Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review | Paper Citation Record

Observation 9e941b47-b067-4a1b-a48c-c918e43672d6 · outbound

This paper cites Smart manufacturing systems: state of the art and future trends.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Smart manufacturing systems: state of the art and future trends

Reference 1

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This paper cites Data- driven smart manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Data- driven smart manufacturing

Reference 2

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This paper cites Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Reference 3

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Observation a45472aa-d45e-47ae-a3e6-a7add0b42d1f · outbound

This paper cites Review of in-situ processmonitoringandin-situmetrologyformetaladditive manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Review of in-situ processmonitoringandin-situmetrologyformetaladditive manufacturing

Reference 4

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This paper cites Vir- tualmanufacturinginindustry4.0: Areview.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Vir- tualmanufacturinginindustry4.0: Areview

Reference 5

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This paper cites Industrialar- tificial intelligence in industry 4.0-systematic review, chal- lenges and outlook.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Industrialar- tificial intelligence in industry 4.0-systematic review, chal- lenges and outlook

Reference 6

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This paper cites Artificialintelligenceforindustry4.0: Systematicre- viewofapplications,challenges,andopportunities.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Artificialintelligenceforindustry4.0: Systematicre- viewofapplications,challenges,andopportunities

Reference 7

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This paper cites A review of in-situ monitoring and process control system in metal-based laser additive manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review A review of in-situ monitoring and process control system in metal-based laser additive manufacturing

Reference 8

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Observation 6b5d82eb-a589-4ad6-a31b-da0ba0d83278 · outbound

This paper cites In-situ optical emis- sion spectroscopy of selective laser melting.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review In-situ optical emis- sion spectroscopy of selective laser melting

Reference 9

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Observation cc2a82b3-eb5d-4498-a3ac-a3ec20b0517b · outbound

This paper cites Extractionandevaluationofmelt pool, plume and spatter information for powder-bed fusion AM process monitoring.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Extractionandevaluationofmelt pool, plume and spatter information for powder-bed fusion AM process monitoring

Reference 10

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Observation 0f712806-3bff-43fb-87f5-c2e2ec7d7d3a · outbound

This paper cites Aerial additive manufacturing with multiple autonomousrobots.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Aerial additive manufacturing with multiple autonomousrobots

Reference 11

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Observation 6ea2c4a3-186c-4659-b4e1-f8369aa634b4 · outbound

This paper cites Cooperative aerial- ground multi-robot system for automated construction tasks.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Cooperative aerial- ground multi-robot system for automated construction tasks

Reference 12

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Observation 035b3d08-08e7-4619-baef-4a64033f0d44 · outbound

This paper cites Additivemanufacturing for space: status and promises.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Additivemanufacturing for space: status and promises

Reference 13

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This paper cites Challenges in the technology development for additive manufacturing in space.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Challenges in the technology development for additive manufacturing in space

Reference 14

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This paper cites Spatial-temporal modeling using deep learning for real-time monitoring of additive manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Spatial-temporal modeling using deep learning for real-time monitoring of additive manufacturing

Reference 15

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Observation 97b4e8fd-25ec-4806-8fe1-0cbb3718da59 · outbound

This paper cites AMTransformer: A Koopman theory-based transformer for learning additive manufacturing dynamics in laser processes.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review AMTransformer: A Koopman theory-based transformer for learning additive manufacturing dynamics in laser processes

Reference 16

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Observation a7c0cbe6-d42f-4f11-ac8d-d5451aed697b · outbound

This paper cites Linking pyrometry to porosity in additively manufactured metals.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Linking pyrometry to porosity in additively manufactured metals

Reference 17

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Observation 1dd05df9-1022-45ea-9b74-a4d757cffa6e · outbound

This paper cites Processmonitoringdataset fromtheadditivemanufacturingmetrologytestbed(ammt): Overhang part x4.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Processmonitoringdataset fromtheadditivemanufacturingmetrologytestbed(ammt): Overhang part x4

Reference 18

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Observation 465e5e71-2f54-4a18-9513-487c150c0dc2 · outbound

This paper cites In-process monitoring of porosity in additive manufacturing using optical emis- sionspectroscopy.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review In-process monitoring of porosity in additive manufacturing using optical emis- sionspectroscopy

Reference 19

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Observation 083c31ab-418f-493b-afb0-16d28a308e34 · outbound

This paper cites Detection of keyhole pore formations in laser powder-bed fusion us- ing acoustic process monitoring measurements.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Detection of keyhole pore formations in laser powder-bed fusion us- ing acoustic process monitoring measurements

Reference 20

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This paper cites An in situ crack detection approach in additive manufac- turing based on acoustic emission and machine learning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review An in situ crack detection approach in additive manufac- turing based on acoustic emission and machine learning

Reference 21

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Observation d932acd6-0a55-4cc9-8eab-06443ece22cb · outbound

This paper cites Investigating statistical correlation between multi- modality in-situ monitoring data for powder bed fusion ad- ditive manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Investigating statistical correlation between multi- modality in-situ monitoring data for powder bed fusion ad- ditive manufacturing

Reference 22

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Observation 439c9f61-9367-48e0-9441-18807edccea8 · outbound

This paper cites Multi-sensor monitoring for in-situ defect detectionandqualityassuranceinlaser-directedenergyde- position.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Multi-sensor monitoring for in-situ defect detectionandqualityassuranceinlaser-directedenergyde- position

Reference 23

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Observation 60714c57-a92b-4cba-8475-dd590db7b2c5 · outbound

This paper cites Unsupervised mul- timodal fusion of in-process sensor data for advanced man- ufacturing process monitoring.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Unsupervised mul- timodal fusion of in-process sensor data for advanced man- ufacturing process monitoring

Reference 24

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Observation 22e7282e-0d1c-4f62-8a8d-229f9087a849 · outbound

This paper cites Data-driven adaptive control for laser-based additive manufacturing with auto- matic controller tuning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Data-driven adaptive control for laser-based additive manufacturing with auto- matic controller tuning

Reference 25

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Observation c31a727a-8ecd-4e8e-98ec-1258f517d427 · outbound

This paper cites Machine learning in additive manufacturing: a re- view.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Machine learning in additive manufacturing: a re- view

Reference 26

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Observation 171dbf37-3d69-4094-a1b4-b4d0f3b7f548 · outbound

This paper cites On The Reliability Of Machine Learning Applications In Manufacturing Environments.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review On The Reliability Of Machine Learning Applications In Manufacturing Environments

Reference 27

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Observation e1c31d4d-b418-4b99-ae22-bbcc4c37837c · outbound

This paper cites In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Reference 28

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Observation eee6b9d7-e6c9-4965-bb2a-df461f482a72 · outbound

This paper cites Overcoming the limitations of adaptive control by means oflogic-basedswitching.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Overcoming the limitations of adaptive control by means oflogic-basedswitching

Reference 29

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Observation 5ce88442-8749-49b4-a0c9-678f8bd173de · outbound

This paper cites Process monitoring, diagnosis and control of additive manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Process monitoring, diagnosis and control of additive manufacturing

Reference 30

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Observation d39e9e1c-7845-461e-a7a1-1a7bc5bafffe · outbound

This paper cites A learn-and-control strategy for jet-based additive man- ufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review A learn-and-control strategy for jet-based additive man- ufacturing

Reference 31

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Observation 1268c61b-e5d0-4d77-9864-65c93948b2b3 · outbound

This paper cites Precise motion control of wafer stages via adaptiveneuralnetworkandfractional-ordersuper-twisting algorithm.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Precise motion control of wafer stages via adaptiveneuralnetworkandfractional-ordersuper-twisting algorithm

Reference 32

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Observation a523e0fe-560b-4c76-a416-425f9fc93584 · outbound

This paper cites Neural-network-based automatic trajectory adaptation for qualitycharacteristicscontrolinpowdercompaction.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Neural-network-based automatic trajectory adaptation for qualitycharacteristicscontrolinpowdercompaction

Reference 33

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Observation 4626f2bb-4270-45b1-af14-2e3dbc733fa2 · outbound

This paper cites A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing con- trol.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing con- trol

Reference 34

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Observation bf00790a-4aef-402a-9617-2baae5cfff3d · outbound

This paper cites Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing

Reference 35

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Observation 9affadcb-d412-471a-87db-240d1f3c3970 · outbound

This paper cites Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks

Reference 36

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Observation ccdba076-af56-414a-b73f-c05c23862993 · outbound

This paper cites Deep learning for smart manufactur- ing: Methods and applications.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Deep learning for smart manufactur- ing: Methods and applications

Reference 37

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Observation 7b667116-5d39-4d1b-a159-c84c4f29e9fb · outbound

This paper cites Industrial Artificial Intelligence for industry 4.0-based manufacturing systems.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

Reference 38

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Observation b6a93d11-eeb6-430d-b061-999e2b09e056 · outbound

This paper cites Adaptive policy learning for offline-to-online reinforcement learning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Adaptive policy learning for offline-to-online reinforcement learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.486192Z

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Observation ac88a3d8-4bdf-4de6-bbbe-e879367c610f · outbound

This paper cites Self-Supervised Meta-Learning for All-Layer DNN-Based Adaptive Control with Stability Guarantees.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Self-Supervised Meta-Learning for All-Layer DNN-Based Adaptive Control with Stability Guarantees

Reference 40

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Observation 84609fe5-953d-4b3d-ae0d-3ef30f56cc7f · outbound

This paper cites Data modeling and ML practice for enabling intelligent digital twins inadaptive productionplanning andcontrol.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Data modeling and ML practice for enabling intelligent digital twins inadaptive productionplanning andcontrol

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.470097Z

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Observation 1c2857a4-369f-49b7-9c39-c5111e2087c5 · outbound

This paper cites Alearning-basedframe- workforerrorcompensationin3Dprinting.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Alearning-basedframe- workforerrorcompensationin3Dprinting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.452814Z

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Observation 57d5276c-ff37-426d-b624-456c0f3ac2d6 · outbound

This paper cites In-Process monitoring of porosity during laser additive manufacturing process.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review In-Process monitoring of porosity during laser additive manufacturing process

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.436189Z

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Observation 4bccd315-a43d-4d0b-9a88-22e811603aea · outbound

This paper cites Optimal data-driven control of manufacturing processes usingreinforcementlearning: anapplicationtowirearcad- ditivemanufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Optimal data-driven control of manufacturing processes usingreinforcementlearning: anapplicationtowirearcad- ditivemanufacturing

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.420015Z

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Observation 0a8f17e0-9798-4cb2-87e6-bf56122355ff · outbound

This paper cites Designinganadaptive production control system using reinforcement learning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Designinganadaptive production control system using reinforcement learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.401942Z

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Observation f18f4023-a0ab-40b3-8f62-ecb1dd58bcce · outbound

This paper cites Deep Learning Agents for Efficient Dy- namic Production Control in Semiconductor Manufactur- ing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Deep Learning Agents for Efficient Dy- namic Production Control in Semiconductor Manufactur- ing

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.385786Z

Source-reported events for the cited work

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Observation fe884d6f-ec6a-4c99-9dae-fb897fc7a118 · outbound

This paper cites Avirtualmetrologysystemforsemiconductormanu- facturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Avirtualmetrologysystemforsemiconductormanu- facturing

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.369337Z

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Observation ff5f7c1a-cf9f-4614-a108-4eae11345fd1 · outbound

This paper cites Virtual metrology in semiconductor fabrication foundry usingdeeplearningneuralnetworks.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Virtual metrology in semiconductor fabrication foundry usingdeeplearningneuralnetworks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.352861Z

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Observation 2a5755d5-8f37-4546-95c1-066f4c21c365 · outbound

This paper cites Physics-informed online machine learning and predictive control of nonlinear pro- cesseswithparameteruncertainty.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Physics-informed online machine learning and predictive control of nonlinear pro- cesseswithparameteruncertainty

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.336432Z

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Observation 2535bfd5-fa66-4cd5-833b-737a4faa875b · outbound

This paper cites Hybrid ther- mal modeling of additive manufacturing processes using physics-informed neural networks for temperature predic- tionandparameteridentification.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Hybrid ther- mal modeling of additive manufacturing processes using physics-informed neural networks for temperature predic- tionandparameteridentification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.320157Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5316d57b-90bd-49ef-89e2-247c5034ea00 · outbound

This paper cites Un- certainty quantification for additive manufacturing process improvement: Recent advances.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Un- certainty quantification for additive manufacturing process improvement: Recent advances

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.303373Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2a501bf7-dcf7-447b-8911-64cf2b6b1ec8 · outbound

This paper cites Deep learning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Deep learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.286243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d56154f-affa-4e76-b286-9eab8e2f0c4d · outbound

This paper cites an unresolved cited work.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:51:07.271118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f8b0e86-cc47-48f4-a5e6-c1cac61107d2 · outbound

This paper cites Auto-Encoding Variational Bayes.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Auto-Encoding Variational Bayes

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.379989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a1dc96c-b493-45d0-89c4-06965129b63b · outbound

This paper cites Tutorial on Variational Autoencoders.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Tutorial on Variational Autoencoders

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.384950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cc8f62b-b86c-4e17-a8fa-6d4fa39d7e44 · outbound

This paper cites Embed to control: A locally lin- ear latent dynamics model for control from raw images.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Embed to control: A locally lin- ear latent dynamics model for control from raw images

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.253413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1a304283-e285-4952-aaf7-eebc3d0c2f84 · outbound

This paper cites Cautious model predictive control using gaussian process regression.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Cautious model predictive control using gaussian process regression

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.233252Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dab65deb-a002-4daa-9ae0-e75f030e0dec · outbound

This paper cites Generative adversarial nets.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Generative adversarial nets

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.214025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bc9cf703-814c-481e-a968-07649da18d48 · outbound

This paper cites McGAN: Generating manufacturable de- signs by embedding manufacturing rules into conditional generativeadversarialnetwork.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review McGAN: Generating manufacturable de- signs by embedding manufacturing rules into conditional generativeadversarialnetwork

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.195784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8afcd3bd-f73a-4595-b48a-afb1a0d03c2b · outbound

This paper cites Applications of generative adversarial net- works in anomaly detection: A systematic literature re- view.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Applications of generative adversarial net- works in anomaly detection: A systematic literature re- view

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.178181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 65ac37eb-eaa0-4021-9603-ebf030da170e · outbound

This paper cites DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:51:06.662112Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30ad86e0-fbd3-4b5f-b9e2-b20db96c4ac2 · outbound

This paper cites Improving language understanding by generative pre-training.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Improving language understanding by generative pre-training

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.158383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e06eff51-414d-4515-a326-e675faa2d07f · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.423237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cf04549-eb5e-4419-ae33-f4eed452851a · outbound

This paper cites Attentionisallyouneed.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Attentionisallyouneed

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.132956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 141b690b-e32a-4b08-9992-73b066a09245 · outbound

This paper cites Decision transformer: Rein- forcement learning via sequence modeling.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Decision transformer: Rein- forcement learning via sequence modeling

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.116211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e78aa22-590d-4eb7-828e-5a1fc6b7d603 · outbound

This paper cites Denoising diffusion probabilistic models.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Denoising diffusion probabilistic models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.438215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af7183ba-a1d9-4e6f-87b9-3bbdd1eb0160 · outbound

This paper cites Im- proved denoising diffusion probabilistic models.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Im- proved denoising diffusion probabilistic models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.086652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0b5dea9-6eec-4a0b-8254-a1cb299dcf90 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Score-Based Generative Modeling through Stochastic Differential Equations

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.447911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d321307-d6d5-4009-806e-a546dd9b8354 · outbound

This paper cites Planning with Diffusion for Flexible Behavior Synthesis.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Planning with Diffusion for Flexible Behavior Synthesis

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.452900Z

Source-reported events for the cited work

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Observation 269dac5d-86da-473b-aca8-232775cb5017 · outbound

This paper cites Aligning optimization trajectories with dif- fusionmodelsforconstraineddesigngeneration.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Aligning optimization trajectories with dif- fusionmodelsforconstraineddesigngeneration

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.068810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9e5f24cc-92f9-48ef-a453-f7d4e9e270d5 · outbound

This paper cites Adadiff: Accelerating diffu- sion models through step-wise adaptive computation.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Adadiff: Accelerating diffu- sion models through step-wise adaptive computation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.045707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c34dcdc2-37b1-42e3-a1f7-bf356116b2ec · outbound

This paper cites Diffusionmod- elsbeatgansonimagesynthesis.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Diffusionmod- elsbeatgansonimagesynthesis

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.027306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e455ab4d-8867-443a-8d0a-f0e9936092f2 · outbound

This paper cites Multidiffusion: Fusingdiffusionpathsforcontrolledimage generation.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Multidiffusion: Fusingdiffusionpathsforcontrolledimage generation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:07.006186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ee3cad8-bdcc-4dce-91d1-748423427519 · outbound

This paper cites Gen- erative adversarial networks (GAN) model for dynamically adjusted weld pool image toward human-based model pre- dictive control (MPC).

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Gen- erative adversarial networks (GAN) model for dynamically adjusted weld pool image toward human-based model pre- dictive control (MPC)

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.988350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 105f4c0a-df97-49c4-98fa-b50f02ac97a2 · outbound

This paper cites Atransformer-baseddeepreinforcement learningapproachfordynamicparallelmachinescheduling problem with family setups.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Atransformer-baseddeepreinforcement learningapproachfordynamicparallelmachinescheduling problem with family setups

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.971244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5a74f1fe-7f97-4c92-8277-6346496dc5e4 · outbound

This paper cites Onlinedistor- tion simulation using generative machine learning models: A step toward digital twin of metallic additive manufactur- ing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Onlinedistor- tion simulation using generative machine learning models: A step toward digital twin of metallic additive manufactur- ing

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.953940Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1b8fcde6-0fc8-41f6-879a-2980edf302dc · outbound

This paper cites Virtual surface morphology generation of Ti-6Al-4V di- rected energy deposition via conditional generative adver- sarial network.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Virtual surface morphology generation of Ti-6Al-4V di- rected energy deposition via conditional generative adver- sarial network

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.935144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d7767ef-6c9e-45c9-a794-bd121863735a · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Diffusion policy: Visuomotor policy learning via action diffusion

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.917425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 494648db-1aa8-4b05-948f-0db583032fec · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review RT-1: Robotics Transformer for Real-World Control at Scale

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:06.501407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd7d9309-fbc7-4deb-bac4-8d09e3fe325b · outbound

This paper cites Perceiver-actor: A multi-task transformer for robotic ma- nipulation.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Perceiver-actor: A multi-task transformer for robotic ma- nipulation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.899935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b26464fc-0a72-4e93-8673-94fdd986adac · outbound

This paper cites Transformer-based imitative reinforcement learning for multirobot path planning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Transformer-based imitative reinforcement learning for multirobot path planning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.883680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation adb7df42-8707-4091-9c99-d8973f404ea8 · outbound

This paper cites Multimodal vae ac- tive inference controller.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Multimodal vae ac- tive inference controller

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.867264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3c0c0c4-b9ec-4fff-966c-003b1bd2dace · outbound

This paper cites Dall-e-bot: Introducing web-scale diffusion models to robotics.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Dall-e-bot: Introducing web-scale diffusion models to robotics

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.849454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ede3958-afed-4ed7-92ab-49915f495ddf · outbound

This paper cites Towards autonomous system: flexible modu- lar production system enhanced with large language model agents.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Towards autonomous system: flexible modu- lar production system enhanced with large language model agents

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.832018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0f84390a-431e-4002-a3a6-3fe9fbbd5950 · outbound

This paper cites Implicit behavioral cloning.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Implicit behavioral cloning

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.815304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06c6d25c-bcce-45f5-911a-1cf2f05cf01e · outbound

This paper cites Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.799048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4423bfd-c1f2-40a3-95b3-8a3d743b0ddc · outbound

This paper cites Digital twin-based cyber physical production system architectural framework for personalized produc- tion.

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review Digital twin-based cyber physical production system architectural framework for personalized produc- tion

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:51:06.781332Z

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