In-process tool condition monitoring systems in CNC turning operations

The present study shows the development of in-process tool condition monitoring systems utilizing signal decomposition technique, statistical data analysis, and artificial neural networks system. Two systems; (1) the system based on the multiple regression, (2) the system based on artificial neural networks with back-propagation learning algorithms were developed;The raw signals obtained from two sensors (tri-axial accelerometer and AE sensor) with different machining parameters and tool conditions were examined and decomposed into six components by utilizing a wavelet transformation. The most significant components of each signal were found by statistical method and implemented to develop two in-process tool monitoring systems;Before the multiple regression system was developed, a statistical process was performed to eliminate the effects of machining parameters from the signals of the accelerometer and AE sensor. The prediction performance improved 12.6% from the process;In order to maximize the benefit of artificial neural networks system in tool monitoring systems, a novel approach was performed in this study. A great number of networks structures were tested systemically to find an optimized structure for the artificial networks tool condition monitoring system. The technique provided benefits of not only saving time but also testing all possible structures more accurately compared with the traditional manual trial-and-error methodology;The developed statistical multiple regression tool condition monitoring system showed 90% accuracy, and the developed artificial neural networks tool condition monitoring system showed 97% accuracy from 151 tests with the reject flank wear size of 0.00787 inch (0.2 mm) or larger;The successful development of the tool condition monitoring systems can provide a practical tool to reduce downtime related with tool changes and minimize the amount of scrap in metal cutting industry. Implications of the study and recommendations for further research were provided

The design of an effective sensor fusion model for condition monitoring systems of turning processes

High energy price and the increasing requirements of quality and low cost of products have created an urgent need to implement new technologies in current automated manufacturing environments. Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the essential technologies that provide the competitive advantage in many manufacturing environments. This research aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Multi-sensors combined with a novelty detection algorithm and Learning Vector Quantisation (LVQ) neural networks are used in this research to detect tool wear and provide diagnostic and prognostic information. A novel approach, termed ASPST, (Automated Sensor and Signal Processing Selection System for Turning) is used to select the most appropriate sensors and signal processing methods. The aim is to reduce the number of sensors needed in the overall system and reduce the cost. The ASPST approach is based on simplifying complex sensory signals into a group of Sensory Characteristic Features (SCFs) and evaluating the sensitivity of these SCFs in detecting tool wear. A wide range of sensory signals (cutting forces, strain, acceleration, acoustic emission and sound) and signal processing methods are also implemented to verify the capability of the approach. A cost reduction method is also implemented based on eliminating the least utilised sensor in an attempt to reduce the overall cost of the system without sacrificing the capability of the condition monitoring system. The experimental results prove that the suggested approach provides a responsive and effective solution in monitoring tool wear in turning with reduced time and cost

Additive Manufacturing Research and Applications

This Special Issue book covers a wide scope in the research field of 3D-printing, including: the use of 3D printing in system design; AM with binding jetting; powder manufacturing technologies in 3D printing; fatigue performance of additively manufactured metals, such as the Ti-6Al-4V alloy; 3D-printing methods with metallic powder and a laser-based 3D printer; 3D-printed custom-made implants; laser-directed energy deposition (LDED) process of TiC-TMC coatings; Wire Arc Additive Manufacturing; cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing; the influence of material properties and characteristics in laser powder bed fusion; Design For Additive Manufacturing (DFAM); porosity evaluation of additively manufactured parts; fabrication of coatings by laser additive manufacturing; laser powder bed fusion additive manufacturing; plasma metal deposition (PMD); as-metal-arc (GMA) additive manufacturing process; and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Polymer Processing and Surfaces

This book focuses on fundamental and applied research on polymer processing and its effect on the final surface as the optimization of polymer surface properties results in the unique applicability of these over other materials. The development and testing of the next generation of polymeric and composite materials is of particular interest. Special attention is given to polymer surface modification, external stimuli-responsive surfaces, coatings, adhesion, polymer and composites fatigue analysis, evaluation of the surface quality and microhardness, processing parameter optimization, characterization techniques, among others

SPATIAL TRANSFORMATION PATTERN DUE TO COMMERCIAL ACTIVITY IN KAMPONG HOUSE

ABSTRACT Kampung houses are houses in kampung area of the city. Kampung House oftenly transformed into others use as urban dynamics. One of the transfomation is related to the commercial activities addition by the house owner. It make house with full private space become into mixused house with more public spaces or completely changed into full public commercial building. This study investigate the spatial transformation pattern of the kampung houses due to their commercial activities addition. Site observations, interviews and questionnaires were performed to study the spatial transformation. This study found that in kampung houses, the spatial transformation pattern was depend on type of commercial activities and owner perceptions, and there are several steps of the spatial transformation related the commercial activity addition. Keywords: spatial transformation pattern; commercial activity; owner perception, kampung house; adaptabilit

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Recent Advances in Metal, Ceramic, and Metal-Ceramic Composite Films/Coatings

This reprint gathers works on various coating materials and technologies aimed at the improvement of materials’ properties, such as corrosion resistance or biocompatibility. Systematic studies demonstrate how the structure and morphology of coatings can change the mechanical, chemical and various functional properties of materials. The reprint contributes to the better understanding of various phenomena induced by metal, ceramic or composite coatings in core materials and, thus, it can help in the more rational design of the selected material’s properties in future studies by the application of coatings