典型零件的數(shù)控銑加工編程設(shè)計(jì)【說(shuō)明書+CAD+UG】

典型零件的數(shù)控銑加工編程設(shè)計(jì)【說(shuō)明書+CAD+UG】,說(shuō)明書+CAD+UG,典型零件的數(shù)控銑加工編程設(shè)計(jì)【說(shuō)明書+CAD+UG】,典型,零件,數(shù)控,加工,編程,設(shè)計(jì),說(shuō)明書,仿單,cad,ug feed- Zealand Monitoring Fuzzy control and interna Impl The se product n optimi meters machining environment with the intention to provide adaptive and automatic in-process machining optimisation. KBE based-MTConnect is responsible for obtaining machining know-how. Optimisation is performed before, during or after machining operations, based on the data collected and monitored al co ab logies g machine are ng dev For real functio machining parameters are best monitored and controlled, so that spite of great technological achievements, contemporary CNC Contents lists available at SciVerse ScienceDirect Robotics and Computer-In Robotics and Computer-Integrated Manufacturing 29 (2013) 1220 that are mostly step-by-step instructions to drive the earliestE-mail address: x.xuauckland.ac.nz (X. Xu). machine tool behaviour is analysed in time and appropriate programmes are still being executed based on a sequential set of NC programming language, aka G-codes. These codes were developed more than 50 years ago with little, if any, intelligence. The initial design of the codes was to hold a set of low-level data 0736-5845/$-see front matter they collectively act as the backbone of the communica- tion standard. The device is referred to components such as controller, sensors and machine tool that are responsible for providing the monitored data. These data are acquired by data acquisition system and gathered by an Adapter. The Adapter is responsible for communicating and streaming it to the Agent in a standard format. The data acquisition process acts as the devices Application Programming Interface (API), with which the Adapter would communicate. The Agent then accepts the data requests from a Client application, which then returns the data in XML format. The client can then extract the data from the document and display it to the user. These data can be evaluated for more meaningful output by taking the current condition of the machine tool. 3.3.2. Data acquisition and analysis Since STEP-NC provides a rich data modelling method for describing machining data, compared with a conventional NC code structure, the machining know-how under the STEP-NC system can thus be preserved for the entire product development cycle. STEP-NC is a high-level data model and its execution also requires more specific machining data. The KBE system is responsible for three tasks: data recording, visualisation and evaluation. First, the data streamed through MTConnect is recorded in a database which can be accessed at GUI. of the KBE system. Table 1 Recorded data via MTConnect. TCPFR A JMVA 6.194 0.061 94.285 C00.934 C01.844 2920.020 6.295 0.059 91.443 C00.469 0.465 3973.309 6.356 0.062 84.417 C01.920 C01.451 3254.824 6.497 0.058 70.085 C01.694 0.226 3086.413 6.544 0.054 54.155 C05.649 C03.955 5063.836 6.653 0.057 41.827 C01.885 3.764 2966.843 6.747 0.055 34.188 C01.354 0.531 2470.152 F. Ridwan, X. Xu / Robotics and Computer-Integrated Manufacturing 29 (2013) 122018 recorded include actual feed-rate, acceleration, jerk, cutting power and maximum vibration amplitude. Second, the system provides a user interface for visualisation purposes. The interface consists of a tree-view, table and graphical representation of the acquired dynamic machining parameters. The snapshot of the user interface is shown in Fig. 6. Third, machining parameters are evaluated with the aim of obtaining another set of optimum parameters for subsequent machining operations. These include: (1) real cutting power values used to calculate optimum feed- different (2) (3) All feed-rates then file. superior 3.3.3. The into shown increment observed. every feed-rates jerk. elements accelerated contouring for locations. The dynamic machining parameters that are Fig. 6. Interface rates, acceleration and jerk values that are evaluated to obtain smooth motion, chatter analysis obtained by observing vibration signal through the Short Time Fourier Transform (STFT) to avoid excessive chatter during cutting. of these evaluated parameters help provide appropriate for safer machining operations. These feed-rates are assigned for updating the STEP-NC data stored in a STEP-NC In this way, the knowledge can be utilised in performing machining operations. Data evaluation data streamed via MTConnect is continuously recorded a know-how database. A portion of the recorded data is in Table 1. It can be seen in the table that for every of time, changes of machine behaviour can be Clearly visible is the gradual change of feed-rates for single increment of time. The dynamic behaviour of these can be further differentiated to obtain acceleration and During machining, excitations of vibrations in machine can result from excessive jerk, which can lead to tool wear, increasing machining noise and large errors 15. Thus, jerk values can act as an indicator smooth machining operation. The data from the time domain needs to be recorded sepa- rately, due to the large amount of time domain data (at a sampling rate of 10,000 Hz). The time domain vibration signal is further processed using the STFT technique. Fig. 7 is an example of the STFT view obtained from part of the recorded data at an interval of 8 s. STFT can perform the frequency variation over the time duration. From this variation, any significant chatter can be detected, which will determine the feed-rate value at that time. For example, significant amplitude at chatter frequency of 185.5 Hz occurred at 3.8 s. The recorded data show that the controller generated a feed-rate of 127 mm/min at around 3.8 s of machining. As a result, the feed-rate of 127 mm/min can be recognised as over-speed of the machine tool table movement. It should be avoided for subsequent machining operations by 6.856 0.053 32.203 C00.303 1.051 3287.138 6.95 0.058 44.134 2.115 2.419 2956.006 7.044 0.056 56.634 2.216 0.101 3369.895 7.153 0.054 58.595 0.300 C01.916 4043.477 7.247 0.058 55.622 C00.527 C00.827 3091.048 7.356 0.055 50.522 C00.780 C00.253 4358.899 7.544 0.056 37.438 C01.073 0.174 4047.113 7.653 0.057 49.938 1.911 2.984 4594.567 TTime (s). CPCutting power (kW). FRFeed-rate (mm/min). AAcceleration (mm/sec 2 ). JJerk (mm/sec 3 ). MVAMax vibration amplitude (1000 C01 ). controller. F. Ridwan, X. Xu / Robotics and Computer-Integrated Manufacturing 29 (2013) 1220 19 4. Conclusions Use of the STEP-NC data model provides a promising platform for various applications consolidated under the same data struc- updating the STEP file. This real feed-rate value can be controlled, to not exceed the value of the allowable amplitude of chatter frequency, by setting the upper limit of feed-rate in the tuning system. Hence, the improved feed-rate can be assigned to the Fig. 7. STFT representation ture. It brings design data such as geometry, tolerances and materials into process control and monitoring of machining operations, allowing a robust control mechanism. Motivated by this benefit, the newly developed EXPRESS schema for optimisa- tion purposes augments the existing STEP-NC data models. This is necessary for an integrated environment in which high-level machine condition monitoring can be exercised for optimising machining processes. The developed EXPRESS data model pro- vides the necessary data for machining optimisation. The STEP-NC enabled machine condition monitoring system consists of three sub-systems. The first subsystem, optiSTEP-NC, is responsible of early phase optimisation. The purpose is to assist process planners in generating optimum machining parameters for a STEP-NC file. This is carried out for two different scenarios: (a) maximizing feed-rate and depth of cut for time-critical machining operations (e.g., roughing operations) and (b) maximizing machining quality for quality-critical machining operations (e.g., finishing operations). A simulator has been developed to verify the optimisation algorithms, the real-time process control and the monitoring algorithm. An adaptive execution of a CMC program with feed-rate optimisation (AECopt) controller is the second sub-system of the framework. The controller allows canonical machine com- mands to be executed with a fuzzy feed-rate optimisation module. The key feature of the proposed NC program executor is the ability to perform adaptive feed-rate optimisation by keeping a constant load within machine tools capability. Further- more, the optimisation algorithm can also help reduce chatter amplitude. Hence, occurrence of excessive chatter can be avoided. This leads to a much healthier machining operation environment. The experimental results approved the effectiveness of the proposed feed-rate optimisation module. The third subsystem (known as a knowledge-based evaluation system) was developed. Accurate, informative and updated machin- ing know-how is utilised for achieving automated and intelligent machining operations. By effectively monitoring and recording machining processes in the standardized environment of STEP-NC and MTConnect, a complete utilization of machining know-how can be applied at any point in time. The KBE system demonstrated that of vibration analysis. valuable machining know-how helps identify optimal feed-rates so that the onset of chatter can be avoided. The system can also record machining parameters such as cutting power, acceleration, jerk and maximum vibration amplitude and further evaluate these data in the effort to obtain an optimal machining performance. Overall, the need for machinists to manipulate and adjust NC codes is mini- mized. The time taken for engineers to develop effective process plans is reduced and finally the decision-making by top-level managers can be made even remotely. Acknowledgements This research is supported by the Directorate General of Higher Education (DGHE) Department of National Education of Indonesia under scholarship contract No:1840-D4.4-2008. The authors wish to thank Johannes Nittinger for his contribution in programming and setting up MTConnect. References 1 Elbestawi MA, Dumitrescu M, E-G NG. Tool condition monitoring in machin- ing, in: Springer Series in Advanced Manufacturing Wang, Lihui; Gao, Robert x (eds.)1st edition., Hardcover isbn: 978-1-84628-268-3 (2006) 5582. 2 Zhao F, Habeeb S, Xu X. Research into integrated design and manufacturing based on STEP. International Journal of Advanced Manufacturing Technology 2009;44(56):606624. 3 Suh SH, Chung DH, Lee BE, Cho JH, Cheon SU, Hong HD, Lee HS. Developing an integrated STEPcompliant CNC prototype. Journal of Manufacturing Systems 2002;21:350362. 4 Xu X, He Q. Striving for a total integration of CAD, CAPP, CAM and CNC. Robotics and Computer-Integrated Manufacturing 2004;20:101109. 5 Maeder W, Nguyen J, Richard VK, and Stark J. Standardisation of the manufacturing process: the IMS STEP-NC project, IPL (National Network of Competence on Integrated Production and Logistics) Net Workshop, Saas Fee, Switzerland (2002). 6 ISO-TC184/SC1/WG7, Automation systems and integration, /http:/www.iso. org/iso/standards_development/technical_committees/list_of_iso_technical_ committees/iso_technical_committee.htm?commid=54110S. 7 Wang H, Xu X, Tedford JD. An adaptable CNC system based on STEP-NC and function blocks. International Journal of Production Research 2007;45: 38093829. 8 Xu X. Integrating advanced computer-aided design, manufacturing, and numerical control: principles and implementations. New York: Information Science Reference IGI global; 2009. 9 Ridwan F, Xu X, Liu G. A framework for machining optimisation based on STEP-NC. Journal of Intelligent Manufacturing 2012;23(3):423441. 10 Maryland-Metrics, Surface roughness tables (1998). 11 Kramer TR, Proctor F, Xu X, Michaloski JL. Run-time interpretation of STEP- NC: implementation and performance. International Journal of Computer Integrated Manufacturing 2006;19:495507. 12 Liu Y, Guo X, Li W, Yamazaki K, Kashihara K, Fujishima M. An intelligent NC program processor for CNC system of machine tools. Robotics and Computer- Integrated Manufacturing 2007;23:160169. 13 Proctor F, Michaloski J, Kramer T, A methodology for integrating sensor feedback in machine tool controllers, Robot Systems Division, National Institute of Standards and Technology Technology Administration, U.S. Department of Commerce Gaithersburg, Maryland 208991(1992). 14 Vijayaraghavan A, Sobel W, Fox A, Dornfeld D, and Warndorf P. Improving machine tool interoperability using standardized interface protocols: MTConnect, Proceedings of ISFA, International Symposium on Flexible Auto- mation, Atlanta, GA, USA (2008). 15 Dong J, Ferreira PM, Stori JA. Feed-rate optimisation with jerk constraints for generating minimum-time trajectories. International Journal of Machine Tools and Manufacture 2007;47:19411955. F. Ridwan, X. Xu / Robotics and Computer-Integrated Manufacturing 29 (2013) 122020