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ZENODO
Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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GENERATIVE DESIGN AS AN INNOVATION IN PRODUCT DEVELOPMENT PROCESSES

Authors: Academic Journal of Manufacturing Engineering;

GENERATIVE DESIGN AS AN INNOVATION IN PRODUCT DEVELOPMENT PROCESSES

Abstract

ABSTRACT: Generative Design (GD) technologies are increasingly recognised as a disruptive innovation in CAx-based product development, offering algorithm-driven solutions to complex design challenges. This study explores how professionals in engineering and design perceive the impact, benefits, and barriers associated with the adoption of GD tools, particularly those integrating Artificial Intelligence. Using a quantitative research design, data were collected from 185 professionals across multiple sectors via an online questionnaire, which assessed familiarity with GD, frequency of use, perceived improvements in design quality, and key challenges such as software complexity and workflow compatibility. Statistical analyses, including t-tests, correlation analysis, and regression modelling, were used to evaluate five core hypotheses regarding the perceived strategic value and implementation barriers of GD technologies. Results indicate strong professional confidence in GD’s capacity to enhance product quality, reduce design errors, and foster collaboration, while also revealing that steep learning curves and integration difficulties continue to hinder adoption. Findings support the view that successful implementation depends on access to training, compatible software ecosystems, and clearer value communication. The study contributes empirical evidence to a growing body of literature on engineering digitalisation and highlights the need for user-centred integration strategies to unlock the full potential of GD in industry.

Keywords

Generative Design, Product Development, Artificial Intelligence, Design Automation, Design Optimisation

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average