Currently, in the manufacturing industry, there are many different component manufacturing processes. Each of them has its own advantages and disadvantages. One phenomenon present in some processes is known as warpage. Essentially, warpage is unwanted deformation in a component caused by internal stresses, thermal variations, uneven cooling of the part, the presence of moisture or the release of residual stresses in the material. Although it is a common problem in some engineering fields, it occurs mainly in processes such as injection moulding, compression moulding and 3D printing. There are different measurement and mitigation techniques that are adapted to each process. This paper discusses the causes of the phenomenon and current and future mitigation techniques. Additionally, an overview of upcoming techniques aimed at fully mitigating and measuring this phenomenon in real time during manufacturing processes is presented.

The transformation of powertrains, powered by internal combustion engines, into electrical systems generates new challenges in developing lightweight materials because electric vehicles are typically heavy. It is therefore important to develop new vehicles and seek more aesthetic and environmentally friendly designs whilst integrating manufacturing processes that contribute to reducing the carbon footprint. At the same time, this research explores the development of new prototypes and custom components using printed composite materials. In this framework, it is essential to formulate new approaches to estimate fatigue life, specifically for components tailored and fabricated with these kinds of advanced materials. This study introduces a novel fatigue life prediction approach based on an artificial neural network. When presented with given inputs, this neural network is trained to predict the accumulation of fatigue damage and the temperature generated during cyclic loading, along with the mechanical properties of the compound. Its validation involves comparing the network’s response with the load ratio result, which can be calculated using the fatigue damage parameter. Comparing both results, the network can successfully predict the fatigue damage accumulation; this implies an ability to directly employ data on the mechanical behavior of the component, eliminating the necessity for experimental testing. Then, the current study introduces a neural network designed to predict the accumulated fatigue damage in printed composite materials with an Onyx matrix and Kevlar reinforcement.

Harbor and coastal structures are essential in maritime connections. Additionally, some offshore structures near the coast are important for supplying energy as a material or transforming the natural resources into energy, as in wind turbines. One of the main issues that needs to be overcome in terms of these structures is mechanical fatigue due to the loads of the structure by its function and waves, wind, the current seawater level, and ice. Structural design has to meet high target loads to ensure that structures can endure extreme marine conditions under the assumption of the probability of loads and based on marine conditions, can mask damage where component immersions are not available for inspection. In this work, the wave loads and fatigue damage under random processes are reviewed.