972 resultados para Manufacturing methods


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This paper examines the applicability of a digital manufacturing framework to the implementation of a Value Driven Design (VDD) approach for the development of a stiffened composite panel. It presents a means by which environmental considerations can be integrated with conventional product and process design drivers within a customized, digital environment. A composite forming process is used as an exemplar for the work which creates a collaborative environment for the integration of more traditional design drivers with parameters related to manufacturability as well as more sustainable processes and products. The environmental stakeholder is introduced to the VDD process through a customized product/process/resource (PPR) environment where application specific power consumption and material waste data has been measured and characterised in the process design interface. This allows the manufacturing planner to consider power consumption as a concurrent design driver and the inclusion of energy as a parameter in a VDD approach to the development of efficiently manufactured, sustainable transport systems.

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Aircraft design is a complex, long and iterative process that requires the use of various specialties and optimization tools. However these tools and specialities do not include manufacturing, which is often considered later in the product development process leading to higher cost and time delays. This work focuses on the development of an automated design tool that accounts for manufacture during the design process focusing on early geometry definition which in turn informs assembly planning. To accomplish this task the design process needs to be open to any variation in structural configuration while maintaining the design intent. Redefining design intent as a map which links a set of requirements to a set of functions using a numerical approach enables the design process itself to be considered as a mathematical function. This definition enables the design process to utilise captured design knowledge and translate it into a set of mathematical equations that design the structure. This process is articulated in this paper using the structural design and definition for an aircraft fuselage section as an exemplar.

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Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

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Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

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The invention relates to immunoassays, methods for carrying out immunoassays, immunoassay kits and methods for manufacturing immunoassay kits. In particular, the invention has relevance to capillary (especially microcapillary) immunoassay technology.

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Waste effluents from the forest products industry are sources of lignocellulosic biomass that can be converted to ethanol by yeast after pretreatment. However, the challenge of improving ethanol yields from a mixed pentose and hexose fermentation of a potentially inhibitory hydrolysate still remains. Hardboard manufacturing process wastewater (HPW) was evaluated at a potential feedstream for lignocellulosic ethanol production by native xylose-fermenting yeast. After screening of xylose-fermenting yeasts, Scheffersomyces stipitis CBS 6054 was selected as the ideal organism for conversion of the HPW hydrolysate material. The individual and synergistic effects of inhibitory compounds present in the hydrolysate were evaluated using response surface methodology. It was concluded that organic acids have an additive negative effect on fermentations. Fermentation conditions were also optimized in terms of aeration and pH. Methods for improving productivity and achieving higher ethanol yields were investigated. Adaptation to the conditions present in the hydrolysate through repeated cell sub-culturing was used. The objectives of this present study were to adapt S. stipitis CBS6054 to a dilute-acid pretreated lignocellulosic containing waste stream; compare the physiological, metabolic, and proteomic profiles of the adapted strain to its parent; quantify changes in protein expression/regulation, metabolite abundance, and enzyme activity; and determine the biochemical and molecular mechanism of adaptation. The adapted culture showed improvement in both substrate utilization and ethanol yields compared to the unadapted parent strain. The adapted strain also represented a growth phenotype compared to its unadapted parent based on its physiological and proteomic profiles. Several potential targets that could be responsible for strain improvement were identified. These targets could have implications for metabolic engineering of strains for improved ethanol production from lignocellulosic feedstocks. Although this work focuses specifically on the conversion of HPW to ethanol, the methods developed can be used for any feedstock/product systems that employ a microbial conversion step. The benefit of this research is that the organisms will the optimized for a company's specific system.

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The European foundry business is a traditional less RTD intensive industry which is dominated by SMEs and which forms a significant part of Europe’s manufacturing industry. The efficient design and manufacturing of cast components and corresponding tooling is a crucial success factor for these companies. To achieve this, information and knowledge around the design, planning and manufacturing of cast components needs to be accessible in a fast and structured way.

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Mode of access: Internet.

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In truck manufacturing, the exhaust and air inlet pipes are specialized equipment that requires highly skilled, heavy machinery and small batch production methods. This paper describes a project to develop the computer numerically controlled (CNC) pipe bending process for a truck component manufacturer. The company supplies a huge range of heavy duty truck parts to the domestic market and is a significant supplier in Australia. The company has been using traditional methods of machine assisted manual pipe bending techniques. In a drive of continuous improvement, the company has acquired a pre-owned CNC bending machine capable of bending pipes automatically up to 25 bends. However, due to process mismatch, this machine is only used for single bending operation. The researchers studied the bending system and changed the manufacturing process. Using an example exhaust pipe as the benchmark, a significant drop of manufacturing lead time from 70 minutes to 40 minutes for each pipe was demonstrated. There was also a decrease of material cost due to the multiple bends part in one piece without cutting excessive materials for each single bend like it used to be.