Hello SwYmmers,

As you know, SIMULIA has just launched Isight 5.8, the newest version of the market-leading open desktop solution for simulation process automation and design exploration. For those that are not familiar with Isight, it is the SIMULIA solution that provides designers, engineers, and researchers from every industry with an open system for integrating design and simulation models—created with various CAD, CAE, and other software applications—to automate the execution of hundreds or thousands of simulations. This is helpful because it allows users to save time and improve their products by optimizing against performance or cost metrics through statistical methods, such as Design of Experiments (DOE) or Design for Six Sigma. Isight combines cross-disciplinary models and applications together in a simulation process flow, automates their execution, explores the resulting design space, and identifies the optimal design parameters based on required constraints.

Isight is a powerful tool for defining new methodology (i.e. creating workflow). In the Isight user can define their engineering problem very easy way. Isight solves engineering problem by using DoE, Approximation, optimization & Taguchi technique that’s helps in product development & customer can take smarter decisions early in the design process. Isight support various graphs which help to Identify the most sensitive design parameters also determines how much scatter in the response is due to input variation and minimize it. Robust design help for improving quality of process by reducing rejection.

Due to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning.

The present work deals with the study and development of an optimization of machining parameter for surface roughness as objective, by genetic algorithm. The experimentation was carried out with CNMG cutting tools, for EN-GJS-400-18U-LT work-pieces covering a wide range of machining conditions.

Key Isight Features and Benefits

• Design of Experiment (DoE): To create experimental data
• RSM: Build mathematical model
• Optimization: Approach to obtain the machining conditions for the required surface finish

Workflow

 The actual experiment spanned across complete surface of the axle pin, but the blog here explains optimization process for a single location within the span of the pin.

Results

• An Isight multi-objective optimization method is proposed to achieve desired surface finish
• Results obtained from this simulation closely match with the experimental data
• Isight Pareto graphs can help in selection of most significant cutting parameters. The Pareto analysis confirms with experimental results and theoretical study
• The optimized process parameters obtained from this simulation model can be directly fed into ‘Computer Aided Process Planning’ system
• This method can further be extended to minimizing of machining time, machining costs and maximizing tool life

Learn More! Register for the FREE Upcoming Isight Webinar...

I would like to thank WALVEKAR Kedar, CHITNIS Mahesh, BIRAJDAR Nilesh, UKE Yogesh, KULKARNI Ashish & NAGOSE Rajesh for providing valuable input and directions for completion of this project.

Experiment conducted at ‘PMT Machine Tool, Pune’ Year 2009