TEXTILE TECHNOLOGIES IN CONCRETE ENVIRONMENTS."

Girli Concrete is a cross disciplinary funded research project based in the University of Ulster involving a textile designer/ researcher, an architect/ academic and a concrete manufacturing firm.
Girli Concrete brings together concrete and textile technologies, testing ideas of
concrete as textile and textile as structure. It challenges the perception of textiles as only the ‘dressing’ to structure and instead integrates textile technologies into the products of building products. Girli Concrete uses ‘low tech’ methods of wet and dry concrete casting in combination with ‘high tech’ textile methods using laser cutting, etching, flocking and digital printing. Whilst we have been inspired by recent print and imprint techniques in architectural cladding, Girli Concrete is generated within the depth of the concrete’s cement paste “skin”, bringing the trades and crafts of both industries together with innovative results.
Architecture and Textiles have an odd, somewhat unresolved relationship. Confined to a subservient role in architecture, textiles exist chiefly within the categories of soft furnishings and interior design. Girli Concrete aims to mainstream tactility in the production of built environment products, raising the human and environmental interface to the same specification level as the technical. This paper will chart:
The background and wider theoretical concerns to the project.
The development of Girli Concrete, highlighting the areas where craft becomes
art and art becomes science in the combination of textile and concrete
technologies.
The challenges of identifying funding to support such combination technologies,
working methods and philosophies.
The challenges of generating and sustaining practice within an academic
research environment
The outcomes to date

The prediction of process-induced deformation in a thermoplastic composite in support of manufacturing simulation

Digital manufacturing techniques can simulate complex assembly sequences using computer-aided design-based, as-designed' part forms, and their utility has been proven across several manufacturing sectors including the ship building, automotive and aerospace industries. However, the reality of working with actual parts and composite components, in particular, is that geometric variability arising from part forming or processing conditions can cause problems during assembly as the as-manufactured' form differs from the geometry used for any simulated build validation. In this work, a simulation strategy is presented for the study of the process-induced deformation behaviour of a 90 degrees, V-shaped angle. Test samples were thermoformed using pre-consolidated carbon fibre-reinforced polyphenylene sulphide, and the processing conditions were re-created in a virtual environment using the finite element method to determine finished component angles. A procedure was then developed for transferring predicted part forms from the finite element outputs to a digital manufacturing platform for the purpose of virtual assembly validation using more realistic part geometry. Ultimately, the outcomes from this work can be used to inform process condition choices, material configuration and tool design, so that the dimensional gap between as-designed' and as-manufactured' part forms can be reduced in the virtual environment.

The Development of an Immersive Virtual Reality Environment for Manufacturing and Improving Organizational Learning

This paper examines the applicability of an immersive virtual reality (VR) system to the process of organizational learning in a manufacturing context. The work focuses on the extent to which realism has to be represented in a simulated product build scenario in order to give the user an effective learning experience for an assembly task. Current technologies allow the visualization and manipulation of objects in VR systems but physical behaviors such as contact between objects and the effects of gravity are not commonly represented in off the shelf simulation solutions and the computational power required to facilitate these functions remains a challenge. This work demonstrates how physical behaviors can be coded and represented through the development of more effective mechanisms for the computer aided design (CAD) and VR interface.

Automated Methods for Aircraft Design Integrated with Manufacturing

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.

Prediction of integral type failures in semiconductor manufacturing through classification methods

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.

Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

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.

Microelectronic manufacturing of a microsensor based on polyaniline for ammonia gas sensing

The fabrication and operation of an ammonia chemoresistor is described. The sensor responds to changes in the resistance (impedance) of a thin layer of conductive polymer is due to changes in ammonia concentration. The polyaniline film was deposited by electroless plating (dipping) method on interdigitated array made by photolithographic technique. The PANI film was characterized by UV/VIS and IR Spectroscopy and respectively, Atomic Force Microscopy. Impedance Spectroscopy was used for sensor characterization