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Customer-Centered Design Sampling for CAD Products Using Spatial Simulated Annealing

CAD’17

Okayama, Japan

Erkan Gunpinar1, Shahroz Khan1, Masaki Moriguchi2

1 Istanbul Technical University

2 Meiji University

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Research Objective

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • Technique that can sample and generate variety of design options for a CAD product based on the customer preferences.
  • Sampling technique called Spatial Simulated Annealing-Design Sampler (SSA-DS) is proposed to obtain different design variations of a CAD product.

SSA-DS

Design parameters

Parametric bounds

 

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Previous Works

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • Based on LHS for design selections.
  • Randomly distributed designs.
  • Mutation operator – to moved designs to feasible region.

Fuerle and Sienz, 2011

Chen et. al. 2013

  • Used spatial simulated annealing approach based on the minimization of shortest distance criterion.

Fuerle and Sienz (2011)

Chen et al. (2013)

Drawbacks

  • Not applicable for the high dimensional sampling problems.
  • CAD samplingis not possible with these techniques.

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Customer Centered Design

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • A Process in which customer/user of a product is involved throughout its design phase.
  • Why customer centered design ?
  • Customer satisfaction
  • Easy-to-use product
  • Improved user productivity
  • Low expenditure on technical support
  • Increase market share

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CAD Design Sampling

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • CAD model parameterization (design parameters).
  • Relationships between the design parameters (design constraints).
  • Range of the design parameters (lower and upper bounds).
  • Design parameters and their bounds form the design space.
  • Each design parameter represents a dimension in the design space.

 

 

Parameterized yacht hull design

Sampled Designs

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Work Flow of Methodology

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

SSA-DS

Learn Customer preferences from initial designs via one-to-one interviews

Translate customer preferences as geometric constraints

Initial Designs

Design parameters

Parametric bounds

SSA-DS

Customer-Centered Designs

Design parameters

Parametric bounds

Number of designs (N)

 

 

Number of designs (N)

Inputs

Output

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SSA Design Sampler (SSA-DS)

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

 

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Cost Function

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • Cost function is define based on the space-filling property.
  • Design should be uniformly distributed over the design space.
  • Space-filling is achieved using the Audze and Eglais‘ (2011) technique.

With Space-filling

Without Space-filling

 

 

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Experimental Study

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • A wine glass (without base) is selected as a test model.
  • Cubic Bezier curves are utilized to generate upper and lower region.
  • 3D surfaces are generated by performing lofting operation.

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Results –Initial designs

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

Parametric Bounds (in millimeters)

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Results –Learning perfernces

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

 

Geometric Constraints

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  • A participant was selected and recognized as customer.
  • Each design was shown to the participant during an one-to-one interview.
  • Participant preferences were quantified and converted into design constraints.

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Conclusions

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

 

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Future Works

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan

  • Current research will try to further improve the space filling property of the SSA-DS algorithm.
  • More test models and participants will be involved in order to validate the performance.
  • Participants response can be used to obtain the critical design parameters.

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Acknowledgment

This work was supported by The Scientific and Technological Research Council of Turkey (Project Number: 315M077)

Erkan Gunpinar, Shahroz Khan, Masaki Moriguchi

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CAD'17, Okayama, Japan