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Modelling and Optimisation of Machining Processes with CAE
Transcript of Modelling and Optimisation of Machining Processes with CAE
Visualisation of machining process
Means of communication
Fig. 1 Machining Input Parameters (Radovanovic and Madic, 2010)
Fig. 2 Machining Output Parameters (Radovanovic and Madic, 2010)
Usage of Neural Networks which can be “trained”.
Fig. 3 Structure of Artificial Neuron (Karayel, 2009)
Reduced overall lead-time and cost
Tool path and speed are optimised with minimum waste material
Operation by less experienced workers
Optimised chip-load for smoother surface finish
Reduced tool wear
Increases efficiency and economy of machining processes
Mimics natural selection
Based on degrees of truth
Combination of both results in “human like” logic which evolves to an optimum solution
Fig. 4 Standard Procedure of Genetic Algorithm (Petkovic and Radovanovic, 2013)
But all this is...
..still subject to user’s ability to operate software
Optimum conditions can be determined virtually through simulation
Reduced lead-time and cost
• Can be used continuously, only switching off for occasional maintenance.
• Errors due to operator fatigue, interruptions and other factors no longer occur.
• One person is usually enough to supervise the work of CNC machines once they are in operation.
• No need for prototyping. Different versions or ideas can be visualised on software.
• Enables manufacture of products that cannot be made on manual lathes (like 5 axis machining)
• Can be improved by updating the software. Training for the use of the machines is done through software.
• Can manufacture thousands of identical end products given a design,
• Investment in CNC machines can lead to unemployment as fewer workers are required to operate CNC machines.
• Expensive to repair.
• Errors are not necessarily eliminated as operators can still make incorrect decisions.
• Even with reducing prices their initial cost is higher than manually operated machines.
Stands for Computer Aided Three-dimensional Interactive Application
Used as Boeing’s main 3D CAD tool
Written in C++
Marketed and supported by IBM
CATIA ICEM Surface Modelling
ICEM is a high quality surface modelling feature of CATIA.
Class A quality models can be made from basic sketches which the software interacts with.
Combination of Casting, Forging, Welding and Manual Machining and a host of other processes.
With growing technological development there was a need for:
Can be used to achieve complex shapes in a single set up
Massive expertise and strict manufacturing controls to reach acceptable quality
Suffered from internal defects, blistering, flashing and other problems.
More efficient process
Capability to tailor products
5 Axis Machining
Complex geometries made with relative ease with effective CAM software.
No need to invest in new tooling or machinery.
Can be used to achieve curved and tilted surface machining and drilling
Simpler impeller design
It is a multipurpose package (CAM/CAD/PLM)
CATIA model of a Boeing Aeroplane
Programmers can easily switch between tool path definition and validation without losing time on data transfer or preparation eliminating interface issues and reducing manufacturing time
"Traffic lights" indicate if there are still undefined parameters to complete the operation. Tool changes and machine rotations are automatically generated and can be visualised.
CATIA can simulate machine tool operation including the amount of material removed and analyse the remaining material.
Machine motion can be simulated based on the ISO code which is fully compatible with the software the code can then be imported back in the to NC machine.
The simulated tool path is accurate enough to warn you of any possible collisions between the part and tool and also propose possible safer paths.
2.5 Axis Machining Operations include
: multi-level pocketing, facing and contouring; point-to-point machining
Machining processes can be stored and applied to different parts and by reusing processes the efficiency and speed can be improved.
3 Axis Machining Operations include:
Sweep roughing, z-level milling, and between-contours machining
Founded in Massachusetts in 1983
Flagship of CNC Software, Inc
Most widely used CAD/CAM package in the world
Dynamic Motion Control
Combines multiple tools into a single "cell"
Necessary commands extracted and then loaded into the CNC machines for production
• Loss of old skills.
• Generate more material waste than casting type processes.
• Reduction in the number of possible features including internal shapes and parts
Presentation by Group 18:
Alex Fung 4184900
Jeremiah Balogun-Macaulay 4182417
Martynas Brazenas 4187674
Md Syafiq Ridauddin bin Raduan 4193112
Saad Rayees 4192268
Wilde Analysis Ltd.. (2014) Manufacturing Process Simulation. [Online] Available from: http://wildeanalysis.co.uk/fea/applications/manufacturing-processes [Accessed: 13th October 2014]
Werner, J. (2000) The Case For Verifying And Optimizing Tool Paths. [Online] Available from: http://www.mmsonline.com/articles/the-case-for-verifying-and-optimizing-tool-paths [Accessed: 13th October 2014]
Ryan, V. (2009) Advantages And Disadvantages Of CNC Machines. [Online] Available from: http://www.technologystudent.com/cam/cncman4.htm [Accessed: 15th October 2014]
RMIT University. (n.d.) Advantages And Disadvantages Of Using CNC Machines. [Online] Available from: https://www.dlsweb.rmit.edu.au/toolbox/furnishindustry/toolbox/shared/resources_mw/ask_expert/tony/advantages.htm [Accessed: 15th October 2014]
Powercatia. (n.d.) Powercatia: Experience the Power of CATIA Design. [Online] Available from: http://www.powercatia.com [Accessed: 16th October 2014]
Dassault Systemes. (2014) CATIA. [Online] Available from: http://www.3ds.com/products-services/catia/ [Accessed: 16th October 2014]
Petkovic, D. & Radovanovic, M. (2013) Using Genetic Algorithms For Optimization Of Turning Machining Process. Journal of Engineering Studies and Research. [Online] 9 (1). p. 47-55. Available from: pubs.ub.ro/dwnl.php?id=JESR201301V19S01A0008 [Accessed: 15th October 2014]
Roth, D., Ismail, F. & Bedi, S. (2003) Mechanistic modelling of the milling process using an adaptive depth buffer. [Online] Elsevier. Computer-Aided Design 35 (1). p.1287-1303. Available from: http://www.researchgate.net/profile/Sanjeev_Bedi/publication/222546716_Mechanistic_modelling_of_the_milling_process_using_an_adaptive_depth_buffer/links/0f31752d53c38936a4000000 [Accessed: 12th October 2014]
Sutherland, J. W., DeVor, R. E., Kapoor, S. G. & Ferreira, P. M. (1988) Machining Process Models for Product and Process Design. Society of Automotive Engineers, Inc. [Online] p.1-12. Available from: www.me.mtu.edu/~jwsuther/Publications/218_J003.pdf [Accessed: 5th October 2014]
Radovanovic, M. & Madic, M. (2010) Methodology of Neural Network Based Modeling of Machining Processes. International Journal of Modern Manufacturing Technologies. [Online] 2 (2) ISSN 2067–3604. p.77-82. Available from: http://modtech.ro/international-journal/vol2no22010/Miroslav_Radovanovic.pdf [Accessed: 14¬th October 2014]
Shandilya, P. & Tiwari, A. (2014) Artificial Neural Network Modeling and Optimization using Genetic Algorithm of Machining Process. Journal of Automation and Control Engineering. [Online] 2 (4). p.348-352. Available from: http://www.joace.org/index.php?m=content&c=index&a=show&catid=40&id=177 [Accessed: 14¬th October 2014]
Aggarwal, A. & Singh, H. (2005) Optimization of machining techniques - A retrospective and literature review. Sādhanā. [Online] 30 (6). p.699-711. Available from: www.ias.ac.in/sadhana/Pdf2005Dec/PE1312.pdf [Accessed: 15¬th October 2014]
Karayel, D. (2009). Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of Materials Processing Technology. 209 (7) ISSN 0924-0136, p. 3125-3137