Multilevel on-line surface roughness recognition system in end milling operation

The use of computer numerically controlled (CNC) machines has become more widespread and as more machining centers are operating unattended, the need for a Smart CNC machine for on-line tool and process monitoring has become critical. An accurate and reliable method of providing real-time information is vital to the continued integration of adaptive control systems (ACS) with machine tools. ACSs are being developed to monitor parameters like tool wear through current sensing, tool breakage from cutting force signals, and tool chatter from vibration signals. These adaptive control systems\u27 capabilities can be broadened to monitor and control various surface quality parameters. For this to happen, a method to provide accurate on-line information about the machined surface is needed;A multi-level on-line fuzzy net controller and multiple regression model was designed to recognize surface roughness in vertical end-milling process. Both models integrate machining parameters of (1) feed speed, (2) depth of cut, (3) tool type, (4) tool material, (5) work material, (6) spindle speed, (7) vibration, and (8) tool diameter. The fuzzy net controller is composed of eight different fizzy designs each having a fuzzifier, rule base, inference engine, and defuzzifier. Individual designs are referenced to perform surface recognition according to the parameter settings for tool diameter, work material, and tool type;The recognition efficiency of the fuzzy net model and a multiple regression model of same configuration are compared with actual Ra readings taken by a profilometer. This multi-level on-line fuzzy net model displayed a recognition accuracy of 90% as compared to an accuracy of 82% for the multiple regression model

STILL RECORDING AFRICAN MUSIC IN THE FIELD

Field sound recordings are an indispensable source of data for ethnomusicologists. However, to my knowledge there are no standards or guidelines of how this data should be captured and managed. With the progress made in machine learning, it has become vital to record data in a way that also supports the retrieval of information about the music. This article describes a model developed for field recordings that aims to aid an objective data gathering process. This model, developed through an action research process that spanned multiple field recording sessions from 2009–2015, include recording equipment, production processes, the gathering of metadata as well as intellectual property rights. The core principles identified in this research are that field recording systems should be designed to provide accurate feedback as a means of quality control and should capture and manage metadata without relying on secondary tools. The major findings are presented in the form of a checklist that can serve as a point of departure for ethnomusicologists making field recordings

Automated Quality Control for In-Situ Water Temperature Sensors

The identification of data not representative of the target subject for outdoor (in-situ) environmental sensors (bad data) is a topic that has been explored in the past. Many tools (such as data filters and computer models) have succeeded in providing an end user with properly identified incorrect data over 95% of the time. However, with the continuous increase in the use of automated data collection, a simple indication of the bad data may no longer provide the end user with enough information to reduce the amount of time that must be spent for manual quality control. The purpose of this research was to devise and test a data classification technique capable of determining when and why water quality data are incorrect in an environment that experiences seasonal and daily fluctuations. This should reduce or eliminate the need for manual quality control (QC) in a large-volume data system where the range of good data is wide and changes often. The objectives this project sought to achieve were; training a learning machine that could identify local maximum and minimum values as well as dulled signals, and forming a multi-class classifier that accurately placed sensor temperature data into three categories; good, bad (because of exposure of the temperature probe to ambient air temperature), and bad (because the sensor has become buried in sediment). This involved the development of a model using a Multi-Class Relevance Vector Machine (MCRVM), and identification of its parameters that would provide at least 90% removal of false negatives for Classes 2 and 3 (the bad data) using only 100 data points from each class for purposes of training the learning machine. These objectives were met using the following methods: (1) QC completion on water temperature sensors manually, (2) an iterative process that involved the selection of inputs for the model and then the optimization of these values based on the RVMs performance, and (3) evaluation of the best performing machines testing a small group of data and then a full year

Optimisation of tool life through novel data acquisition and decision making techniques

Variations in the operation and management of machine tool cutting processes will cause deviations in the quality of the manufactured parts. Current process management approaches combat these variations using combinations of pre- and/or post- process operator centred actions. The experience of the Author, and indications from involved industry partners, indicates that the associated ”conservative” approaches to tool life management is costing between five and ten percent of the money spent on cutting tools, here amounting to two million pounds per annum. The additional cost of quality arising from process related variations cannot be accurately assessed. This research enables the real time assessment of CNC milling cutting processes and the management of process variations. Innovative systems, programs and algorithms are developed through the course of this research project for the on-line monitoring of cutting tool health. These innovations include: the development of a cross-section area model to indicate variable metal removal in milling processes, the conversion of limited load data into process energy consumption, the engineering of an embedded tool wear data acquisition program, the application of an offline cubic change-point detection algorithm to quantitatively identify changes in cutting tool wear behaviour, the implementation of the density evaluation and separation algorithm to enable the separation of cutting and non-cutting process control signals, the development of novel Dispersion Plots, and the development of novel 3D process plots for illustrating instantaneous cutting tool condition. In support of these innovations specially defined methods of signal analysis are deployed to acquire information for the assessment of enabled and complex health features. The approach is autonomous and based upon learning from the acquisition and analysis of information directly from the machine controller. This approach limits the impact on the operation and availability of the machine tool and mitigates any further impact on the capacity of the machine tools in question. Decision making is enabled within the deployed diagnostic techniques. This provides the opportunity for plant-wide tool condition status monitoring and data visualisation. The deployed approach enables researchers to engineer machine systems that can provide more accurate, reliable and repeatable machine operations, with less waste and better managed processes. It is shown that there is significant value in the process control data that was acquired throughout this study. The data is used to show the deployed cutting tool condition based on current and imminent machining requirements. It is also deployed to estimate the expected end of useful life for specific cutting tools and to generate innovative models of the cutting process. These models will enable Engineers to improve the cutting processes and to optimise the assessment of cutting tool condition and life

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

CAD/CAM, CNC TECHNOLOGY APPLIED IN THE FIELD OF ENGINEERING, SECURITY TECHNOLOGY AND MECHANICAL ENGINEER TRAINING I.

In the last decades the spectacular results of each developmental stages of computer-aided design, were considered as great magic of computer use. Professionals were shocked by the impressive building of engineer works and their more and more realistic appearance. It was hard to believe and for many people it still is that this technology becomes indispensable in everyday engineering work. By now, in front-rank product development, it is impossible to do a competitive designer work without applying the most up-to- date design technology. This all leads to the fact that an engineer student of our days, in his design practice, is definitely going to work with the momentarily most up-to-date technology, which will be out-of-date in a couple of years. | A számítógépek alkalmazásának nagy varázslatai közé számított az elmúlt évtizedekben a számítógépen végzett tervezés egy-egy fejlıdési szakaszának látványos eredménye. Szakembereket is meghökkentett a mérnöki alkotások látványos építése és mind valósághőbb megjelenítése. Nehezen hitték, sıt sokan ma is nehezen hiszik azt, hogy a mérnöki munka mindennapjaiban is nélkülözhetetlenné válik ez a technika. Mára az élvonalbeli termékfejlesztésben a mindenkori legjobb tervezési technika igénybevétele nélkül képtelenség versenyképes tervezımunkát végezni. Ennek következtében napjaink mérnökhallgatója tervezıi gyakorlatában minden bizonnyal a ma legkorszerőbbnek számító, de néhány év alatt elavuló módszert leváltó technikával fog dolgozni. Keywords/kulcsszavak: computer aided design, CAD1/CAM2, CNC3 ~ számítógépes tervezés, CAD/CAM, CN

Realising the open virtual commissioning of modular automation systems

To address the challenges in the automotive industry posed by the need to rapidly manufacture more product variants, and the resultant need for more adaptable production systems, radical changes are now required in the way in which such systems are developed and implemented. In this context, two enabling approaches for achieving more agile manufacturing, namely modular automation systems and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at Loughborough University which aims to provide a modular approach to automation systems design coupled with a virtual engineering toolset for the (re)configuration of such manufacturing automation systems is reported. The problems faced in the virtual commissioning of modular automation systems are outlined. AutomationML - an emerging neutral data format which has potential to address integration problems is discussed. The paper proposes and illustrates a collaborative framework in which AutomationML is adopted for the data exchange and data representation of related models to enable efficient open virtual prototype construction and virtual commissioning of modular automation systems. A case study is provided to show how to create the data model based on AutomationML for describing a modular automation system

A virtual environment to support the distributed design of large made-to-order products

An overview of a virtual design environment (virtual platform) developed as part of the European Commission funded VRShips-ROPAX (VRS) project is presented. The main objectives for the development of the virtual platform are described, followed by the discussion of the techniques chosen to address the objectives, and finally a description of a use-case for the platform. Whilst the focus of the VRS virtual platform was to facilitate the design of ROPAX (roll-on passengers and cargo) vessels, the components within the platform are entirely generic and may be applied to the distributed design of any type of vessel, or other complex made-to-order products