A Simplified Phase Display System for 3D Surface Measurement and Abnormal Surface Pattern Detection

Today’s engineering products demand increasingly strict tolerances. The shape of a machined surface plays a critical role to the desired functionality of a product. Even a small error can be the difference between a successful product launch and a major delay. It is important to develop tools that confirm the quality and accuracy of manufactured products. The key to assessing the quality is robust measurement and inspection tools combined with advanced analysis. This research is motivated by the goals of 1) developing an advanced optical metrology system that provides accurate 3D profiles of target objects with curvature and irregular texture and 2) developing algorithms that can recognize and extract meaningful surface features with the consideration of machining process information. A new low cost measurement system with a simple coherent interferometric fringe projection system is developed. Comparing with existing optical measurement systems, the developed system generates fringe patterns on object surface through a pair of fiber optics that have a relatively simple and flexible configuration. Three-dimensional measurements of a variety of surfaces with curvatures demonstrate the applicability and flexibility of the developed system. An improved phase unwrapping algorithm based on a flood fill method is developed to enhance the performance of image processing. The developed algorithm performs phase unwrapping under the guidance of a hybrid quality map that is generated by considering the quality of both acquired original intensity images and the calculated wrapped phase map. Advances in metrology systems enable engineers to obtain a large amount of surface information. A systematic framework for surface shape characterization and abnormal pattern detection is proposed to take the advantage of the availability of high definition surface measurements through advanced metrology systems. The proposed framework evaluates a measured surface in two stages. The first step focuses on the extraction of general shape (e.g., surface form) from measurement for surface functionality evaluation and process monitoring. The second step focuses on the extraction of application specific surface details with the consideration of process information (e.g., surface waviness). Applications of automatic abnormal surface pattern detection have been demonstrated. In summary, this research focuses on two core areas: 1) developing metrology system that is capable of measuring engineered surfaces accurately; 2) proposing a methodology that can extract meaningful information from high definition measurements with consideration of process information and product functionality.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136999/1/xinweng_1.pd

Visual Inspection System To Detect Connector Tilts In PCBAs [TS156. V844 2005 f rb] [Microfiche 7845].

Sistem pemeriksaan visual automatic memainkan peranan penting dalam bahagian tapisan kualiti di industri eletronik. AVI’s are playing important roles in quality inspection in the electronic industry

Visual Inspection System To Detect Connector Tilts In Pcbas

Sistem pemeriksaan visual automatic memainkan peranan penting dalam bahagian tapisan kualiti di industri eletronik. AVI’s are playing important roles in quality inspection in the electronic industry

Research & Technology 2005

This report selectively summarizes NASA Glenn Research Center's research and technology accomplishments for fiscal year 2005. It comprises 126 short articles submitted by the staff scientists and engineers. The report is organized into three major sections: Programs and Projects, Research and Technology, and Engineering and Technical Services. A table of contents and an author index have been developed to assist readers in finding articles of special interest. This report is not intended to be a comprehensive summary of all the research and technology work done over the past fiscal year. Most of the work is reported in Glenn-published technical reports, journal articles, and presentations prepared by Glenn staff and contractors. In addition, university grants have enabled faculty members and graduate students to engage in sponsored research that is reported at technical meetings or in journal articles. For each article in this report, a Glenn contact person has been identified, and where possible, a reference document is listed so that additional information can be easily obtained. The diversity of topics attests to the breadth of research and technology being pursued and to the skill mix of the staff that makes it possible. For more information, visit Glenn's Web site at http://www.nasa.gov/glenn/. This document is available online (http://www.grc.nasa.gov/WWW/RT/). For publicly available reports, visit the Glenn Technical Report Server (http://gltrs.grc.nasa.gov)

A Status of NASA Rotorcraft Research

In 2006, NASA rotorcraft research was refocused to emphasize high-fidelity first-principles predictive tool development and validation. As part of this new emphasis, documenting the status of NASA rotorcraft research and defining the state-of-the-art in rotorcraft predictive capability were undertaken. This report is the result of this two-year effort. Contributors to this work encompass a wide range of expertise covering the technical disciplines of aeromechanics, acoustics, computational fluid dynamics (CFD), flight dynamics and control, experimental capabilities, propulsion, structures and materials, and multi-disciplinary analysis

Face shape analysis in people with epilepsy

Stereophotogrammetry and dense surface modelling are novel techniques that have been used to study face shape in genetic and neurodevelopmental disorders. In people with epilepsy, it has been recognised that the condition may be associated with underlying structural variants or malformations of cortical development in some cases. Here I recruited 869 people with epilepsy or unaffected relatives and control subjects to study face shape. I sought to explore whether face shape and symmetry, using new metrics for each, could help to predict those people with epilepsy who may have potential underlying genetic or structural causes. My reproducibility studies found that stereophotogrammetry and dense surface modelling were susceptible to error from changes in head position or face expression, but not from camera calibration, image acquisition and image landmarking. The next study found that in people with epilepsy, a measurement of atypical face shape, Face Shape Difference (FSD), was significantly increased in those with pathogenic structural variants compared to those without pathogenic structural variants. The FSD value was used to predict the presence of pathogenic structural variants with a sensitivity of 66- 80% and specificity of 65-78%. Body mass index affects face shape in a partly predictable manner. The effect of body mass index differences was controlled for in a further analysis. I then analysed facial asymmetry and showed that it was increased in people with developmental lesions in the brain but not in people with pathogenic structural variants. A final study showed that stereophotogrammetry, dense surface modelling, FSD and reflected FSD could be used to study a single genetic disorder associated with epilepsy, to find previously unrecognised face shape changes. Stereophotogrammetry and dense surface modelling therefore appear to be promising tools to aid both in discovery of underlying causes for epilepsy and in understanding of such causes in terms of facial development

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems