Residual stresses’ magnitude generated by deposition, quenching stress, thermal stress operation temperature, and infiltration in the thermal barrier coating (TBC) of gas turbines was determined. A thermal barrier coating was manufactured by the deposition of two layers, CoNiCrAlY and yttria-stabilized zirconia (YSZ), on an AISI 304 stainless steel substrate. The CoNiCrAlY was deposited by using an HVOF gun and the YSZ by an atmospheric plasma spray (APS). The TBCs were heat-treated at 1250 °C, with a CMAS (CaO, MgO, Al₂O₃, and SiO₂) attack with a concentration of 10 mg/cm² for 6 h in order to evaluate the evolution of the state of residual stresses in the coating at a high temperature. Residual stresses were determined by employing the modified layer removal method for duplex coatings (MLRMDC), ANSYS Version R19.2, and the equations proposed by Noda et al. In the YSZ, the total maximum residual stresses were 139 MPa in compression, and in the CoNiCrAlY, the maximum residual stress was 214 MPa in compression. The factor that has the largest effect on the magnitude of residual stresses was the infiltration of the CMAS in the YSZ.

El término Industria 4.0 se ha convertido en prioridad y objeto de estudio para empresas y centros de investigación pero aún se encuentra dentro de sus primeras etapas de implementación. Además, las compañías enfrentan dificultades al desarrollar soluciones para Industria 4.0, sin estar seguras de cómo afrontar sus requerimientos básicos. El diseño de una arquitectura de referencia aborda explícitamente este problema, apoya a los profesionales en la implementación de soluciones siendo la base del desarrollo y proporciona un soporte ante los desafíos que la Industria 4.0 representa. Por lo tanto, la contribución de este documento se centra en diseñar una arquitectura de referencia para sistemas y aplicaciones en Industria 4.0 basada en computación en la nube y análisis de datos, mostrando su viabilidad a través de la implementación en un caso de uso: Agricultura 4.0. Mediante esta arquitectura, ingenieros e investigadores podrán enfrentar los desafíos actuales de la producción inteligente, así como investigar, desarrollar e implementar soluciones (aplicaciones y sistemas) guiadas, estandarizadas y a costos accesibles, que cumplan los requerimientos que gobiernan Industria 4.0.

Wood–plastic composites (WPCs) are materials that combine a polymeric phase with a filler, typically wood powder or fiber. This common practice significantly enhances certain mechanical properties of WPCs, such as tensile, compressive, and flexural strength, especially when coupling agents are added. Polyethylene terephthalate (PET) is a thermoplastic commonly used due to its high stiffness, mechanical strength, and chemical resistance. However, the increasing demand for PET has led to a rise in plastic waste accumulation, posing an environmental challenge. Therefore, recycling and promoting a circular economy are considered key strategies to reduce environmental impact and achieve sustainable development. The main challenge in WPC production lies in its formulation—specifically, determining the appropriate proportion of thermoplastic polymer, wood filler, and coupling agent. To optimize the mechanical properties of WPCs, it is necessary to vary these component proportions and understand how these changes affect the composite properties. This article describes a statistical approach for optimizing the mechanical properties of a WPC with PET as the polymer matrix. It is assumed that the mechanical properties of the WPC are a function of the proportions of the components that make up the mixture, so the optimization consists of determining these proportions in a way that maximizes the mechanical properties. With the experimental design, three models with inverse terms were fitted, which modeled the relationship between the component proportions and the flexural, compression, and tensile strength. The optimal formulation for maximizing flexural strength was found to be 58% PET, 39% wood, and 3% E‐GMA. The optimal formulation for maximizing compressive strength was found to be 58% PET, 40% wood, and 2% E‐GMA. The optimal formulation for maximizing tensile strength was found to be 86% PET, 11% wood, and 3% E‐GMA. The results are validated through analysis of variance of the mixture experimental design for mixtures. Finally, three response surface plots are presented for each mechanical property, which graphically describe the relationship between the different proportions of the mixture components and the mechanical properties.

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.

In this work, we present the effect of the amount of CMAS infiltration into YSZ of Thermal barrier coatings (TBC) on the magnitude of residual stresses. The (TBC) were deposited by thermal spraying of CoNiCrAlY (Bond Coat-BC) and YSZ (Top Coat-TC) powders. The deposition of the BC was through the high velocity oxygen fuel (HVOF) system. The TC was deposited via an atmospheric plasma-spraying gun (APS). The TBCs were heat treated at 1250 °C, with a CMAS attack at a concentration of 10 mg/cm².The attack exposure was for 2 and 4 hours respectively. In this evaluation, the measured parameter was the magnitude of the residual stress state in Yttria Stabilized Zirconia (YSZ). The residual stress profiles were obtained using the Modified Layer Removal Method for Duplex Coatings (MRCMRB) and the Noda equations. An increase of 26.446 MPa was determined for 2 hours of thermal treatment and 30.743 MPa for 4 hours.