A surface roughness prediction model for the ballnose machining of polypropylene

Vimal G. Dhokia, Sanjeev Kumar, Parag Vichare, Stephen T. Newman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The advent of personalised product design has brought about the need for more sophisticated manufacturing practices and technology. Personalised manufacture of consumer goods is now being pursued on CNC machining centres as opposed to the alternative of highly expensive rapid prototyping methods. The problem with the manufacture of personalised consumer products is that they can be free formed objects, which require sophisticated machining setups. Ballnose machining is a practice used to create cusp type geometry for such products, and together with CAM and CNC technology now makes it possible to create a wide range of sculptured surfaces. Ballnose machining is not new, however the ability to use this existing technology for personalised products particularly for parts machined out of polypropylene offers new avenues of research. The ability to optimise a process offers distinct advantages in terms of improved part, reduced cutting time and reduced tool degradation. The purpose of this paper is to provide a predictive model using the design of experiments for ballnose machining of polypropylene. The goal being the creation of a new knowledge base which will be used to improve the machining of polypropylene using ballnose tools in order to generate sculptured parts. The paper presents an experimental methodology based on a design of experiments strategy. The final part of the paper illustrates an optimised surface roughness model developed using Genetic Algorithm. This prediction model is tested on a validation data set and the percentage deviation of the result when compared with the measured results is shown to be small, which demonstrates the efficacy ofthe model.
Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing
Subtitle of host publicationFAIM 2007
Pages211-218
Number of pages8
Volume1
Publication statusPublished - 2007
Externally publishedYes

Fingerprint

Polypropylenes
Machining
Surface roughness
Design of experiments
Machining centers
Consumer products
Rapid prototyping
Computer aided manufacturing
Product design
Genetic algorithms
Degradation
Geometry

Keywords

  • Stirnfräsen
  • Polypropylen,
  • Vorhersagetheorie,
  • Rauigkeit
  • mathematisches Modell,
  • statistische
  • Versuchsplanung
  • Wissensbank,
  • genetischer Algorithmus,
  • Theorie-Experiment-Vergleich

Cite this

Dhokia, V. G., Kumar, S., Vichare, P., & Newman, S. T. (2007). A surface roughness prediction model for the ballnose machining of polypropylene. In Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2007 (Vol. 1, pp. 211-218)
Dhokia, Vimal G. ; Kumar, Sanjeev ; Vichare, Parag ; Newman, Stephen T. / A surface roughness prediction model for the ballnose machining of polypropylene. Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2007. Vol. 1 2007. pp. 211-218
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Dhokia, VG, Kumar, S, Vichare, P & Newman, ST 2007, A surface roughness prediction model for the ballnose machining of polypropylene. in Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2007. vol. 1, pp. 211-218.

A surface roughness prediction model for the ballnose machining of polypropylene. / Dhokia, Vimal G.; Kumar, Sanjeev; Vichare, Parag; Newman, Stephen T.

Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2007. Vol. 1 2007. p. 211-218.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The advent of personalised product design has brought about the need for more sophisticated manufacturing practices and technology. Personalised manufacture of consumer goods is now being pursued on CNC machining centres as opposed to the alternative of highly expensive rapid prototyping methods. The problem with the manufacture of personalised consumer products is that they can be free formed objects, which require sophisticated machining setups. Ballnose machining is a practice used to create cusp type geometry for such products, and together with CAM and CNC technology now makes it possible to create a wide range of sculptured surfaces. Ballnose machining is not new, however the ability to use this existing technology for personalised products particularly for parts machined out of polypropylene offers new avenues of research. The ability to optimise a process offers distinct advantages in terms of improved part, reduced cutting time and reduced tool degradation. The purpose of this paper is to provide a predictive model using the design of experiments for ballnose machining of polypropylene. The goal being the creation of a new knowledge base which will be used to improve the machining of polypropylene using ballnose tools in order to generate sculptured parts. The paper presents an experimental methodology based on a design of experiments strategy. The final part of the paper illustrates an optimised surface roughness model developed using Genetic Algorithm. This prediction model is tested on a validation data set and the percentage deviation of the result when compared with the measured results is shown to be small, which demonstrates the efficacy ofthe model.

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BT - Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing

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Dhokia VG, Kumar S, Vichare P, Newman ST. A surface roughness prediction model for the ballnose machining of polypropylene. In Proceedings of the 17th International Conference on Flexible Automation and Intelligent Manufacturing : FAIM 2007. Vol. 1. 2007. p. 211-218