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Understanding the relationships between aesthetic properties and geometric quantities of free-form surfaces using machine learning techniques

  • Aleksandar PetrovEmail author
  • Jean-Philippe Pernot
  • Franca Giannini
  • Philippe Véron
  • Bianca Falcidieno
Original Paper

Abstract

Designing appealing products plays a key role in commercial success. Understanding the relationships between aesthetic properties and shape characteristics of a product can contribute to define user-friendly and interactive designing tools supporting the early design phases. This paper introduces a generic framework for mapping aesthetic properties to 3D free form shapes. The approach uses machine learning techniques to identify rules between the user-defined classifications of shapes and the geometric parameters of the underlying free form surfaces and to create an efficient classification model. The framework has been set up and validated focusing on the flatness aesthetic property but is generic and can be applied to others. Several experiments have been conducted to understand if there is a consistency among people in the judgement of a specific aesthetic properties, if and to which extent the surrounding of the judged surface affects the perception consistency, and which are the surface geometric quantities influencing the perception. A graphic user interface has been designed to allow a fast classification of thousands of shapes automatically generated. The experiments have been conducted following a systematic methodology comparing two different approaches. The results confirm that the perception of flatness is commonly shared by the majority and the most relevant attributes have been identified. Additionally, it results that the surrounding information extension and context influence the perception of the flatness strengthening the classification consistency. The way those results can be used to design new interactive tools and to improve the product design process is discussed.

Keywords

Free-Form surfaces Machine Learning Techniques Data Mining Aesthetic Properties 

Abbreviations

MLT

Machine learning techniques

AE

Affective engineering

PDP

Product design process

FIORES

Formalization and Integration of an Optimized Reverse Engineering Styling Workflow

IDS

Instance data set

GUI

Graphical user interface

WEKA

Waikato Environment for Knowledge Analysis

M

Morphing

Ts

Target shapes

DP

Deformation path

TsCM

Target shapes of a coffee machine

TsCB

Target shapes of a car back

TsCD

Target shapes of a car door

Notes

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Copyright information

© Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  1. 1.LISPEN EA 7515, HeSamArts et Métiers ParisTechAix-en-ProvenceFrance
  2. 2.CNR-IMATIGenoaItaly

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