Dynamic property characterization of ionic polymer metal composite (Ipmc)

In this thesis dynamic properties of Ionic Polymer Metal Composite (IPMC) is studied. The ionic polymer (IPMC) is made out of a high polymer gel film whose surface is plated with platinum. This ionic polymer finds its application in future as artificial muscle. Analytical modeling method for both single and segmented ionic polymer which can exhibit varying curvature along the polymer was introduced. This segmented ionic polymer can generate more flexible propulsion compared with a single strip ionic polymer where only forward propulsion can be generated by a simple oscillatory bending motion. It is well known in biomimetic system research that a simple bending motion has lower efficiency than a snake-like wavy motion in propulsion. In this segmented ionic polymer each segment can be bent individually. This segmented ionic polymer design consists of a number of independent electrode sections along the length of the ionic polymer to realize the undulatory motion by selectively activating each segment. The magnitude of curvature can be controlled by adjusting the voltage level applied across each segment. In this thesis we focus on the development of an analytical model to predict the deflection of this single and segmented ionic polymers and the model is validated with experimental results. Due to the complexity of the polymer, it is necessary to find the dynamic parameters from the experimental data. After proper tuning of dynamic model, this can be used for various control applications including the underwater robotic propulsor device design and others. The dynamic model developed in this work reasonable complies with experimental data and can be further developed for future control algorithm design

In-Situ Process Monitoring for Metal Additive Manufacturing (AM) Through Acoustic Technique

Additive Manufacturing (AM) is currently a widely used technology in different industries such as aerospace, medical, and consumer products. Previously it was mainly used for prototyping of the products, but now it is equally valuable for commercial product manufacturing. More profound understanding is still needed to track and identify defects during the AM process to ensure higher quality products with less material waste. Nondestructive testing becomes an essential form of testing for AM parts, where AE is one of the most used methods for in situ process monitoring. The Acoustic Emission (AE) approach has gained a reputation in nondestructive testing (NDT) as one of the most influential and proven techniques in numerous engineering fields. Material testing through Acoustic Emission (AE) has become one of the most popular techniques in AM because of its capability to detect defects and anomalies and monitor the progress of flaws. Various AE technique approaches have been under investigation for in-situ monitoring of AM products. The preliminary results from AE exploration show promising results which need further investigation on data analysis and signal processing. AE monitoring technique allows finding the defects during the fabrication process, so that failure of the AM can be prevented, or the process condition can be finely tuned to avoid significant damages or waste of materials. In this work, recorded AE data over the Direct Energy Deposition (DED) additive manufacturing process was analyzed by the Machine Learning (ML) algorithm to classify different build conditions. The feature extraction method is used to obtain the required data for further processing. Wavelet transformation of signals has been used to acquire the time-frequency spectrum of the AE signals for different process conditions, and image processing by Convolutional Neural Network (CNN) is used to identify the transformed spectrum of different build conditions. The identifiers in AE signals are correlated to the part quality by statistical methods. The results show a promising approach for quality evaluation and process monitoring in AM. In this work, the assessment of deposition properties at different process conditions is also done by optical microscope, Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and nanoindentation technique

A contemporary reinterpretation of Jorn Utzon’s material and technological innovations

Standardisation and the use of repetitive production processes was a key determinant of achievable forms in the industrial paradigm, impressively displayed in the Sydney Opera House. Today, complex non-repetitive, yet readily achievable, forms can be designed using computational design techniques that explicitly embedstructural, and fabrication logic from the beginning and which later enable the direct generation of instruction code for their accurate and efficient production viaComputer Numerically Controlled (CNC) machines.This paper builds on the thesis that Utzon's approach - the consideration of technology as an integral part of the design process - can give rise to novel structures which take advantage of the new technological situation. Furthering earlier research by the authors, a method for constructing a hybrid grid shellstructure combining timber and pre-cast concrete elements is proposed. The method is tested through the realisation of a pavilion in relation to the UtzonSymposium in Sydney in March 2014. The previous research shows how a grid shell structure of discrete concrete components can be produced with lasercut amorphous polyethylene terephthalate (PET) templates. The casting method minimises the material use for templates in relation to customisation. This paperconcerns realisation of a hybrid construction through integration of customised plywood components. Furthermore, the concrete construction is improved.Precision is improved through stabilisation of the template and modification of the geometry. Stability of the construction is achieved through new joint solutions. Reinforcement and joints are treated as a single element, thereby simplifying both production and assembly. In previous case studies scaffolding was amajor task, and this aspect is minimised with the method described here

Analysis of effective mechanical properties of thin films used in microelectromechanical systems

This research aims at analyzing the effective mechanical properties of thin film materials that are used in MEMS. Using the effective mechanical properties, reliable simulations of new or slightly altered designs can be performed successfully. The main reason for investigating effective material properties of MEMS devices is that the existing techniques can not provide consistent prediction of the mechanical properties without time-consuming and costly physical prototyping if the device or the fabrication recipe is slightly altered. To achieve this goal, two approaches were investigated: soft computing and analytical. In the soft computing approach, the effective material properties are empirically modeled and estimated based on experimental data and the relationships between the parameters affecting the mechanical properties of devices are discovered. In this approach, 2D-search, Micro Genetic Algorithms, Neural networks, and Radial Basis Functions Networks were explored for the search of the effective material properties of the thin films with the help of a Finite Element Analysis (FEA) and modeling the mechanical behavior such that the effective material properties can be estimated for a new device. In the analytical approach, the physical behavior of the thin films is modeled analytically using standard elastic theories such as Stoney’s formulae. As a case study, bilayer cantilevers of various dimensions were fabricated for extracting the effective Young’s modulus of thin film materials: Aluminum, TetraEthylOrthoSilicate (TEOS)-based SiO2, and Polyimide. In addition, a Matlab® graphical user interface (GUI), STEAM, is developed which interfaces with Ansys®. In STEAM, a fuzzy confidence factor is also developed to validate the reliability of the estimates based on factors such as facility and recipe-dependent variables. The results obtained from both approaches generated comparable effective material properties which are in accord with the experimental measurements. The results show that effective material properties of thin films can be estimated so that reliable MEMS devices can be designed without timely and costly physical prototyping

From Sensors to Knowledge: The Challenge of Training the Next Generation of Data Analysts

With the advent of commercial-off-the-shelf sensors for use in a variety of applications, integration with analytical software tools, and expansion of available archived datasets, there is a critical need to address the problem of transforming resultant data into comprehensible, actionable information for decision-makers through rigorous analysis. In previous research the participating authors have emphasized that users are often faced with the situation in which they are “drowning in a sea of data” but still “thirsting for knowledge”. The availability of analysis software, tools, and techniques provide opportunities for information collection of ever increasing complexity, but the need for the training of analysts to employ appropriate tools and processes to ensure accurate and applicable results has not been addressed. The purpose of this paper is to discuss the challenges and opportunities facing the training of effective analysts capable of handling a wide-range of data types in this era of dynamic tools and techniques

Rapid prototyping in early stages of architectural design

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1997.Includes bibliographical references (leaves 59).This thesis shows how architects can use Rapid Prototyping and what the advantages and disadvantages are in different manipulations of the tool. Chapter two attempts to chart a road map of the rapid prototyping media. The data were drawn from a number of first hand experiments conducted by the author as well as by colleagues in MIT School of Architecture and Harvard Graduate School of Design, and in actual practice. The whole research lies on the boundary between virtual and real, on physical prototyping from a digital file. Digital prototyping and manual prototyping are mentioned only as references. The research offers examples of manipulations of the media and conclude that rapid prototyping in preliminary stages of design is most appropriate when used in what is defined as Direct CAD (Computer Aided Design) with Direct CAM (Computer Aided Manufacturing). Furthermore, it identifies Semi -Direct CAD with Direct CAM as the manipulation most commonly used by architects. This manipulation is useful for presentation models but not very useful in early stages where ideas are less definite. This is the reason why rapid prototyping is generally considered inappropriate for early stages of architectural design. Instead of analyzing Rapid Prototyping technology this work concentrates on the process that involves Rapid Prototyping in new ways in design . It aims to stimulate the designer's imagination when thinking about three -dimensional design, design in motion and design at the interface between people and architecture, for example, chairs and kitchens. In this context Rapid Prototyping becomes merely a vehicle by which the architect explores the design process. Rapid Prototyping is proposed as a media to escape the limitation imposed by flat screen representation in what is defined as true three dimensional digital design. This technology was invented in engineering to increase design and manufacturing process performances.by Alvise Simondetti.M.S

Investigation of Laser Clad Bead Geometry to Process Parameter Settings for Effective Parameter Selection, Simulation, and Optimization

Laser cladding is an additive manufacturing technique involving deposition of powdered clad metal in successive 2D layers onto a substrate thereby creating surface coatings with enhanced material properties. Process and shape parameters contribute in defining the geometry of the clad bead; however, due to the highly coupled nature of the process, it is difficult to determine the relationship between parameters. This research predicts such parameters through development of a cognitive artificial intelligence system using artificial neural networks. A robust experimentation design process applying response surface methodology technique is adopted to collect the bead geometry data for various process configurations. Furthermore, the research identifies the extent of contribution of each factor and the impact of their interactions on the model output through ANOVA and sensitivity analysis. Lastly, a K-mean clustering algorithm is incorporated to identify optimal number of clusters present in the collected dataset on the basis of bead shape characteristics