Universite de technologie de Belfort-Montbeliard

About: Universite de technologie de Belfort-Montbeliard is a education organization based out in Belfort, France. It is known for research contribution in the topics: Coating & Microstructure. The organization has 1087 authors who have published 2593 publications receiving 47819 citations.

Optoelectronic properties of delafossite structure CuCr0.93Mg0.07O2 sputter deposited coatings

Abstract: CuCr0.93Mg0.07O2 thin films with improved optoelectronic properties were deposited by reactive magnetron sputtering on fused quartz substrates. The influence of annealing temperature under vacuum on optoelectronic properties of the films was investigated. The amorphous films annealed under vacuum at temperatures higher than 923 K are single-phased delafossite structure, while impurity phases like CuCr2O4 that affect the optoelectronic properties of the films are detected below 873 K. c-axis orientation is observed for CuCr0.93Mg0.07O2 layers and the annealing temperature window in which the films are single-phased delafossite is much larger with Mg doping (923 K → 1073 K) than that for undoped films (~953 K). The optical and electrical behaviours of the films are enhanced by Mg substitution and their direct band gap energy of about 3.12–3.14 eV is measured. The film possesses the optimum properties after annealing under vacuum at about 1023 K; its average transmittance in the visible region can reach 54.23% while the film's conductivity is about 0.27 S cm−1.

Improvement in the microstructure and property of plasma sprayed metallic, alloy and ceramic coatings by pre-/during-treatment of dry-ice blasting

Abstract: Dry-ice blasting was introduced into plasma spray as substrate's under-cooling system to improve the microstructure and property of coatings. In this study, steel, CoNiCrAlY and Al2O3 coatings were deposited by plasma spray coupled with the pre-/during-treatment of dry-ice blasting. Moreover, the effect of dry-ice blasting as a pre-/during-treatment and only the during-treatment was examined to discover a better way to use dry-ice blasting during thermal spraying. It is revealed that a denser steel or CoNiCrAlY alloy coating with a lower content of oxide can be achieved with the application of dry-ice blasting during the plasma spraying. Such decrease of the oxide content is mainly attributed to the better cooling effect of dry-ice pellets. Interestingly, it is also found that the pre-treatment of dry-ice blasting on the substrate can help to get metallic, alloy and even ceramic coatings with improved adhesions. The adhesive strengths of steel, CoNiCrAlY and Al2O3 coatings were increased by 14%, 5% and 30%, respectively, compared with those of the coatings deposited with conventional air cooling. The cleaning effect of dry-ice blasting is responsible for the increase of the adhesive strength. Based on the present study, dry-ice blasting can be proposed as a pretreatment and cooling system during plasma spray.

Neural Computation to Predict In-Flight Particle Characteristic Dependences From Processing Parameters in the APS Process

Abstract: In-flight particle sensors for thermal spraying are used for real-time monitoring of coating manufacture. However, such tools do not offer facilities to tune the processing parameters when the monitoring reveals fluctuations or instabilities in the thermal jet. To complete the process control, any diagnostic sensors need to be coupled with a predictive system to separate the effect of each processing parameter on the in-flight particle characteristics. In this work, a nonlinear dynamic system based on an artificial neural network (ANN) model is proposed to play this role. It consists of a method that relates the processing parameters to the particle emitted signal characteristics recorded with a DPV2000 (TECNAR Automation, St-Bruno, QC, Canada) optical sensing device. In such a way, a database was built to train and optimize an ANN structure. The in-flight particle average velocity, temperature, and diameter of an alumina-13wt.%titania feedstock were correlated to the injection and power parameters. Correlations are discussed on the basis of these predictive results.

In-flight and upon impact particle characteristics modelling in plasma spray process

Abstract: In Atmospheric plasma spray process, the in-flight particle characteristics such as their particle size, velocity and surface temperature influence significantly their flight duration and consequently their melting degree. The knowledge of the correlations between process parameters and in-flight particle characteristics is very important for optimizing the coating qualities. Artificial neural networks was trained and optimized to establish the relationships linking in-flight particle average diameter and process parameters to in-flight particle average velocity and surface temperature. Then, the established ANN relationships permitted to determine the in-flight particle average velocity and surface temperature versus their diameter for given process parameters. These predicted average velocity and surface temperature data were then used to determine the time for complete melting of the particle and its dwell-time before impact by an analytical model for given operating conditions.