The paper investigated the effect of localized friction on sheet thinning under lubricated conditions in the deep-drawing process. The Finite Element Analysis (FEA) was used to evaluate the sheet thinning of the AISI 304 sheet under six segmented blank-sheet interfaces. Different values of variable coefficient of friction (VCOF) in each segmented area were investigated, and the sheet thickness values at the considered areas were measured. The regression analysis models (Linear Regression, Response Surface Method, and Polynomial Regression) was used to determine the relationships between VCOF and sheet thinning. The results showed that the Linear Regression showed the best fit. The significant factor analysis was also carried out to determine how the localized friction affected the sheet thinning. The contributions of VCOF from at least two segmented areas affected the sheet thinning at any particular location. The obtained relationships of the VCOF and sheet thinning could be beneficial for the localized friction control for highly complex shapes.

In this paper, we propose a deep Q-network (DQN) method to develop an autonomous vehicle control system to achieve trajectory design and collision avoidance with regard to obstacles on the road in a virtual environment. The intention of this work is to simulate a case scenario and train the DQN algorithm in a virtual environment before testing it in a real scenario in order to ensure safety while reducing costs. The CARLA simulator is used to emulate the motion of the autonomous vehicle in a virtual environment, including an obstacle vehicle parked on the road while the autonomous vehicle drives on the road. The target position, real-time position, velocity, and LiDAR point cloud information are taken as inputs, while action settings such as acceleration, braking, and steering are taken as outputs. The actions are sent to the torque control in the transmission system of the vehicle. A reward function is created using continuous equations designed, especially for this case, in order to imitate human driving behaviors. The results demonstrate that the proposed method can be used to navigate to the destination without collision with the obstacle, through the use of braking and dodging methods. Furthermore, according to the trend of DQN behavior, a better result can be obtained with an increased number of training episodes. This method is a non-global path planning method successfully implemented on a virtual environment platform, which is an advantage of this method over other autonomous vehicle designs, allowing for simulation testing and application with further experiments in future work.

In heat exchange applications, the heat transfer efficiency could be improved by surface modifications. Shot peening was one of the cost-effective methods to provide different surface roughness. The objectives of this study were (1) to investigate the influences of the surface roughness on the heat transfer performance and (2) to understand how the shot peening process parameters affect the surface roughness. The considered specimens were 316L stainless steel hollow tubes having smooth and rough surfaces. The computational fluid dynamics (CFD) simulation was used to observe the surface roughness effects. The CFD results showed that the convective heat transfer coefficients had linear relationships with the peak surface roughness (Rz). Finite element (FE) simulation was used to determine the effects of the shot peening process parameters. The FE results showed that the surface roughness was increased at higher sandblasting speeds and sand diameters.

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Abstract. This paper investigates the formability effects of variable blank holder force on deep drawing of AISI 304 rectangular cup. Various sets of blank holder forces were set to observe the formability, which was indicated by the percentage of sheet thinning in this study. The results showed that the blank holder forces at the locations of the sheet edges surrounding the cavity areas were considered dominant to reduce sheet thinning and enhance the formability.