Fatigue life tests of different plate thicknesses and penetration degrees were carried out for load-bearing cross-shaped welded joints. The fatigue results show a significant "thickness effect". The fatigue limit of the 25 mm-thick fatigue specimen is 13.4% lower than that of the 16mm-thick fatigue specimen; the fatigue life of the weld toe has basically no difference with the change of penetration degree. Plate thickness is a key factor affecting fatigue life, and the thickness effect is jointly determined by the stress gradient along the plate thickness and the degree of surface stress concentration. The correction result based on the effective notch stress method is more accurate than the thickness correction formula in the ABS standard. The thickness effect produced by the load-bearing cross-shaped welded joint is evaluated by the thickness correction formula too low, which will bring a certain risk. The penetration ratio a/t affects the fatigue failure position of the load-bearing cross-shaped welded joint. The fatigue design of the load-bearing cross-shaped welded joint can be carried out by appropriately increasing the degree of penetration while satisfying the failure at the weld toe.
The main goal of this paper is to present interesting results on seismic wave propagation in a system comprised by two semi spaces and a flat interface, using a numerical approach. The first semi space is an elastic solid that has inside a fluid inclusion, in which is completely saturated only by the fluids; water, gas or oil, the air is considered for the other semi space. The Boundary Element Method (BEM) is used to develop this analysis, where results are presented in time and frequency domain. Several aspects related to displacement amplification that emerge in this kind of system are discussed. Beyond of mechanical properties of the soil, the behavior and amplitude of displacements are linked to the fluid properties immersed in the inclusion. The P wave in the fluid inclusion show that a greater wave interaction is generated for the inclusion that is saturated by gas.
Conventional chip-removal processes remain indispensable in modern manufacturing, delivering high-quality, fast, and economical machining. To further improve efficiency, these processes must be instrumented and integrated into the Industry 4.0 framework through systematic data collection and analytics. A central question in machining is the usable duration of a cutting tool. Artificial Intelligence-based predictive models can provide this information on demand to support real-time decision-making. This chapter presents a comprehensive review of Artificial Intelligence-based (AI) predictive modeling techniques for tool life and remaining useful life (RUL) estimation. It begins by exploring traditional empirical and physics-based models, emphasising their limitations in dynamic and data-rich manufacturing environments. The review then delves into machine learning (ML) and deep learning approaches, including artificial neural networks, convolutional neural networks, long short-term memory (LSTM), support vector machines, and Random Forests, demonstrating their effectiveness in modeling nonlinear degradation patterns and enabling real-time decision-making. Hybrid and optimisation-based models, such as adaptive neuro-fuzzy inference system, genetic algorithm, particle swarm optimisation, and surrogate modeling, are discussed for their role in enhancing prediction accuracy and adaptability, especially in machining of hardened steels and superalloys. The integration of AI with Industry 4.0 technologies (digital twins, cloud AI, CPS, and Internet-of-Things) is also discussed. Additionally, we present an AI-based predictive model for cutting tool life, developed from published experimental datasets. The review underscores the transformative potential of AI in predictive maintenance and tool life management, while identifying key research gaps and opportunities toward sustainable, intelligent manufacturing.
The application of the porous gas journal bearing in industrial environments requires crossing certain barriers, such as supporting higher loads and supply pressures in the lubricating gas film. Given the relatively low elastic modulus of the porous medium, it is necessary to consider the deformation of the porous medium. The fluid-structure interaction is solved in this work through a coupled solution of the finite difference method (FDM) (to solve the lubricant fluid film) and analytical (to solve the porous media deformation) to predict the dynamic behavior of the porous gas journal bearing (PGJB). The work analyzes the effect of frequency excitation, supply pressure, thickness, length, and permeability coefficient on the stiffness coefficients and damping coefficients and their impact on the stability of the porous gas journal bearing.
