arXiv
PDF resource
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing a…
- Licence
- OPEN CC-BY-4.0
- Authors
- Jay Lee, Hanqi Su, Marco Macchi, Adalberto Polenghi, Wei Wu, Zhiheng …
- Published
- 2026-04-05 · arXiv
- Language
- en
- Length
- 49059 words
- Type
- narrative text
If your browser cannot display the embedded file, open it in a new tab.
Metadata record
Valid
One description, two standard projections
Built from what the sources declared and what the gates observed. Nothing absent has been filled in here. 1 value read out of the text by the enrichment rules stands under its element, marked inferred, and is kept apart in the exports.
- Profile
aimpro-oer-profile/1built 2026-10-09- Content language
- en declared English
DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection. the standard ↗
The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.
Identity 3/3
-
Identifier
dcterms:identifier- this engine resource_id
- source 2 assertions
-
- tag:aim-pro.eu,2026:oer/9ebcf3235b27
- https://arxiv.org/abs/2605.00839
- arxiv:2605.00839
- doi:10.1088/3049-4761/ae5967
This engine's id, the source URI, and any persistent identifier the source published.
-
Title
dcterms:title- source resource/title
-
- 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
The title of this resource, not of the record or repository it came from.
-
Type
dcterms:type- source resource
-
- dcmitype:Text
- preprint
A DCMI Type term for the form held, plus the source's own genre label as declared.
Responsibility 1/3
-
Creator
dcterms:creator- source 53 assertions
-
- Jay Lee
- Hanqi Su
- Marco Macchi
- Adalberto Polenghi
- Wei Wu
- Zhiheng Zhao
- George Q. Huang
- Kiva Allgood
- Devendra Jain
- Benedikt Gieger
- Vibhor Pandhare
- Soumyabrata Bhattacharjee
- Ram Mohril
- Lingbao Kong
- Qiyuan Wang
- Xinlan Tang
- Sungjong Kim
- Chan Hee Park
- Byeng D. Youn
- Guo Dong Goh
- Xi Huang
- Wai Yee Yeong
- Yung C Shin
- He Zhang
- Zitong Wang
- Fei Tao
- Jagjit Singh Srai
- Satyandra K. Gupta
- Byung Gun Joung
- Albin John
- John W. Sutherland
- Sang Won Lee
- Olga Fink
- Vinay Sharma
- Faez Ahmed
- Wei Chen
- Mark Fuge
- Arild Waaler
- Martin G. Skjæveland
- Dimitris Kyritsis
- Wei Chen
- VispiNevile Karkaria
- Yi-Ping Chen
- Ying-Kuan Tsai
- Joseph Cohen
- Xun Huan
- Jing Lin
- Liangwei Zhang
- Gregory W. Vogl
- Aaron W. Cornelius
- Xiaodong Jia
- Dai-Yan Ji
- Takanobu Minami
- Ruoxin Wang
Only agents the source named as creators. A repository owner is not made an author by default.
-
Contributor
dcterms:contributor- source 53 assertions
-
none found — the source did not declare it
Everyone else named, with the role as declared and the scope they were declared at.
-
Publisher
dcterms:publisher -
none found — the source did not declare it
Only when the source names a publisher of the work. The platform it was collected from is recorded as provenance instead.
Content 3/5
-
Description
dcterms:description- source resource/summary
-
- The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing and control systems, and the demand for trustworthy, explainable, and reliable operation in high-stakes industrial environments. In this roadmap, we present a comprehensive perspective on the foundations, applications, and emerging directions of AI and ML in smart manufacturing. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in smart manufacturing. The second focuses on key topics where AI is already enabling advances, including industrial big data analytics, advanced sensing and perception, autonomous systems, additive and laser-based manufacturing, digital twins, robotics, supply chain and logistics optimization, and sustainable manufacturing. The third section explores non-traditional ML approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models for highly connected and complex manufacturing systems. By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. We hope this roadmap will serve as a guide for researchers, engineers, and practitioners to accelerate innovation, align academic and industrial priorities, and ensure that AI-driven smart manufacturing delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.
The abstract, and the depositor's additional notes after it when the source has a field for them.
-
Subject
dcterms:subject- source resource/categories/0
-
- cs.AI (arxiv)
- cs.LG (arxiv)
Declared keywords and taxonomy terms, each carrying its scheme.
-
Language
dcterms:language- language gate read the declared field
-
- en
The language of the content as the language gate read it.
-
Audience
dcterms:audience- mapped learning_resource_type/preprint
-
none found — the source did not declare it
Imported when the source declares an intended audience. Never inferred.
-
Education level
dcterms:educationLevel- mapped learning_resource_type/preprint
-
none found — the source did not declare it
Imported when the source declares an educational context.
Rights 3/4
-
Licence
dcterms:license- licence gate arXiv:arXiv/arXiv:license
-
- http://creativecommons.org/licenses/by/4.0/
The licence document, as a URI where the gate resolved one.
-
Rights
dcterms:rights- licence gate arXiv:arXiv/arXiv:license
-
- CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive
The verdict and the conditions, in words.
-
Rights holder
dcterms:rightsHolder -
none found — the source did not declare it
Only when named at source.
-
Access rights
dcterms:accessRights- source resource
-
- open
The access condition the source declared. Not the licence.
Form and dates 3/10
-
Format
dcterms:format- source resource
- conversion pdf
-
- application/pdf
- text/markdown
One value per form held — the original and the extracted text.
-
Extent
dcterms:extent- source resource
- conversion pdf
-
- 5588235 bytes (original)
- 342852 bytes (extracted text)
Sizes in bytes, each saying which form it measures.
-
Issued
dcterms:issued- source resource
-
- 2026-04-05
When the resource was published. Not when it was collected.
-
Modified
dcterms:modified- source resource
-
none found — the source did not declare it
When the source's own record was last changed.
-
Created
dcterms:created- source resource
-
none found — the source did not declare it
-
Accepted
dcterms:dateAccepted- source resource
-
none found — the source did not declare it
When a publisher accepted the work, where the source records it.
-
Submitted
dcterms:dateSubmitted- source resource
-
none found — the source did not declare it
When the work was submitted, where the source records it.
-
Available
dcterms:available- source resource
-
none found — the source did not declare it
-
Valid
dcterms:valid- source resource
-
none found — the source did not declare it
-
Bibliographic citation
dcterms:bibliographicCitation -
none found — the source did not declare it
Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.
Relations and custody 1/9
-
Is part of
dcterms:isPartOf -
none found — the source did not declare it
The repository, book or record this resource was found inside.
-
Has part
dcterms:hasPart -
none found — the source did not declare it
What this resource is made of, when the source lists its parts.
-
Is version of
dcterms:isVersionOf -
none found — the source did not declare it
-
Has version
dcterms:hasVersion -
none found — the source did not declare it
-
References
dcterms:references -
none found — the source did not declare it
Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.
-
Is referenced by
dcterms:isReferencedBy -
none found — the source did not declare it
What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.
-
Requires
dcterms:requires -
none found — the source did not declare it
What this resource needs in order to be used, when a source declares it. The files a lab works on are inferred, and stand under it apart.
-
Is required by
dcterms:isRequiredBy -
none found — the source did not declare it
What needs this resource, when a source declares it. The lab a component belongs to is inferred, and stands under it apart.
-
Provenance
dcterms:provenance- this engine source
- source resource
- conversion pdf
-
- Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
- Text extracted from pdf to Markdown by pdf; the original is retained unchanged beside it.
Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.
IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other. the standard ↗
1 General 5/8
-
Identifier
1.1- this engine resource_id
- source 2 assertions
-
- URI: tag:aim-pro.eu,2026:oer/9ebcf3235b27
- URI: https://arxiv.org/abs/2605.00839
- ARXIV: 2605.00839
- DOI: 10.1088/3049-4761/ae5967
This engine's identifier for the resource, the source URI, and any persistent identifier the source published.
-
Title
1.2- source resource/title
-
- 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
-
Language
1.3- language gate read the declared field
-
- en
The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.
-
Description
1.4- source resource/summary
-
- The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing and control systems, and the demand for trustworthy, explainable, and reliable operation in high-stakes industrial environments. In this roadmap, we present a comprehensive perspective on the foundations, applications, and emerging directions of AI and ML in smart manufacturing. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in smart manufacturing. The second focuses on key topics where AI is already enabling advances, including industrial big data analytics, advanced sensing and perception, autonomous systems, additive and laser-based manufacturing, digital twins, robotics, supply chain and logistics optimization, and sustainable manufacturing. The third section explores non-traditional ML approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models for highly connected and complex manufacturing systems. By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. We hope this roadmap will serve as a guide for researchers, engineers, and practitioners to accelerate innovation, align academic and industrial priorities, and ensure that AI-driven smart manufacturing delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.
The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.
-
Keyword
1.5- source resource/categories/0
-
- cs.AI
- cs.LG
-
Coverage
1.6 -
not collected — this library does not fill it
The time, place or culture the resource applies to. No source declares it.
-
Structure
1.7 -
not collected — this library does not fill it
Not declared by any of the three sources, and not estimated here.
-
Aggregation level
1.8 -
not collected — this library does not fill it
Not declared by any of the three sources, and not estimated here.
2 Life cycle 2/3
-
Version
2.1- source resource
-
- v1
-
Status
2.2 -
none found — the source did not declare it
Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.
-
Contribute
2.3- source 53 assertions
-
- Jay Lee — author
- Hanqi Su — author
- Marco Macchi — author
- Adalberto Polenghi — author
- Wei Wu — author
- Zhiheng Zhao — author
- George Q. Huang — author
- Kiva Allgood — author
- Devendra Jain — author
- Benedikt Gieger — author
- Vibhor Pandhare — author
- Soumyabrata Bhattacharjee — author
- Ram Mohril — author
- Lingbao Kong — author
- Qiyuan Wang — author
- Xinlan Tang — author
- Sungjong Kim — author
- Chan Hee Park — author
- Byeng D. Youn — author
- Guo Dong Goh — author
- Xi Huang — author
- Wai Yee Yeong — author
- Yung C Shin — author
- He Zhang — author
- Zitong Wang — author
- Fei Tao — author
- Jagjit Singh Srai — author
- Satyandra K. Gupta — author
- Byung Gun Joung — author
- Albin John — author
- John W. Sutherland — author
- Sang Won Lee — author
- Olga Fink — author
- Vinay Sharma — author
- Faez Ahmed — author
- Wei Chen — author
- Mark Fuge — author
- Arild Waaler — author
- Martin G. Skjæveland — author
- Dimitris Kyritsis — author
- Wei Chen — author
- VispiNevile Karkaria — author
- Yi-Ping Chen — author
- Ying-Kuan Tsai — author
- Joseph Cohen — author
- Xun Huan — author
- Jing Lin — author
- Liangwei Zhang — author
- Gregory W. Vogl — author
- Aaron W. Cornelius — author
- Xiaodong Jia — author
- Dai-Yan Ji — author
- Takanobu Minami — author
- Ruoxin Wang — author
Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.
3 Meta-metadata 4/4
-
Identifier
3.1- this engine resource_id
-
- URI: tag:aim-pro.eu,2026:oer/9ebcf3235b27/record
The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
-
Contribute
3.2- this engine source
-
- AIM-PRO WP3 OER harvester (arXiv) — creator
Who generated this record and when — a statement about the record, not about the resource.
-
Metadata schema
3.3 -
- LOMv1.0
- aimpro-oer-profile/1
LOMv1.0, and the profile this was built against.
-
Language
3.4- language gate read the declared field
-
- en
4 Technical 3/7
-
Format
4.1- source resource
- conversion pdf
-
- application/pdf
- text/markdown
One value per form held: the original as the source published it, and the Markdown this engine extracted.
-
Size
4.2- source resource
- conversion pdf
-
- 5588235
Bytes. The original's, because the resource is the file and not our conversion of it.
-
Location
4.3- this engine resource_id
-
- https://arxiv.org/abs/2605.00839
Where the source serves it.
-
Requirement
4.4 -
not collected — this library does not fill it
Software or hardware needed to use it. No source declares it.
-
Installation remarks
4.5 -
not collected — this library does not fill it
No source declares it.
-
Other platform requirements
4.6 -
not collected — this library does not fill it
No source declares it.
-
Duration
4.7 -
not collected — this library does not fill it
Playing time, for audio and video. The corpus holds neither.
5 Educational 1/11
-
Interactivity type
5.1 -
not collected — this library does not fill it
Active, expositive or mixed. A judgement about how the resource is used; no source declares it.
-
Learning resource type
5.2- source resource
- mapped learning_resource_type/preprint
-
- narrative text
Only from a declaration that maps onto LOM's vocabulary. A file format is not a declaration.
-
Interactivity level
5.3 -
not collected — this library does not fill it
No source declares it, and nothing here reads the resource to judge it.
-
Semantic density
5.4 -
not collected — this library does not fill it
No source declares it, and nothing here reads the resource to judge it.
-
Intended end user role
5.5- mapped learning_resource_type/preprint
-
none found — the source did not declare it
Imported when declared.
-
Context
5.6- mapped learning_resource_type/preprint
-
none found — the source did not declare it
Imported when declared.
-
Typical age range
5.7 -
not collected — this library does not fill it
No source declares it.
-
Difficulty
5.8- mapped learning_resource_type/preprint
-
none found — the source did not declare it
Imported when declared and never estimated.
-
Typical learning time
5.9- mapped learning_resource_type/preprint
-
none found — the source did not declare it
Imported when declared. Never computed from a word count.
-
Description
5.10- mapped learning_resource_type/preprint
-
none found — the source did not declare it
An explicit statement about educational use, when the source makes one.
-
Language
5.11 -
not collected — this library does not fill it
The language of the intended learner. Not assumed to be the content's: a lesson in English may be written for learners of it.
6 Rights 3/3
-
Cost
6.1- licence gate arXiv:arXiv/arXiv:license
-
- no — free to use
No: the licence gate refuses anything it cannot establish open reuse terms for, before the bytes are requested.
-
Copyright and other restrictions
6.2- licence gate arXiv:arXiv/arXiv:license
-
- yes — attribution required (CC-BY-4.0)
Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
-
Description
6.3- licence gate arXiv:arXiv/arXiv:license
-
- CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.
The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.
7 Relation 2/2
-
Kind
7.1 -
- hasformat
-
Resource
7.2 -
- Markdown extracted from the original by pdf
The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.
8 Annotation 0/3
-
Entity
8.1 -
not collected — this library does not fill it
Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.
-
Date
8.2 -
not collected — this library does not fill it
-
Description
8.3 -
not collected — this library does not fill it
9 Classification 0/4
-
Purpose
9.1 -
not collected — this library does not fill it
Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.
-
Taxon path
9.2 -
not collected — this library does not fill it
Where the competency framework goes. Empty in the record for the reason above.
-
Description
9.3 -
not collected — this library does not fill it
-
Keyword
9.4 -
not collected — this library does not fill it
What could not be established3
Where the source's metadata could not be carried over as it was — missing, contradictory, with no matching term in the standard, restructured, or taken from the repository — and what was done instead. Without these notes, an empty element would look like something the harvester missed.
| Status | Field | Why |
|---|---|---|
| Not available | rights_holder |
no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it |
| Not available | publisher |
the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim |
| Not available | educational |
the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them |