Processing and testing of reinforced PA66 based composites

The new requirements in different sectors, such as aerospace, automotive and construction, for lightweight materials have led to an increase in demand for composite materials suitable for use in high rate production processes, such as plastic injection. This makes it necessary to look for matrices and reinforcements that, in addition to being compatible with each other, are also compatible with the injection process. It is in this area of research where the work presented here arises. To meet the two requirements mentioned above, this study contemplates a battery of composite materials obtained by combining PA66 and fiberglass, in different proportions and configuration, both for the preparation of the matrix and for reinforcement. For the elaboration of the matrix, two options have been evaluated, PA66 and PA66 reinforced at 35% with short glass fibre. To obtain reinforcement, six different options have been evaluated; two conventional fiberglass fabrics (each with different density) and four hybrid fabrics obtained from the previous ones by adding PA66 in different configurations (two over-stitched fabrics and two other fabrics). The different composite materials obtained were validated by means of the corresponding adhesion, peeling and resistance tests.Xunta de Galicia | Ref. 046_IN848D_2020_112386

Tomographic and tension analysis of polypropylene reinforced with carbon fiber fabric by injection molding

The use of thermoplastic materials has had significant growth in recent years. However, with great mechanical requirements, thermoplastics have limitations to their use. To improve these restrictions, these materials are reinforced to obtain better properties. Polypropylene is one of the most versatile polymers and is used in almost all modern industries. Thus, the aim of this study is to create composite materials that offer performance for various industrial fields using carbon fiber fabric reinforcement, which is an inexpensive material widely used by the aerospace, automotive, and marine industries. The samples are produced by the over-injection molding of polypropylene. The investigation is focused on the impact of two critical control parameters in the injection molding process: temperature and pressure. Twelve experiments have therefore been considered, taking into account the combination of three factors: the presence or absence of carbon fiber fabric reinforcement, three levels of temperature (200 °C, 220 °C, and 240 °C), and two injection pressures (5000 kPa and 10,000 kPa). To evaluate the influence of these factors, three analyses were carried out: first, on the samples’ shrinkage using a portable metrology-grade 3D laser scanner; second, on the internal defects using computed tomography (CT); and third, on the mechanical properties with tensile tests. From the results obtained, it is observed that the mold shrinkage fell slightly when PP samples were reinforced with carbon fiber, with both materials (PP and carbon-fiber-reinforced PP) having linear behavior with temperature. It is also noticed that polypropylene behaves as a crystalline material when processed at higher temperatures and pressures. From tests on the mechanical properties, it is concluded that the mean yield strength of PP-CF for injection temperatures of 220 °C and 240 °C represents an increase of 43% compared to the non-reinforced material

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Análisis de desgaste de herramienta y optimización de proceso mecanizado mediante visión computarizada y consumo eléctrico

En un entorno competitivo como el actual las empresas se ven obligadas a reducir costes en la producción para poder competir en precios con países de bajo coste en mano de obra. En los procesos de mecanizado por arranque de viruta las herramientas de corte constituyen una gran parte de los costes asociados, principalmente cuando se trata de mecanizar materiales de alta dureza como el acero AISI H13 utilizado en la fabricación de matrices de forja para componentes de automóvil. Motivados por la necesidad de la industria para reducir costes en la fabricación se propone en este trabajo de investigación un procedimiento de control del desgaste de herramientas de corte para la optimización de su vida útil basado en tres métodos de medición complementarios entre sí. La experimentación se realiza en dos etapas. La primera analiza el desgaste de herramientas ISO APKT durante el mecanizado en condiciones de desbaste de un acero AISI H13 recocido de dureza 21 HRC. La segunda etapa de la experimentación analiza y compara el desgaste sufrido por dos tipos de herramientas ISO APKT de gran radio de punta durante el mecanizado en condiciones de acabado de un acero AISI H13 bonificado de dureza 47 HRC, según se realiza en la industria. Para la medición del desgaste se aplican tres metodologías diferentes. La medición de la herramienta de corte por métodos ópticos, midiendo el desgaste en los flancos de la herramienta y en la cara de desprendimiento sobre las distintas imágenes realizadas, es la primera metodología que se propone. Durante el trabajo de investigación se analiza la viabilidad de esta metodología para la obtención del desgaste de la herramienta y los inconvenientes que puede tener para la implantación dentro de un sistema productivo. Estas mediciones directas se complementan con otros dos métodos indirectos. El primero trata la medición del consumo de potencia de la máquina herramienta durante el mecanizado, introduciendo un analizador de corriente que registra el consumo en línea de la máquina. El aumento en el consumo de potencia asociado al desgaste de la herramienta puede ser detectado y relacionado con el desgaste de la herramienta de corte. Por último, la medición de la topografía superficial es el tercer método analizado para obtener la relación entre la calidad superficial de la pieza mecanizada y de la herramienta de corte con el desgaste que se produce en la herramienta. Finalmente se plantea un procedimiento para el control del desgaste de la herramienta en combinación de los tres métodos analizados.In the present competitive environment, companies have to reduce manufacturing costs to compete in prices with countries having low cost workforce. In the process of machining by chip removal, cutting tools are a great part of global costs, mainly when it is about machining materials with high hardness like AISI H13 steel used in forge diez manufacturing for automotive components. Justified by the necessity of the industry to reduce manufacturing costs, the following thesis proposes an investigation about a process to manage the cutting tools wear to optimise tool life based on three complementary methods of measurement. The experiment is done in two steps. First, analyse the wear of ISO APKT tool during rough machining of an annealed AISI H13 steel with a 21 HRC hardness. Second, analyse and compare the wear of two kinds of ISO APKT tools with a large radius tip during the finish machining of a quenched and tempered AISI H13 steel with a 47 HRC hardness as it’s done in the industry. To measure the wear, we use three different methodologies. Measure of the cutting tool by optical methods, measuring the flank wear and cutting edge retract on rake face with the different pictures made, this is the first proposed method. During the investigation piece, we analyse the viability of this methodology to obtain the tool wear and the drawbacks that can occur for the layout in a productive system. These direct measures are completed with other indirect methods. The first is about measuring power consumption of the machine tool during machining, introducing a current analyser which record the online consumption of the machine. The increase of the power consumption associated to the tool wear can be detected and linked with the cutting tool wear. Lastly, the measure of the surface topography is the third method analysed to obtain the relation between the surface quality of the machined part and the cutting tool with the tool wear. Finally, a procedure is considered to manage the tool wear combined with the three analysed methods

Effect of surface texture on the structural adhesive joining properties of Aluminum 7075 and TEPEX®

In the process of continuous improvement of manufacturing processes, this study was developed within the framework of the Ecovoss project, based on the inclusion of lightweight and new materials parts in the automotive sector. The objective was based on the replacement of aluminum welding operations with the option of adhesive operations with other types of materials such as polyamides or, in this case, a TEPEX® composite material (Dynalite 202-c200/50% TYP 13). The aim of this work is to test the best texturing of substrate made in 7075 aluminum specimens manufactured by robotic polishing with an ABB 6640 robot. Another substrate is TEPEX composite. A structural adhesive film AF-163-2 from the 3M company (St Paul, MN, USA) is used, which must be applied according to the manufacturing procedure. The tests carried out are based on the topographic measurement of the surfaces to be joined with an Alicona focus variation microscope, and the uniaxial shear tests of adhesive samples have been analyzed. The texture of the surface failure has been analyzed, and the results confirm a significant correlation between the texture parameters of initial surfaces and maximum shear stress. The expected results should provide a better understanding of the surfaces aimed to optimize the adhesion of the studied materials

Simple discriminatory methodology for wear analysis of cutting tools: impact on work piece surface morphology in case of differently milled kinetics steel H13

Recently, there is growing interest in optimisation of finishing process thanks to the technologies to follow online the wear of cutting tools. In the present paper, one of the cheapest and simplest non-contact methodologies is described in detail and investigated with robustness evaluation. To simulate the finishing operation of a die, in this study, two cavities were designed in AISI H13 steel. Different inserts corresponding to PVD-(Ti,Al)N coated cemented carbide tool were tested. The described methodology is easy to be applied in manufacturing cutting machining with the opportunity to be implemented on machining processes to follow reasonably wear process of cutting tools.Centre for Industrial Technological Development | Ref. IDI-2015069