Influence of Laser Shock Peening on the Bobbin Tool Friction Stir-Welded AW6060 Alloy.

Friction stir welding (FSW) is a solid-state welding process that uses a rotating tool to soften and stir the base metal, thereby joining it. A special type of tool that has attracted the interest of researchers is the so-called bobbin tool (BTFSW), which, unlike conventional tools with one shoulder, features two shoulders that envelop the base metal from both the top and bottom sides. As a result, significant tensile stresses develop on both sides of the weld, caused by the action of both tool shoulders.

Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles.

This paper presents the development and evaluation of neural network models using a small input-output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques.

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset.

Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters.