A Robust Method for Drilling Monitoring using Acoustic Emission

Acoustic Emission (AE) is considered an efficient tool for monitoring of machining operations, for both tool condition and working piece integrity. However, the use of AE is more challenging in case of drilling, due to heavy dependence of AE signals to process parameters. Monitoring drilling using AE thus requires robust methods to extract useful information in signals. The paper describes such a method that adapts itself to AE signals obtained during drilling, allowing the automatic set-up of an adaptive threshold to perform AE count rate. Experiments have been conducted that show the robustness of the method and its usefulness in drilling monitoring.International audienceAcoustic Emission (AE) is considered an efficient tool for monitoring of machining operations, for both tool condition and working piece integrity. However, the use of AE is more challenging in case of drilling, due to heavy dependence of AE signals to process parameters. Monitoring drilling using AE thus requires robust methods to extract useful information in signals. The paper describes such a method that adapts itself to AE signals obtained during drilling, allowing the automatic set-up of an adaptive threshold to perform AE count rate. Experiments have been conducted that show the robustness of the method and its usefulness in drilling monitoring

Evidence for enhanced neurobehavioral vulnerability to nicotine during periadolescence in rats

Epidemiological studies indicate that there is an increased likelihood for the development of nicotine addiction when cigarette smoking starts early during adolescence. These observations suggest that adolescence could be a “critical ” ontogenetic period, during which drugs of abuse have distinct effects responsible for the development of dependence later in life. We compared the long-term behavioral and molecular effects of repeated nicotine treatment during either periadolescence or postadolescence in rats. It was found that exposure to nicotine during periadolescence, but not a similar exposure in the postadolescent period, increased the intravenous self-administration of nicotine and the expression of distinct subunits of the ligand-gated acetylcholine receptor in adult animals. Both these changes indicated an increased sensitivity to the addictive properties of nicotine. In conclusion, adolescence seems to be a critical developmental period, characterized by enhanced neurobehavioral vulnerability to nicotine. Key words: nicotine; adolescence; intravenous; self-administration; acetylcholine receptor; PC

Multisensor data fusion and belief functions for robust singularity detection in signals

This paper addresses the problem of robust detection of signal singularity in hostile environments using multisensor data fusion. Measurement uncertainty is usually treated in a probabilistic way, assuming lack of knowledge is totally due to random effects. However, this approach fails when other effects, such as sensor failure, are involved. In order to improve the robustness of singularity detection, an evidence theory based approach is proposed for both modeling (data alignment) and merging (data fusion) information coming from multiple redundant sensors. Whereas the fusion step is done classically, the proposed method for data alignment has been designed to improve singularity detection performances in multisensor cases. Several case studies have been designed to suit real life situations. Results provided by both probabilistic and evidential approaches are compared. Evidential methods show better behavior facing sensors dysfunction and the proposed method takes fully advantage of component redundancy

Conformal Printing of Graphene for Single- and Multilayered Devices onto Arbitrarily Shaped 3D Surfaces

Printing has drawn a lot of attention as a means of low per-unit cost and high throughput patterning of graphene inks for scaled-up thin-form factor device manufacturing. However, traditional printing processes require a flat surface and are incapable of achieving patterning on to 3D objects. Here, we present a conformal printing method to achieve functional graphene-based patterns on to arbitrarily-shaped surfaces. Using experimental design, we formulate a water-insoluble graphene ink with optimum conductivity. We then print single and multi-layered electrically functional structures on to a sacrificial layer using conventional screen printing. The print is then floated on water, allowing the dissolution of the sacrificial layer, while retaining the functional patterns. The single and multilayer patterns can then be directly transferred on to arbitrarily-shaped 3D objects without requiring any post deposition processing. Using this technique, we demonstrate conformal printing of single and multilayer functional devices that include joule heaters, resistive deformation sensors and proximity sensors on hard, flexible and soft substrates, such as glass, latex, thermoplastics, textiles, and even candies and marshmallows. Our simple strategy offers great promises to add new device and sensing functionalities to previously inert 3D surfaces.EPSRC (EP/L016087/1) Graphene Flagshi

Feature Selection for Complex Systems Monitoring: an Application using Data Fusion

Emergence of automated and flexible production means leads to the need of robust monitoring systems. Such systems are aimed at the estimation of the production process state by deriving it as a function of critical variables, called features, that characterize the process condition. The problem of feature selection, which consists, given an original set of features, in finding a subset such the estimation accuracy of the monitoring system is the highest possible, is therefore of major importance for sensor-based monitoring applications. Considering real-world applications, feature selection can be tricky due to imperfection on available data collections: depending on the data acquisition conditions and the monitored process operating conditions, they can be heterogeneous, incomplete, imprecise, contradictory, or erroneous. Classical feature selection techniques lack of solutions to deal with uncertain data coming from different collections. Data fusion provides solutions to process these data collections altogether in order to achieve coherent feature selection, even in difficult cases involving imperfect data. In this work, condition monitoring of the tool in industrial drilling systems will serve as a basis to demonstrate how data fusion techniques can be used to perform feature selection in such difficult cases

Conformal Printing of Graphene for Single- and Multilayered Devices onto Arbitrarily Shaped 3D Surfaces

Printing has drawn a lot of attention as a means of low per-unit cost and high throughput patterning of graphene inks for scaled-up thin-form factor device manufacturing. However, traditional printing processes require a flat surface and are incapable of achieving patterning on to 3D objects. Here, we present a conformal printing method to achieve functional graphene-based patterns on to arbitrarily-shaped surfaces. Using experimental design, we formulate a water-insoluble graphene ink with optimum conductivity. We then print single and multi-layered electrically functional structures on to a sacrificial layer using conventional screen printing. The print is then floated on water, allowing the dissolution of the sacrificial layer, while retaining the functional patterns. The single and multilayer patterns can then be directly transferred on to arbitrarily-shaped 3D objects without requiring any post deposition processing. Using this technique, we demonstrate conformal printing of single and multilayer functional devices that include joule heaters, resistive strain sensors and proximity sensors on hard, flexible and soft substrates, such as glass, latex, thermoplastics, textiles, and even candies and marshmallows. Our simple strategy offers great promises to add new device and sensing functionalities to previously inert 3D surfaces.EPSRC (EP/L016087/1) Graphene Flagshi

A Robust Method for Drilling Monitoring using Acoustic Emission

Acoustic Emission (AE) is considered an efficient tool for monitoring of machining operations, for both tool condition and working piece integrity. However, the use of AE is more challenging in case of drilling, due to heavy dependence of AE signals to process parameters. Monitoring drilling using AE thus requires robust methods to extract useful information in signals. The paper describes such a method that adapts itself to AE signals obtained during drilling, allowing the automatic set-up of an adaptive threshold to perform AE count rate. Experiments have been conducted that show the robustness of the method and its usefulness in drilling monitoring