ENBIS-16 in Sheffield
11 – 15 September 2016; Sheffield
Abstract submission: 20 March – 4 July 2016
Statistical Approach for Knowledge Improvement in Waterjet Machining of High Performance Ceramics
13 September 2016, 09:00 – 09:20
- Submitted by
- Biagio Palumbo
- Flaviana Tagliaferri (Fraunhofer JL IDEAS-SQUARE, Department of Industrial Engineering, University of Naples Federico II), Biagio Palumbo (Fraunhofer JL IDEAS-SQUARE, Department of Industrial Engineering, University of Naples Federico II), Matthias Putz (Fraunhofer Institute for Machine Tools and Forming Technology IWU (Chemnitz)), Markus Dittrich (Chemnitz University of Technology, Institute for Machine Tools and Production Processes IWP), Martin Dix (Chemnitz University of Technology, Institute for Machine Tools and Production Processes IWP)
- This work highlights the strategic role that a systematic and sequential approach to experimentation plays in order to get competitive advantage, technological innovation and knowledge improvement.
An accurate pre-design (i.e. pre-experimental planning phase) is the solid basis on which a statistical approach has to be built. The investigation was based on the use of customized Pre-Design Guide Sheets. The documents provide a way to systematize the experimental planning. In fact, the pre-design guide sheets drive the research team to clearly define the objectives and scope of an experiment and to gather information needed to design it.
The efficacy of this approach is demonstrated here by an applicative example about the Waterjet Machining of High Performance Ceramics, developed at Chemnitz University of Technology.
The aim was to achieve a high material removal rate at an adequate surface quality. The experimental design was shared in two phases: screening and optimization phase. In the screening experimental phase a 25-1 design was adopted. In the optimization phase 6 central points and 10 axial (or star) points were added to the original design. The Face Centered (α=1) Central Composite Design (CCD) was used for fitting second order response surface regression model. The Response Surface Methodology (RSM) allowed to optimize the response variables.
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