Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Extraction of 3D Machined Surface Features and Applications.

In the modern production, the measurement of surface functions becomes more and more important. Most previous work on surface functional characterization are focused on surface tribological properties (roughness domain) and cover only a small area of a large engineering surface. Therefore, characterizing large engineering surface comprehensively and rapidly presents significant challenges. This research is focused on extracting 3D surface features from waviness domain and using these features to predict surface function and detect machining errors. In this research, an improved Gaussian filter is first designed to accurately extract 3D surface waviness from a large surface height map measured by a large field view interferometer. This filter technique enhances the performance of the standard Gaussian filter when applied to a surface which has large form distortion and many sharp peaks/valleys/noise. Following this, a 3D surface waviness feature of the machined workpiece is defined and applied to assess severe tool wear. Secondly, a two-channel filter bank diagram is developed that applies a 2D wavelet to decompose a 3D surface into multiple-scale subsurfaces. 3D surface features extracted from multiple-scale subsurfaces are then used to predict surface functions and detect machining faults. In the proposed surface decomposition process, two important issues: the elimination of border distortion and the transformation between the wavelet scale and its physical dimension are addressed. Applications of 2D wavelet decomposition to 3D surfaces are demonstrated using several automotive case studies, including abrupt tool breakage detection, chatter detection, cylinder head mating/sealing surface leak path detection, and transmission clutch piston surface non-clean up detection. Finally, a novel and automated surface defect detection and classification system for flat machined surfaces is designed. The purpose of this work is to extract microscopic surface anomalies and assign each anomaly to a surface defect type commonly found on the automotive machined surfaces. A “breadboard” version surface defect inspection system using multiple directional illuminations is constructed. Related image processing algorithms are developed to detect and identify 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge).Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78782/1/yiliao_1.pd

Instationary modal Analysis for Impulse-type stimulated structures

In order to determine modal parameters, classical experimental modal analysis can be used in engineering application. This method finds a system frequency response function using fast Fourier Transform (FFT). The Fourier Transform is one type of global data analysis method. The frequency resolution is equal to the reciprocal of the total sample time. So applying the FFT is not suitable for any transient signal to reveal local characteristics. However, in modern manufacturing industries, processing forces are rapidly changing. The dynamic behavior may vary rapidly in a short time due to variations in the machining parameters and changes in boundary conditions. These nonlinear and non-stationary dynamic parameters are not constant during machining operations identification using FFT. In this research, an innovative transient signal analysis approach has been developed, which is based on an application of the least squares estimation. The proposed method provides transient information with high resolution and to identify the time-varying modal parameters during machining. Least squares estimation can be augmented with a sliding-window operation (SWLSE) to reveal the actual system dynamic behavior at any moment. The accuracy of this method depends on the window size, the noise ratio and the sampling rate etc. The estimation accuracy of modal parameters is discussed in this work. To examine the efficiency of the SWLSE method experimental tests are performed on a laboratory beam system and the results are compared with the classical experimental modal analysis (CEMA) method. The laboratory beam system is designed and assembled that the stiffness and damping ratio of the structure can be adjusted. Additionally, the proposed method is applied to the identification of the actual modal parameters of machine tools during machining operations. In another application, the proposed method provides also the process varied damping information in a process monitoring

Ultra-high precision grinding of BK7 glass

With the increase in the application of ultra-precision manufactured parts and the absence of much participation of researchers in ultra-high precision grinding of optical glasses which has a high rate of demand in the industries, it becomes imperative to garner a full understanding of the production of these precision optics using the above-listed technology. Single point inclined axes grinding configuration and Box-Behnken experimental design was developed and applied to the ultra-high precision grinding of BK7 glass. A high sampling acoustic emission monitoring system was implemented to monitor the process. The research tends to monitor the ultra-high precision grinding of BK7 glass using acoustic emission which has proven to be an effective sensing technique to monitor grinding processes. Response surface methodology was adopted to analyze the effect of the interaction between the machining parameters: feed, speed, depth of cut and the generated surface roughness. Furthermore, back propagation Artificial Neural Network was also implemented through careful feature extraction and selection process. The proposed models are aimed at creating a database guide to the ultra-high precision grinding of precision optics

Monitoring of long steel pipes using acoustic emission

This thesis relates to the condition monitoring of long steel pipes using acoustic emission (AE). A number of experiments were carried out on pipes with a range of internal and external environments using a linear axial array of sensors with the ultimate aim of locating and reconstituting the time-domain and frequency-domain signatures of AE sources. The AE waves were generated from simulated, discontinuous, continuous, and semi-continuous sources and from real sources generated by impacts and crack propagation. The simulated source work in different internal and external environments was carried out to develop a generic empirical approach to AE propagation in long steel pipes which acknowledges the distortion of a source disturbance in the time and frequency domains. Generally, the acquired signals have two identifiable components and methods are developed for separating these components automatically and determining their group velocities. A simple model for attenuation is also developed which includes effects brought about by burial of the pipe and /or the nature of the fluid transported (liquid or gas). In the impact and crack extension tests a variety of intensities were simulated and the effect of type and intensity on time- and frequency-domain characteristics of the source was determined. The overall outcome is the demonstration of the potential of AE for identifying the nature, intensity and location of damaging events, such as crack growth and denting, and for the location and intensity assessment of leaks

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

Copyright: © 2021 by the authors. The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies