
Academic Journal
Q2Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
About Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM is a scholarly journal published by Cambridge University Press. SCImago 2025 lists it in Q2, with an SJR of 0.447 and H-index of 65.
Coverage: 1987-2026. Research categories: Industrial and Manufacturing Engineering (Q2); Artificial Intelligence (Q3).
Source-backed journal facts
Topics in published research
Design Education and Practice; Manufacturing Process and Optimization; Product Development and Customization; Diverse Scientific and Economic Studies; BIM and Construction Integration; AI-based Problem Solving and Planning.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Reported open-access list prices
3,550.00 USD; 2,460.00 GBP
APC list prices reported by OpenAlex, which obtains this information from DOAJ. Confirm current charges, taxes, waivers and eligibility with the publisher; this is not a fee quotation.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-02. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
Quartile, SJR and the listed SCImago H-index use the 2025 imported SCImago dataset. A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
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2026 · DOI: 10.1017/s0890060426100225Creative potential of image-generative AI models for conceptual engineering design tasks
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2026 · DOI: 10.1017/s0890060425000058Evaluating the performance of large language models in taxonomic classification of questions in verbal protocols of design
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2026 · DOI: 10.1017/s0890060426100213Creation-as-transmission: a cognitive-based framework for cultural heritage learning through AI collaborative creation
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2026 · DOI: 10.1017/s0890060426100237Synthetic users: insights from designers’ interactions with persona-based chatbots
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2025 · DOI: 10.1017/s0890060424000283A novel idea generation tool using a structured conversational AI (CAI) system
B. Sankar, Dibakar Sen
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2025 · DOI: 10.1017/s0890060425100036An ontological framework for the integration of system design and FMEA
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2025 · DOI: 10.1017/s0890060425100206Internet of Things and hybrid models-based interpretation systems for surface roughness estimation – CORRIGENDUM
R. S. Umamaheswara Raju, Ravi Kumar Kottala, B. Madhava Varma, Palla Krishna et al.
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Niccolò Batini, Niccolò Becattini, Gaetano Cascini
2025 · DOI: 10.1017/s089006042510005xAutomated generation of floor plans with minimum bends
Pinki, Krishnendra Shekhawat, Akshat Lal
2025 · DOI: 10.1017/s0890060424000179BLIPS-PA: a qualitative-physics-based method for fault causality and probabilistic analysis of novel designs
Ali Mansoor, Xiaoxu Diao, Carol Smidts
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Unal Yildirim, Felician Campean, Amad Uddin
2025 · DOI: 10.1017/s0890060425100176Machine learning models for sequential motion recognition in human activity for logistics operations efficiency improvement
Chih-Feng Cheng, Chiuhsiang Joe Lin, Qin-Xuan Hu
2025 · DOI: 10.1017/s0890060424000313Reviews
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October 4, 2026 at 9:21 pm
October 2, 2026