived equations. The results are plotted and discussed to acknowledge the merits and demerits of existing system which helped in proposing the alternate steering solution. Different steering architectures are evaluated and trade-off study performed to choose the Electro-Hydraulic Power Steering (EHPS) architecture.

In any off-highway machinery throughout the product development cycle, noise is considered an important characteristic. This characteristic drives the product quality, safety, and productivity and meets the homologation requirements. Identifying the critical noise source and finding out the true root cause of the noise source is a very critical element in improving the design to reduce the noise levels. A systematic approach is needed to understand the behavior of the system, which can be achieved through collaborative efforts among the analysis, design, and testing teams. This paper describes how virtual analysis helps to determine the main source of noise radiation in the audible frequency range of the human ear. The sound pressure level (SPL) in the test data at the end unit drive of an agriculture machine showed high peaks at a few frequencies in the critical frequency range. The spectral content remains the same regardless of the backshaft speed.