Yapay Zekâ Destekli Araçların Kullanımıyla Endüstriyel Tasarım Sürecinde Sağlanan Disiplinler Arası Gelişmeler

The integration of artificial intelligence (AI) into industrial design is revolutionizing traditional methods, particularly in areas such as rapid prototyping, sustainable material selection, and predictive analytics. AI-driven tools accelerate prototyping cycles, reduce reliance on physical models, and enable designers to predict product performance under real-world conditions. Additionally, AI enhances sustainability by analyzing extensive material databases, guiding decisions that balance durability, cost, and environmental impact. Its role in data-driven personalization allows mass production to meet individual user needs efficiently while minimizing waste. Moreover, AI fosters collaboration among interdisciplinary teams by providing real-time feedback and facilitating iterative improvements throughout the design process, ultimately empowering designers to achieve greater innovation and productivity. By seamlessly integrating these capabilities, AI is transforming design workflows across industries, improving both their speed and quality. This article delves into current academic literature to evaluate AI’s current role as a powerful tool in shaping the future of industrial design and offers suggestions for its effective application, alongside proposed areas for further research.

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DergiPark (Istanbul University)
Published
2024-11-22
Primary Topic
Machine Learning in Materials Science
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article
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article

Yapay Zekâ Destekli Araçların Kullanımıyla Endüstriyel Tasarım Sürecinde Sağlanan Disiplinler Arası Gelişmeler

Hüseyin Özkal Özsoy
DergiPark (Istanbul University)
Machine Learning in Materials Science
article

Yapay Zekâ Destekli Araçların Kullanımıyla Endüstriyel Tasarım Sürecinde Sağlanan Disiplinler Arası Gelişmeler

Hüseyin Özkal Özsoy
article en

Abstract

The integration of artificial intelligence (AI) into industrial design is revolutionizing traditional methods, particularly in areas such as rapid prototyping, sustainable material selection, and predictive analytics. AI-driven tools accelerate prototyping cycles, reduce reliance on physical models, and enable designers to predict product performance under real-world conditions. Additionally, AI enhances sustainability by analyzing extensive material databases, guiding decisions that balance durability, cost, and environmental impact. Its role in data-driven personalization allows mass production to meet individual user needs efficiently while minimizing waste. Moreover, AI fosters collaboration among interdisciplinary teams by providing real-time feedback and facilitating iterative improvements throughout the design process, ultimately empowering designers to achieve greater innovation and productivity. By seamlessly integrating these capabilities, AI is transforming design workflows across industries, improving both their speed and quality. This article delves into current academic literature to evaluate AI’s current role as a powerful tool in shaping the future of industrial design and offers suggestions for its effective application, alongside proposed areas for further research.

DergiPark (Istanbul University)
Mimar Sinan Güzel Sanatlar Üniversitesi (TR)
Openalex Percentile: Top 82%
Machine Learning in Materials Science
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Yapay Zekâ Destekli Araçların Kullanımıyla Endüstriyel Tasarım Sürecinde Sağlanan Disiplinler Arası Gelişmeler — Hüseyin Özkal Özsoy · DergiPark (Istanbul University) (2024) | TGRS Research Map | TGRS