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REVIEW 4 major objections 4 minor 87 references

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

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This review argues that generative machine learning in manufacturing should move from predicting process states to directly issuing control policies in integrated frameworks.

desk verdict A useful taxonomy and a mostly sound review, but the central gap claim rests on a thin, undisclosed sample and should be softened or substantiated before the paper steers the field. read the letter →

arxiv 2505.00210 v2 pith:5ADURJU3 submitted 2025-04-30 cs.LG cs.CEcs.SYeess.SY

Suk Ki Lee , Hyunwoong Ko This is my paper · ORCID
classification cs.LGcs.CEcs.SYeess.SY
keywords generativemachinelearningadaptivecontroldynamicmanufacturingvariationalautoencodersadversarialnetworkstransformersdiffusionmodelsdigitaltwin
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Generative machine learning—models that learn the probability distribution of data and sample new, realistic examples from it—has been applied to dynamic manufacturing almost entirely as a forecaster. This review claims that such models currently generate predictions, such as future weld-pool images or distortion fields, that are handed to a separate control system, rather than generating the control actions themselves. The paper's contribution is a four-way functional classification of ML-enhanced control—Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated—and a mapping of variational autoencoders, GANs, transformers, and diffusion models onto it. The central thesis is that the next step is to build integrated frameworks in which generative models directly produce control policies while respecting manufacturing constraints. If the thesis is right, adaptive manufacturing control could use the probabilistic reasoning and scenario generation of generative models to handle uncertainty and rare faults that conventional controllers miss.

What carries the argument

The carrying mechanism is the paper's four-way functional classification of ML-enhanced control—Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated—used as a lens for asking how information flows from sensor data to control decisions. The classification does the work of making the generation-control separation visible: when each generative architecture is placed in the taxonomy, its outputs land on the prediction or inference side, not the policy side. The paper pairs this lens with control-relevant properties of the four generative families—latent-space compression and uncertainty bounds for VAEs, implicit distribution learning and synthetic fault generation for GANs, long-range attention and interpretability for transformers, and iterative constraint-guided trajectory generation for diffusion models—to argue that the missing integration is technically plausible.

What would settle it

A systematic literature search with explicit inclusion criteria that uncovers multiple deployed manufacturing control loops in which a generative model directly outputs control actions in a closed loop—rather than feeding predictions to a separate controller—would undercut the paper's central separation gap. Finding no such cases would support it.

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Extended reading notes

Core claim

The review's core discovery is a functional gap rather than a new algorithm: in current manufacturing practice, generative ML mostly acts as a predictive module inside a larger control loop. Surveying the field through its four functional categories, the paper shows that existing ML-enhanced control either forecasts future states (Prediction-Based), learns state-to-action mappings (Direct Policy), infers unmeasurable quality variables (Quality Inference), or embeds physical knowledge into the model (Knowledge-Integrated). Generative models map onto these categories mainly on the prediction and inference side—GANs and diffusion models synthesize future process images, distortion fields, and surface morphology—while direct policy generation remains rare. The authors therefore assert that the field's next step is to make generative models themselves the control policy generators, combining the uncertainty awareness of generative architectures with the direct-action capability of reinforcement learning, and to do so with physics-informed, purpose-built, computationally tractable models.

Load-bearing premise

The review's taxonomy and gap analysis assume that the four generative architectures and the nine illustrative control examples in Table 1 faithfully represent the whole field of generative ML for manufacturing control, but no systematic search or inclusion criteria are given.

Editorial extensions

If this is right

  • If generative models directly emit control policies, adaptive manufacturing systems can respond to in-situ sensor feedback in real time without waiting for a separate controller's prediction-and-optimization step.
  • Embedding manufacturing physics as explicit constraints in generative architectures would shift the field from pattern mimicry to process understanding, improving reliability of generated trajectories.
  • Purpose-built generative models designed for manufacturing, rather than architectures adapted from image generation or language processing, would more naturally respect manufacturing-specific constraints and quality requirements.
  • Model compression and architectural improvements will be needed to reconcile the computational cost of iterative generative methods with real-time manufacturing requirements.
  • Hybrid frameworks that combine Prediction-Based or Quality Inference strengths with Direct Policy action generation could enable simultaneous optimization of quality, efficiency, and adaptability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The review's own selection of nine illustrative examples and four architectures is not justified by a systematic search, so a broader literature scan could change the taxonomy or shift the claimed gap; this is my inference from the absence of inclusion criteria, not a finding the paper reports.
  • Robotics already demonstrates closed-loop generative policies (for example diffusion-based visuomotor policies), which suggests the transfer barrier to manufacturing may be less about the generative mechanism itself and more about physical process models, safety constraints, and real-time latency.
  • If integrated generative controllers mature, the four-way classification may need a fifth category for models that simultaneously infer quality and emit actions, since the proposed hybrid direction blurs the boundary between Prediction-Based and Direct Policy control.
  • A concrete benchmark would compare a diffusion-based or VAE-based policy against a deterministic neural-network policy under out-of-distribution disturbances in a simulated manufacturing process; the generative models' uncertainty accounting should show an advantage exactly where the distribution shifts.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript is a review paper that proposes a functional classification of ML-enhanced control in dynamic manufacturing processes—Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated—and then examines four generative ML architectures (VAEs, GANs, Transformers, Diffusion models) for their control-relevant properties. The central claim is that current integrations of generative ML in manufacturing are primarily predictive, feeding separate control systems rather than directly producing control policies, leading to three research gaps: separation of generation and control, insufficient physical understanding, and domain adaptation challenges. The paper concludes with four future research directions, the first being integrated frameworks in which generative models produce control policies. The standard equations for each generative architecture are presented correctly, and the illustrative applications, while few, are on-topic.

Significance. If the gap analysis were rigorously established, the review would provide a useful roadmap for a growing research area at the intersection of generative modeling and manufacturing control. The paper has strengths: the standard formulations of VAE, GAN, attention, and diffusion are correctly summarized; the proposed functional classification is a sensible organizing scheme; and the concrete applications cited—weld pool forecasting, distortion simulation, surface morphology prediction, and transformer-DRL scheduling—genuinely fit the categories. The authors also explicitly acknowledge limitations of current approaches, which is appropriate for a review. However, the review's load-bearing empirical generalization about the primacy of predictive integrations is not backed by a systematic corpus, and several control-relevant properties are asserted without direct evidence. The central future-work recommendation therefore rests on a claim that the paper's own cited transfer examples partially contradict. The significance of the review is conditional on substantially strengthening the evidence base and sharpening the claims.

major comments (4)
  1. [§5.4, Gap (1); §6, direction 1] The central claim that current generative ML integrations in manufacturing 'primarily produce predictive outputs that serve as inputs to separate control systems rather than directly producing control strategies themselves' is not supported by a disclosed systematic corpus, and the manuscript's own evidence undermines it. Section 5.3 cites Diffusion Policy [78] and RT-1 [79] as transferable approaches, and both are direct-policy generative controllers in the paper's own sense. The authors should either provide a systematic literature search with inclusion criteria demonstrating the predominance of predictive uses in manufacturing specifically, or articulate a precise manufacturing-specific barrier (e.g., safety constraints, sample efficiency, real-time latency) that prevents direct-policy generation in this domain. Without this, the primary future-work recommendation in Section 6, direction 1, is an unsupported empirical generalization.
  2. [§3.2, Table 1; end of §3.2] The functional classification is presented as a framework for 'incorporating generative ML,' but the nine examples in Table 1 are all non-generative methods (CNN, RL, SVR, physics-informed NN) in the distribution-modeling sense defined in Section 4. The claim at the end of Section 3.2 that generative ML 'offer solutions through their inherent probabilistic frameworks' is therefore not actually demonstrated by the taxonomy. The authors should either add generative examples to Table 1 (for instance, the generative applications surveyed in Section 5), or explicitly state that the taxonomy currently only covers conventional ML and that extending it to generative models is a hypothesis, not an observed regularity.
  3. [§5.1 vs. §4.3] There is a terminological inconsistency in the treatment of Transformers. Section 4.3 correctly states that 'Transformers are not inherently generative,' yet Section 5.1 describes the transformer-based scheduling system [75] as 'a generative ML model.' If the authors intend to count auto-regressive sequence generation as an instance of generative modeling, they should say so explicitly and reconcile this with the earlier caveat; otherwise the classification of [75] as a generative integration is misleading.
  4. [§4.2, property (1)] The claim that GANs 'can produce synthetic data that adheres to physical constraints without requiring these constraints to be explicitly encoded' is asserted without citation or demonstration. This is not obvious, and it is in tension with Gap (2) in Section 5.4, which states that current approaches rely on pattern mimicry 'without deeper process understanding.' The authors should either provide evidence for the constraint-adherence claim or temper it to reflect that physical consistency is not guaranteed and must be evaluated.
minor comments (4)
  1. [Abstract] The abstract states that the review 'presents a functional classification' but the classification in Section 3.2 is for ML-enhanced control generally, not for generative ML specifically; a phrase clarifying this scope would prevent over-reading.
  2. [Section 5.2] The statement that existing studies 'primarily align with the Quality Inference control approaches' is not quantitatively supported; only three generative simulation/digital-twin examples are described. A sentence acknowledging the small sample would be more accurate.
  3. [Figure 1] Figure 1 is referenced in the text but not present in the provided manuscript; if the figure is common to all architectures, a brief caption description would help readers interpret the properties diagram.
  4. [Section 3.1] The phrase 'Even though, in recent times, conventional ML approaches...' is grammatically awkward and could be rewritten as 'Although conventional ML approaches...'.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation; the only circularity signal is two minor non-load-bearing self-citations.

full rationale

This is a review paper with no fitted parameters, no predictive equations derived from a fit, and no uniqueness argument. Its central content—the Section 3.2 functional taxonomy, the Section 4 architecture survey, and the Section 5.4 research-gap analysis—is a classification and qualitative synthesis of cited external literature, not a derivation from the paper's own assumptions. The generative model equations (Eqs. 1-8) are standard textbook definitions (VAE ELBO, GAN objective, attention, diffusion), and none is set equal to a later 'prediction.' The only in-house references are [15] (Ko et al.) and [16] (Lee and Ko), cited in Section 2.2 for the general value of ML in in-situ monitoring and in Section 4.3 for transformer attention properties. Those citations are supporting illustrations, not load-bearing: the transformer properties are independently captured by Eq. 5 and refs [62], [64], [65], and the gap claim that current integrations are 'primarily predictive' rests on the surveyed examples in Section 5, not on [15] or [16]. The absence of a disclosed systematic search protocol is a methodological completeness concern about corpus representativeness, not a circularity concern, because the review makes claims about the papers it surveys rather than deriving those claims from a self-referential premise. Score 2 reflects the two minor self-citations; the central derivation chain is otherwise self-contained.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities, fitted parameters, or formal derivations. Its burden comes from assumptions that the selected architectures and examples are representative, plus several qualitative claims about generative model properties that are treated as established.

assumptions (3)
  • domain assumption The four surveyed architectures (VAE, GAN, Transformer, Diffusion) are representative of generative ML for manufacturing control.
    The review generalizes control-relevant properties from these four architectures without justifying their representatives or systematically excluding other generative families such as flow matching or autoregressive models.
  • domain assumption Attention weights are a valid proxy for model interpretability in control decisions.
    Section 4.3 claims attention reveals which prior inputs most strongly influence output. Attention interpretability is contested in the ML literature and is not established for control use.
  • domain assumption GANs can generate data that adhere to physical constraints without explicit constraint encoding.
    Section 4.2 asserts this without a citation or demonstration. The claim is load-bearing because it supports a stated advantage of GANs for manufacturing data.

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Cite this review

Pith. "Pith review of Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review." pith.science (2026). https://pith.science/paper/5ADURJU3

@misc{pith2026250500210,
  author       = {Pith},
  title        = {Pith review of: Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ADURJU3}},
  note         = {Machine review of arXiv:2505.00210}
}
read the original abstract

Dynamic manufacturing processes exhibit complex characteristics defined by time-varying parameters, nonlinear behaviors, and uncertainties. These characteristics require sophisticated in-situ monitoring techniques utilizing multimodal sensor data and adaptive control systems that can respond to real-time feedback while maintaining product quality. Recently, generative machine learning (ML) has emerged as a powerful tool for modeling complex distributions and generating synthetic data while handling these manufacturing uncertainties. However, adopting these generative technologies in dynamic manufacturing systems lacks a functional control-oriented perspective to translate their probabilistic understanding into actionable process controls while respecting constraints. This review presents a functional classification of Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated approaches, offering a perspective for understanding existing ML-enhanced control systems and incorporating generative ML. The analysis of generative ML architectures within this framework demonstrates control-relevant properties and potential to extend current ML-enhanced approaches where conventional methods prove insufficient. We show generative ML's potential for manufacturing control through decision-making applications, process guidance, simulation, and digital twins, while identifying critical research gaps: separation between generation and control functions, insufficient physical understanding of manufacturing phenomena, and challenges adapting models from other domains. To address these challenges, we propose future research directions aimed at developing integrated frameworks that combine generative ML and control technologies to address the dynamic complexities of modern manufacturing systems.

Figures

Figures reproduced from arXiv: 2505.00210 by the authors.

Figure 1
Figure 1. FIGURE 1: GENERATIVE ML OVERVIEW WITH MODEL-SPECIFIC [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗

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Reference graph

Works this paper leans on

87 extracted references · 69 canonical work pages

  1. [78]

    Diffusion policy: Visuomotor policy learning via action diffusion

    Chi, Cheng, Xu, Zhenjia, Feng, Siyuan, Cousineau, Eric, Du, Yilun, Burchfiel, Benjamin, Tedrake, Russ and Song, Shuran. “Diffusion policy: Visuomotor policy learning via action diffusion.” The International Journal of Robotics Research(2023): p. 02783649241273668

    work page 2023

  2. [79]

    Rt-1: Robotics transformer for real-world control at scale

    Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebo- tar, Yevgen, Dabis, Joseph, Finn, Chelsea, Gopalakrishnan, Keerthana, Hausman, Karol, Herzog, Alex, Hsu, Jasmine et al. “Rt-1: Robotics transformer for real-world control at scale.” arXiv preprint arXiv:2212.06817(2022)

    arXiv 2022

  3. [75]

    Atransformer-baseddeepreinforcement learningapproachfordynamicparallelmachinescheduling problem with family setups

    Li, Funing, Lang, Sebastian, Tian, Yuan, Hong, Bingyuan, Rolf, Benjamin, Noortwyck, Ruben, Schulz, Robert and Reggelin,Tobias. “Atransformer-baseddeepreinforcement learningapproachfordynamicparallelmachinescheduling problem with family setups.”Journal of Intelligent Manu- facturing(2024): pp. 1–34

    work page 2024

  4. [1]

    Smart manufacturing systems: state of the art and future trends

    Qu, YJ, Ming, XG, Liu, ZW, Zhang, XY and Hou, ZT. “Smart manufacturing systems: state of the art and future trends.” The International Journal of Advanced Manufac- turing TechnologyVol. 103 (2019): pp. 3751–3768

    2019

  5. [2]

    Data- driven smart manufacturing

    Tao,Fei,Qi,Qinglin,Liu,AngandKusiak,Andrew. “Data- driven smart manufacturing.” Journal of Manufacturing SystemsVol. 48 (2018): pp. 157–169

    2018

  6. [3]

    Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

    Lu,Yuqian,Liu,Chao,Kevin,I,Wang,Kai,Huang,Huiyue and Xu, Xun. “Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues.” Robotics and computer-integrated manufacturing Vol. 61 (2020): p. 101837

    2020

  7. [4]

    Review of in-situ processmonitoringandin-situmetrologyformetaladditive manufacturing

    Everton, Sarah K, Hirsch, Matthias, Stravroulakis, Petros, Leach, Richard K and Clare, Adam T. “Review of in-situ processmonitoringandin-situmetrologyformetaladditive manufacturing.” Materials & DesignVol. 95 (2016): pp. 431–445

    2016

  8. [5]

    Vir- tualmanufacturinginindustry4.0: Areview

    Soori, Mohsen, Arezoo, Behrooz and Dastres, Roza. “Vir- tualmanufacturinginindustry4.0: Areview.” DataScience and ManagementVol. 7 No. 1 (2024): pp. 47–63

    2024

Show all 87 references
  1. [6]

    Industrialar- tificial intelligence in industry 4.0-systematic review, chal- lenges and outlook

    Peres, Ricardo Silva, Jia, Xiaodong, Lee, Jay, Sun, Keyi, Colombo,ArmandoWalterandBarata,Jose. “Industrialar- tificial intelligence in industry 4.0-systematic review, chal- lenges and outlook.” IEEE access Vol. 8 (2020): pp. 220121–220139

    2020

  2. [7]

    Artificialintelligenceforindustry4.0: Systematicre- viewofapplications,challenges,andopportunities

    Jan, Zohaib, Ahamed, Farhad, Mayer, Wolfgang, Patel, Niki, Grossmann, Georg, Stumptner, Markus and Kuusk, Ana. “Artificialintelligenceforindustry4.0: Systematicre- viewofapplications,challenges,andopportunities.” Expert Systems with ApplicationsVol. 216 (2023): p. 119456

    2023

  3. [8]

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

    Cai, Yuhua, Xiong, Jun, Chen, Hui and Zhang, Guangjun. “A review of in-situ monitoring and process control system in metal-based laser additive manufacturing.”Journal of Manufacturing SystemsVol. 70 (2023): pp. 309–326

    2023

  4. [9]

    In-situ optical emis- sion spectroscopy of selective laser melting

    Lough, Cody S, Escano, Luis I, Qu, Minglei, Smith, Christopher C, Landers, Robert G, Bristow, Douglas A, Chen, Lianyi and Kinzel, Edward C. “In-situ optical emis- sion spectroscopy of selective laser melting.”Journal of Manufacturing ProcessesVol. 53 (2020): pp. 336–341

    2020

  5. [10]

    Extractionandevaluationofmelt pool, plume and spatter information for powder-bed fusion AM process monitoring

    Zhang,Yingjie,Hong,GeokSoon,Ye,Dongsen,Zhu,Kun- pengandFuh,JerryYH. “Extractionandevaluationofmelt pool, plume and spatter information for powder-bed fusion AM process monitoring.” Materials & DesignVol. 156 (2018): pp. 458–469

    2018

  6. [11]

    Aerial additive manufacturing with multiple autonomousrobots

    Zhang, Ketao, Chermprayong, Pisak, Xiao, Feng, Tzoumanikas, Dimos, Dams, Barrie, Kay, Sebastian, Ko- cer, Basaran Bahadir, Burns, Alec, Orr, Lachlan, Alhinai, Talib et al. “Aerial additive manufacturing with multiple autonomousrobots.” NatureVol.609No.7928(2022): pp. 709–717

    2022

  7. [12]

    Cooperative aerial- ground multi-robot system for automated construction tasks

    Krizmancic, Marko, Arbanas, Barbara, Petrovic, Tamara, Petric, Frano and Bogdan, Stjepan. “Cooperative aerial- ground multi-robot system for automated construction tasks.” IEEERoboticsandAutomationLetters Vol.5No.2 (2020): pp. 798–805

    2020

  8. [13]

    Additivemanufacturing for space: status and promises

    Sacco,EneaandMoon,SeungKi. “Additivemanufacturing for space: status and promises.”The International Journal of Advanced Manufacturing TechnologyVol. 105 (2019): pp. 4123–4146

    2019

  9. [14]

    Challenges in the technology development for additive manufacturing in space

    Zocca, Andrea, Wilbig, Janka, Waske, Anja, Günster, Jens,Widjaja,MartinusPutra,Neumann,Christian,Clozel, Mélanie,Meyer,Andreas,Ding,Jifeng,Zhou,Zuoxinetal. “Challenges in the technology development for additive manufacturing in space.” Chinese Journal of Mechani- cal Engineeri...

    2022

  10. [15]

    Spatial-temporal modeling using deep learning for real-time monitoring of additive manufacturing

    Ko, Hyunwoong, Kim, Jaehyuk, Lu, Yan, Shin, Dongmin, Yang, Zhuo and Oh, Yosep. “Spatial-temporal modeling using deep learning for real-time monitoring of additive manufacturing.” InternationalDesign EngineeringTechni- cal Conferences and Computers and Information in Engi- neer...

    2022

  11. [16]

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

    Lee, Suk Ki and Ko, Hyunwoong. “AMTransformer: A Koopman theory-based transformer for learning additive manufacturing dynamics in laser processes.”International JournalofAIforMaterialsandDesign Vol.1No.2(2024): pp. 76–91

    2024

  12. [17]

    Linking pyrometry to porosity in additively manufactured metals

    Mitchell, John A, Ivanoff, Thomas A, Dagel, Daryl, Madi- son, Jonathan D and Jared, Bradley. “Linking pyrometry to porosity in additively manufactured metals.”Additive ManufacturingVol. 31 (2020): p. 100946

    2020

  13. [18]

    Processmonitoringdataset fromtheadditivemanufacturingmetrologytestbed(ammt): Overhang part x4

    Lane,BrandonandYeung,Ho. “Processmonitoringdataset fromtheadditivemanufacturingmetrologytestbed(ammt): Overhang part x4.” Journal of research of the National Institute of Standards and TechnologyVol. 125 (2020): p. 125027

    2020

  14. [19]

    In-process monitoring of porosity in additive manufacturing using optical emis- sionspectroscopy

    Montazeri, Mohammad, Nassar, Abdalla R, Dunbar, Alexander J and Rao, Prahalada. “In-process monitoring of porosity in additive manufacturing using optical emis- sionspectroscopy.” IiseTransactions Vol.52No.5(2020): pp. 500–515. 9

    2020

  15. [20]

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

    Tempelman, Joshua R, Wachtor, Adam J, Flynn, Eric B, Depond, Phillip J, Forien, Jean-Baptiste, Guss, Gabe M, Calta, Nicholas P and Matthews, Manyalibo J. “Detection of keyhole pore formations in laser powder-bed fusion us- ing acoustic process monitoring measurements.”Additive...

    2022

  16. [21]

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

    Kononenko, Denys Y, Nikonova, Viktoriia, Seleznev, Mikhail, van den Brink, Jeroen and Chernyavsky, Dmitry. “An in situ crack detection approach in additive manufac- turing based on acoustic emission and machine learning.” Additive manufacturing lettersVol. 5 (2023): p. 100130

    2023

  17. [22]

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

    Yang, Zhuo, Adnan, Muhammad, Lu, Yan, Cheng, Fan- Tien, Yang, Haw-Ching, Perisic, Milica and Ndiaye, Yande. “Investigating statistical correlation between multi- modality in-situ monitoring data for powder bed fusion ad- ditive manufacturing.”2022 IEEE 18th International Con- ...

    2022

  18. [23]

    Multi-sensor monitoring for in-situ defect detectionandqualityassuranceinlaser-directedenergyde- position

    Chen, Lequn. “Multi-sensor monitoring for in-situ defect detectionandqualityassuranceinlaser-directedenergyde- position.” (2024)

    2024

  19. [24]

    Unsupervised mul- timodal fusion of in-process sensor data for advanced man- ufacturing process monitoring

    McKinney, Matthew, Garland, Anthony, Cillessen, Dale, Adamczyk, Jesse, Bolintineanu, Dan, Heiden, Michael, Fowler, Elliott and Boyce, Brad L. “Unsupervised mul- timodal fusion of in-process sensor data for advanced man- ufacturing process monitoring.”Journal of Manufacturing S...

    2025

  20. [25]

    Data-driven adaptive control for laser-based additive manufacturing with auto- matic controller tuning

    Chen, Lequn, Yao, Xiling, Chew, Youxiang, Weng, Fei, Moon, Seung Ki and Bi, Guijun. “Data-driven adaptive control for laser-based additive manufacturing with auto- matic controller tuning.”Applied SciencesVol. 10 No. 22 (2020): p. 7967

    2020

  21. [26]

    Machine learning in additive manufacturing: a re- view

    Meng,Lingbin,McWilliams,Brandon,Jarosinski,William, Park, Hye-Yeong, Jung, Yeon-Gil, Lee, Jehyun and Zhang, Jing. “Machine learning in additive manufacturing: a re- view.” JomVol. 72 (2020): pp. 2363–2377

    2020

  22. [27]

    On the reliability of machine learning appli- cations in manufacturing environments

    Jourdan, Nicolas, Sen, Sagar, Husom, Erik Johannes, Garcia-Ceja, Enrique, Biegel, Tobias and Metternich, Joachim. “On the reliability of machine learning appli- cations in manufacturing environments.” arXiv preprint arXiv:2112.06986(2021)

    2021 arXiv

  23. [28]

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

    Wang,Tianjiao,Kwok,Tsz-Ho,Zhou,ChiandVader,Scott. “In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing.”Jour- nal of manufacturing systemsVol. 47 (2018): pp. 83–92

    2018

  24. [29]

    Overcoming the limitations of adaptive control by means oflogic-basedswitching

    Hespanha,JoaoP,Liberzon,DanielandMorse,AStephen. “Overcoming the limitations of adaptive control by means oflogic-basedswitching.” Systems&controlletters Vol.49 No. 1 (2003): pp. 49–65

    2003

  25. [30]

    Process monitoring, diagnosis and control of additive manufacturing

    Fang, Qihang, Xiong, Gang, Zhou, MengChu, Tamir, Tariku Sinshaw, Yan, Chao-Bo, Wu, Huaiyu, Shen, Zhen and Wang, Fei-Yue. “Process monitoring, diagnosis and control of additive manufacturing.”IEEE Transactions on AutomationScienceandEngineering Vol.21No.1(2022): pp. 1041–1067

    2022

  26. [31]

    A learn-and-control strategy for jet-based additive man- ufacturing

    Inyang-Udoh, Uduak, Chen, Alvin and Mishra, Sandipan. “A learn-and-control strategy for jet-based additive man- ufacturing.” IEEE/ASME Transactions on Mechatronics Vol. 27 No. 4 (2022): pp. 1946–1954

    2022

  27. [32]

    Precise motion control of wafer stages via adaptiveneuralnetworkandfractional-ordersuper-twisting algorithm

    Kuang, Zhian, Sun, Liting, Gao, Huijun and Tomizuka, Masayoshi. “Precise motion control of wafer stages via adaptiveneuralnetworkandfractional-ordersuper-twisting algorithm.” IFAC-PapersOnLineVol. 53 No. 2 (2020): pp. 8315–8320

    2020

  28. [33]

    Neural-network-based automatic trajectory adaptation for qualitycharacteristicscontrolinpowdercompaction

    MoradiMaryamnegari, Hoomaan, Hasseni, Seif-El-Islam, Ganthaler, Elias, Villgrattner, Thomas and Peer, Angelika. “Neural-network-based automatic trajectory adaptation for qualitycharacteristicscontrolinpowdercompaction.” Jour- nal of Intelligent ManufacturingVol. 36 No. 2 (2025...

    2025

  29. [34]

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

    Humfeld, Keith D, Gu, Dawei, Butler, Geoffrey A, Nelson, Karl and Zobeiry, Navid. “A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing con- trol.” Composites Part B: EngineeringVol. 223 (20...

    2021

  30. [35]

    Deep neural operator enabled digital twin modeling for additive manufacturing

    Liu, Ning, Li, Xuxiao, Rajanna, Manoj R, Reutzel, Ed- ward W, Sawyer, Brady, Rao, Prahalada, Lua, Jim, Phan, Nam and Yu, Yue. “Deep neural operator enabled digital twin modeling for additive manufacturing.”arXiv preprint arXiv:2405.09572(2024)

    2024 arXiv

  31. [36]

    Real-time decision-making for digital twin in additive manufacturing with model predictive control using time-series deep neural networks

    Chen, Yi-Ping, Karkaria, Vispi, Tsai, Ying-Kuan, Ro- lark, Faith, Quispe, Daniel, Gao, Robert X, Cao, Jian and Chen, Wei. “Real-time decision-making for digital twin in additive manufacturing with model predictive control using time-series deep neural networks.” arXiv preprint...

    2025 arXiv

  32. [37]

    Deep learning for smart manufactur- ing: Methods and applications

    Wang, Jinjiang, Ma, Yulin, Zhang, Laibin, Gao, Robert X and Wu, Dazhong. “Deep learning for smart manufactur- ing: Methods and applications.”Journal of manufacturing systemsVol. 48 (2018): pp. 144–156

    2018

  33. [38]

    Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

    Lee, Jay, Davari, Hossein, Singh, Jaskaran and Pand- hare, Vibhor. “Industrial Artificial Intelligence for industry 4.0-based manufacturing systems.”Manufacturing letters Vol. 18 (2018): pp. 20–23

    2018

  34. [39]

    Adaptive policy learning for offline-to-online reinforcement learning

    Zheng, Han, Luo, Xufang, Wei, Pengfei, Song, Xuan, Li, Dongsheng and Jiang, Jing. “Adaptive policy learning for offline-to-online reinforcement learning.” Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 9: pp. 11372–11380. 2023

    2023

  35. [40]

    Self- Supervised Meta-Learning for All-Layer DNN-Based AdaptiveControlwithStabilityGuarantees

    He, Guanqi, Choudhary, Yogita and Shi, Guanya. “Self- Supervised Meta-Learning for All-Layer DNN-Based AdaptiveControlwithStabilityGuarantees.” arXivpreprint arXiv:2410.07575(2024)

    2024 arXiv

  36. [41]

    Data modeling and ML practice for enabling intelligent digital twins inadaptive productionplanning andcontrol

    Chiurco, Alessandro, Elbasheer, Mohaiad, Longo, Francesco, Nicoletti, Letizia and Solina, Vittorio. “Data modeling and ML practice for enabling intelligent digital twins inadaptive productionplanning andcontrol.”Proce- dia Computer ScienceVol. 217 (2023): pp. 1908–1917

    2023

  37. [42]

    Alearning-basedframe- workforerrorcompensationin3Dprinting

    Shen, Zhen, Shang, Xiuqin, Zhao, Meihua, Dong, Xisong, Xiong,GangandWang,Fei-Yue. “Alearning-basedframe- workforerrorcompensationin3Dprinting.” IEEEtransac- tionsoncybernetics Vol.49No.11(2019): pp.4042–4050. 10

    2019

  38. [43]

    In-Process monitoring of porosity during laser additive manufacturing process

    Zhang, Bin, Liu, Shunyu and Shin, Yung C. “In-Process monitoring of porosity during laser additive manufacturing process.” AdditiveManufacturing Vol.28(2019): pp.497– 505

    2019

  39. [44]

    Optimal data-driven control of manufacturing processes usingreinforcementlearning: anapplicationtowirearcad- ditivemanufacturing

    Mattera, Giulio, Caggiano, Alessandra and Nele, Luigi. “Optimal data-driven control of manufacturing processes usingreinforcementlearning: anapplicationtowirearcad- ditivemanufacturing.” JournalofIntelligentManufacturing (2024): pp. 1–20

    2024

  40. [45]

    Designinganadaptive production control system using reinforcement learning

    Kuhnle, Andreas, Kaiser, Jan-Philipp, Theiß, Felix, Stricker, NicoleandLanza, Gisela. “Designinganadaptive production control system using reinforcement learning.” Journal of Intelligent ManufacturingVol. 32 (2021): pp. 855–876

    2021

  41. [46]

    Deep Learning Agents for Efficient Dy- namic Production Control in Semiconductor Manufactur- ing

    Boydon, Christian John Immanuel S, Zhang, Bin and Wu, Cheng-Hung. “Deep Learning Agents for Efficient Dy- namic Production Control in Semiconductor Manufactur- ing.” 2023 IEEE 19th International Conference on Au- tomation Science and Engineering (CASE): pp. 1–6. 2023. IEEE

    2023

  42. [47]

    Avirtualmetrologysystemforsemiconductormanu- facturing

    Kang, Pilsung, Lee, Hyoung-joo, Cho, Sungzoon, Kim, Dongil, Park, Jinwoo, Park, Chan-Kyoo and Doh, Seungy- ong. “Avirtualmetrologysystemforsemiconductormanu- facturing.” ExpertSystemswithApplications Vol.36No.10 (2009): pp. 12554–12561

    2009

  43. [48]

    Virtual metrology in semiconductor fabrication foundry usingdeeplearningneuralnetworks

    Tin, Tze Chiang, Tan, Saw Chin and Lee, Ching Kwang. “Virtual metrology in semiconductor fabrication foundry usingdeeplearningneuralnetworks.” IEEEAccess Vol.10 (2022): pp. 81960–81973

    2022

  44. [49]

    Physics-informed online machine learning and predictive control of nonlinear pro- cesseswithparameteruncertainty

    Zheng, Yingzhe and Wu, Zhe. “Physics-informed online machine learning and predictive control of nonlinear pro- cesseswithparameteruncertainty.” Industrial&Engineer- ing Chemistry ResearchVol. 62 No. 6 (2023): pp. 2804– 2818

    2023

  45. [50]

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

    Liao, Shuheng, Xue, Tianju, Jeong, Jihoon, Webster, Samantha, Ehmann, Kornel and Cao, Jian. “Hybrid ther- mal modeling of additive manufacturing processes using physics-informed neural networks for temperature predic- tionandparameteridentification.” ComputationalMechan- icsVo...

    2023

  46. [51]

    Un- certainty quantification for additive manufacturing process improvement: Recent advances

    Mahadevan, Sankaran, Nath, Paromita and Hu, Zhen. “Un- certainty quantification for additive manufacturing process improvement: Recent advances.”ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Me- chanical EngineeringVol. 8 No. 1 (2022): p. 010801

    2022

  47. [52]

    Deep learning

    Goodfellow, Ian. “Deep learning.” (2016)

    2016

  48. [53]

    Bishop, Christopher M and Nasrabadi, Nasser M.Pattern recognitionandmachinelearning . Vol.4. Springer(2006)

    2006

  49. [54]

    Auto-encoding variational bayes

    Kingma, Diederik P. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114(2013)

    2013 arXiv

  50. [55]

    Tutorial on variational autoencoders

    Doersch, Carl. “Tutorial on variational autoencoders.” arXiv preprint arXiv:1606.05908(2016)

    2016 arXiv

  51. [56]

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

    Watter, Manuel, Springenberg, Jost, Boedecker, Joschka and Riedmiller, Martin. “Embed to control: A locally lin- ear latent dynamics model for control from raw images.” Advances in neural information processing systemsVol. 28 (2015)

    2015

  52. [57]

    Cautious model predictive control using gaussian process regression

    Hewing, Lukas, Kabzan, Juraj and Zeilinger, Melanie N. “Cautious model predictive control using gaussian process regression.” IEEE Transactions on Control Systems Tech- nology Vol. 28 No. 6 (2019): pp. 2736–2743

    2019

  53. [58]

    Generative adversarial nets

    Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing,Warde-Farley,David,Ozair,Sherjil,Courville,Aaron and Bengio, Yoshua. “Generative adversarial nets.”Ad- vances in neural information processing systemsVol. 27 (2014)

    2014

  54. [59]

    McGAN: Generating manufacturable de- signs by embedding manufacturing rules into conditional generativeadversarialnetwork

    Wang, Zhichao, Yan, Xiaoliang, Melkote, Shreyes and Rosen, David. “McGAN: Generating manufacturable de- signs by embedding manufacturing rules into conditional generativeadversarialnetwork.” AdvancedEngineeringIn- formaticsVol. 64 (2025): p. 103074

    2025

  55. [60]

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

    Sabuhi, Mikael, Zhou, Ming, Bezemer, Cor-Paul and Musilek, Petr. “Applications of generative adversarial net- works in anomaly detection: A systematic literature re- view.” Ieee AccessVol. 9 (2021): pp. 161003–161029

    2021

  56. [61]

    DeepInspect: an AI-powered defect detection for man- ufacturing industries

    Kumbhar, Arti, Chougule, Amruta, Lokhande, Priya, Navaghane, Saloni, Burud, Aditi and Nimbalkar, Saee. “DeepInspect: an AI-powered defect detection for man- ufacturing industries.” arXiv preprint arXiv:2311.03725 (2023)

    2023 arXiv

  57. [62]

    Improving language understanding by generative pre-training

    Radford, Alec, Narasimhan, Karthik, Salimans, Tim, Sutskever, Ilya et al. “Improving language understanding by generative pre-training.” (2018)

    2018

  58. [63]

    Bart: Denoising sequence-to-sequencepre-trainingfornaturallanguagegen- eration, translation, and comprehension

    Lewis, Mike, Liu, Yinhan, Goyal, Naman, Ghazvinine- jad, Marjan, Mohamed, Abdelrahman, Levy, Omer, Stoy- anov, Ves and Zettlemoyer, Luke. “Bart: Denoising sequence-to-sequencepre-trainingfornaturallanguagegen- eration, translation, and comprehension.” arXiv preprint arXiv:1910...

    2019 arXiv

  59. [64]

    Attentionisallyouneed

    Vaswani,A. “Attentionisallyouneed.” AdvancesinNeural Information Processing Systems(2017)

    2017

  60. [65]

    Decision transformer: Rein- forcement learning via sequence modeling

    Chen, Lili, Lu, Kevin, Rajeswaran, Aravind, Lee, Kimin, Grover, Aditya, Laskin, Misha, Abbeel, Pieter, Srinivas, Aravind and Mordatch, Igor. “Decision transformer: Rein- forcement learning via sequence modeling.”Advances in neural information processing systemsVol. 34 (2021): ...

    2021

  61. [66]

    Denoising diffusion probabilistic models

    Ho, Jonathan, Jain, Ajay and Abbeel, Pieter. “Denoising diffusion probabilistic models.”Advances in neural infor- mation processing systemsVol. 33 (2020): pp. 6840–6851

    2020

  62. [67]

    Im- proved denoising diffusion probabilistic models

    Nichol, Alexander Quinn and Dhariwal, Prafulla. “Im- proved denoising diffusion probabilistic models.” Inter- national conference on machine learning: pp. 8162–8171

  63. [68]

    Score- based generative modeling through stochastic differential equations

    Song, Yang, Sohl-Dickstein, Jascha, Kingma, Diederik P, Kumar,Abhishek,Ermon,StefanoandPoole,Ben. “Score- based generative modeling through stochastic differential equations.” arXiv preprint arXiv:2011.13456(2020)

    2020 arXiv

  64. [69]

    Planningwithdiffusionforflexiblebehav- ior synthesis

    Janner, Michael, Du, Yilun, Tenenbaum, Joshua B and Levine,Sergey. “Planningwithdiffusionforflexiblebehav- ior synthesis.”arXiv preprint arXiv:2205.09991(2022)

    2022 arXiv

  65. [70]

    Aligning optimization trajectories with dif- fusionmodelsforconstraineddesigngeneration

    Giannone, Giorgio, Srivastava, Akash, Winther, Ole and Ahmed, Faez. “Aligning optimization trajectories with dif- fusionmodelsforconstraineddesigngeneration.” Advances 11 in Neural Information Processing SystemsVol. 36 (2023): pp. 51830–51861

    2023

  66. [71]

    Adadiff: Accelerating diffu- sion models through step-wise adaptive computation

    Tang, Shengkun, Wang, Yaqing, Ding, Caiwen, Liang, Yi, Li, Yao and Xu, Dongkuan. “Adadiff: Accelerating diffu- sion models through step-wise adaptive computation.”Eu- ropean Conference on Computer Vision: pp. 73–90. 2024. Springer

    2024

  67. [72]

    Diffusionmod- elsbeatgansonimagesynthesis

    Dhariwal,PrafullaandNichol,Alexander. “Diffusionmod- elsbeatgansonimagesynthesis.” Advancesinneuralinfor- mation processing systemsVol. 34 (2021): pp. 8780–8794

    2021

  68. [73]

    Multidiffusion: Fusingdiffusionpathsforcontrolledimage generation

    Bar-Tal, Omer, Yariv, Lior, Lipman, Yaron and Dekel, Tali. “Multidiffusion: Fusingdiffusionpathsforcontrolledimage generation.” (2023)

    2023

  69. [74]

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

    Li,Tianpu,Cao,Yue,Ye,QiangandZhang,YuMing.“Gen- erative adversarial networks (GAN) model for dynamically adjusted weld pool image toward human-based model pre- dictive control (MPC).” Journal of Manufacturing Pro- cessesVol. 141 (2025): pp. 210–221

    2025

  70. [76]

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

    Mu, Haochen, He, Fengyang, Yuan, Lei, Hatamian, Houman,Commins,PhilipandPan,Zengxi. “Onlinedistor- tion simulation using generative machine learning models: A step toward digital twin of metallic additive manufactur- ing.” Journal of Industrial Information IntegrationVol. 38 (...

    2024

  71. [77]

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

    Kim, Taekyeong, Kim, Jung Gi, Park, Sangeun, Kim, Hy- oung Seop, Kim, Namhun, Ha, Hyunjong, Choi, Seung- Kyum,Tucker,Conrad,Sung,HyokyungandJung,ImDoo. “Virtual surface morphology generation of Ti-6Al-4V di- rected energy deposition via conditional generative adver- sarial net...

    2023

  72. [80]

    Perceiver-actor: A multi-task transformer for robotic ma- nipulation

    Shridhar, Mohit, Manuelli, Lucas and Fox, Dieter. “Perceiver-actor: A multi-task transformer for robotic ma- nipulation.” Conference on Robot Learning: pp. 785–799

  73. [81]

    Transformer-based imitative reinforcement learning for multirobot path planning

    Chen, Lin, Wang, Yaonan, Miao, Zhiqiang, Mo, Yang, Feng, Mingtao, Zhou, Zhen and Wang, Hesheng. “Transformer-based imitative reinforcement learning for multirobot path planning.” IEEE Transactions on Indus- trial InformaticsVol. 19 No. 10 (2023): pp. 10233–10243

    2023

  74. [82]

    Multimodal vae ac- tive inference controller

    Meo, Cristian and Lanillos, Pablo. “Multimodal vae ac- tive inference controller.” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): pp. 2693–2699. 2021. IEEE

    2021

  75. [83]

    Dall-e-bot: Introducing web-scale diffusion models to robotics

    Kapelyukh, Ivan, Vosylius, Vitalis and Johns, Edward. “Dall-e-bot: Introducing web-scale diffusion models to robotics.” IEEE Robotics and Automation LettersVol. 8 No. 7 (2023): pp. 3956–3963

    2023

  76. [84]

    Towards autonomous system: flexible modu- lar production system enhanced with large language model agents

    Xia,Yuchen,Shenoy,Manthan,Jazdi,NasserandWeyrich, Michael. “Towards autonomous system: flexible modu- lar production system enhanced with large language model agents.” 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA): pp. 1–8. 2023. IEEE

    2023

  77. [85]

    Implicit behavioral cloning

    Florence, Pete, Lynch, Corey, Zeng, Andy, Ramirez, Os- car A, Wahid, Ayzaan, Downs, Laura, Wong, Adrian, Lee, Johnny, Mordatch, Igor and Tompson, Jonathan. “Implicit behavioral cloning.” Conference on robot learning: pp. 158–168. 2022. PMLR

    2022

  78. [86]

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

    Gunasegaram,DR,Barnard,AS,Matthews,MJ,Jared,BH, Andreaco, AM, Bartsch, K and Murphy, AB. “Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing.” Additive Manufacturing(2024): p. 104013

    2024

  79. [87]

    Digital twin-based cyber physical production system architectural framework for personalized produc- tion

    Park, Kyu Tae, Lee, Jehun, Kim, Hyun-Jung and Noh, Sang Do. “Digital twin-based cyber physical production system architectural framework for personalized produc- tion.” The International Journal of Advanced Manufactur- ing TechnologyVol. 106 (2020): pp. 1787–1810. 12

    2020

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.