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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 automotive electronic systems are composed of two major mechanical elements: an equipment housing or enclosure, and a printed circuit board (PCB) assembly. The PCB is made up of alternating layers of copper and FR-4 glass epoxy laminated together. An estimation of the mechanical reliability of a PCB in an electronic system is considered to be an important part of the overall reliability estimate of the entire system and vibration is often one of the key causes of system and component failures. As different kind of electronic components (like transformers, capacitors, chips etc) are mounted on both sides of the PCB using solder joints, adhesive etc, various complexities are encountered while modeling them for analysis. For avoiding those, simple PCBs without any components are considered for the present study. This paper focuses on the methodology to understand and predict the dynamic behavior of the system using various mechanical tests and simulations.

Earth moving machines are steered using various steering methodology. Articulated steering mechanism is the most popular type of steering methods. It is actuated by hydraulic cylinders connected to the steering linkages. While the machine is deployed with articulated type steering, vehicle stability is proportionate to the steering velocity. Articulated steering provides two important features, shorter turning radius, allows front and back axle to be solid. Directional stability refers to a vehicle’s ability to stabilize its direction of motion against disturbances. Majority of earth moving equipment operates on basic cycle of Load-Haul-Dump. During each cycle, vehicle needs to be steered at least once. To optimize the machine performance for improved productivity it is essential to study the correlation between articulation speed and operational weight of the vehicle.

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.

This paper proposes a practical optimal-time window-based path-planning approach for a fleet of autonomous vehicles. Specifically, autonomous vehicles in this work refers to fleet of tractors that performs spraying operations in a vineyard field. The approach involves two main steps. In the first step based on a behavior and actions of the tractors that mimic manual spraying operations, a linear integer programming (ILP) optimization model is constructed. The second step then seeks a solution for this MIP model to obtain paths for autonomous navigation of the tractors in a vineyard field. The simulation results on a real-world data collected using Google Maps application for Sula vineyards located in the Nashik region [1] is reported. The obtained results show effectiveness of the proposal with respect to manual operator driven fleet management.

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.