Pith. sign in

REVIEW 4 major objections 4 minor 112 references

This paper argues that a data-driven Six-Sigma DMAIC loop, built from layerwise sensing, deep-learning defect detection, ontology knowledge graphs, and constrained Markov control, can make one-of-a-kind 3D-printed parts as statistically con

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

A position paper argues that Six Sigma's DMAIC cycle, extended with in-situ sensing, deep learning, ontology, and constrained-MDP control, can provide repeatable quality for additive manufacturing.

T0 review reviewed 2026-08-01 challenge →

load-bearing objection A coherent but largely synthetic roadmap for DMAIC in AM, worth reviewing as a position piece once the production-scale overclaim and the uncalibrated CMDP are addressed. the 4 major comments →

arxiv 2607.15430 v1 pith:YUHRFPGQ submitted 2026-07-16 eess.SY cs.SYstat.AP

Six-sigma Quality Management of Additive Manufacturing

Hui Yang , Prahalad Rao , Timothy Simpson , Yan Lu , Paul Witherell , Abdalla R. Nassar , Edward Reutzel , Soundar Kumara This is my paper
classification eess.SY cs.SYstat.AP
keywords additive manufacturingSix SigmaDMAICquality managementlayerwise defect detectiondeep learningMarkov decision processin-situ sensing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

This paper makes the case that additive manufacturing can acquire the same statistical quality discipline as mass production by adapting the Six-Sigma DMAIC cycle—Define, Measure, Analyze, Improve, Control—to layer-by-layer fabrication. The proposed pipeline chains together in-situ sensing, CAD-guided image registration, multifractal and deep-learning analysis that estimates each layer's defect risk, ontology-based knowledge graphs and design of experiments that link process settings to build quality, and a constrained Markov decision process that chooses corrective actions (do nothing, re-fuse, or machine off a layer) to minimize lead time and energy while keeping defect probability within specified bounds. If the chain holds, even one-of-a-kind builds become controllable in real time rather than left to post-build CT inspection, which the authors argue is what moves AM beyond the rapid-prototyping status quo.

Core claim

The paper's central claim is that the bottleneck to production-scale additive manufacturing is not the machines but the absence of a statistical control loop, and that such a loop can be assembled from existing data. Reported components: multifractal–lacunarity features predict build-quality indices with adjusted R² ≈ 94.8%; a deep network on CAD-registered sub-regions of in-situ images detects embedded defects with ≈92.5% accuracy; a graph-theory thermal solver matches a classical welding heat-source model within 90% at 10% of the computation time; a constrained MDP yields optimal layerwise actions. The authors' assertion: integrating these into the five DMAIC phases is a new scientific bas

What carries the argument

The load-bearing object is the layerwise defect-state estimate: each printed layer is assigned a risk probability of containing defects using a deep network trained on CAD-registered spatial characterizations of in-situ optical images. That state feeds a constrained Markov Decision Process (CMDP) whose actions are do nothing, laser re-fusion, or machining off a layer; the CMDP minimizes expected total cost (lead time or energy) subject to a bound h on the defect-state probability distribution at every layer. Supporting machinery: joint multifractal–lacunarity analysis quantifies nonlinear self-similar patterns in layerwise images; an AM ontology forms knowledge graphs that trace process-para

Load-bearing premise

The loop's load-bearing premise is that a layer's defect risk can be reliably estimated from in-situ imagery and that the layer-to-layer defect state evolves as a Markov chain with known or learnable transition probabilities; if the estimates are uncalibrated across geometries, machines, or build layouts, or if the state has long-range thermal memory, the CMDP's optimal policy is optimizing against noise.

What would settle it

Build the four-layer intentional-defect test geometry on a different machine (or on the same machine with a different build layout) while holding process parameters fixed, then compare the deep network's per-layer defect-risk probabilities against X-ray CT ground truth; if the roughly 92.5% accuracy does not transfer, the state estimates feeding the control policy are not trustworthy.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the pipeline holds, a hybrid AM machine can correct a defect in one layer before it is sealed by subsequent layers, shifting quality control from post-build CT inspection to in-process intervention.
  • The arithmetic in Section II shows that with a 1.14% per-layer defect probability, a 100-layer build has a 68% chance of containing at least one defect—so layerwise control is necessary, not optional, for high-yield builds.
  • The CMDP extends the single-objective MDP with a quality constraint, so the optimal policy explicitly trades lead time and energy against defect probability; this is presented as a step toward sustainable and economical AM.
  • Ontology knowledge graphs can be navigated backward from a requirement (e.g., hardness) to the sensor data that should be captured, guiding experiment planning and sensor selection.
  • The graph-theory thermal solver's speedup over finite-element models opens the possibility of screening part designs and build layouts for thermal red flags before printing.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The reported component accuracies come from single machines and geometries; the decisive test is cross-machine and cross-layout calibration. If the defect-risk estimates do not transfer, the CMDP's optimal policy is optimizing against noise.
  • The Markov assumption is the least-examined link: LPBF's thermal memory (cooling rates near 10^5 K/s, interlayer cooling times that depend on build layout) suggests the defect state may have longer-range dependence, and a natural extension is to compare CMDP policies against a higher-order model.
  • The 'zero lead time' retail scenario the paper sketches implies a business-model consequence: inventory moves from physical stock to validated digital files, relocating quality liability onto the data-and-control pipeline.
  • A testable lever: vary build layout while holding process parameters fixed on the intentional-defect drag-link part—if the deep network's per-layer risk estimates and CMDP actions track the actual defect pattern, the loop is doing real work; if not, the loop is a shell over fixed process settings.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes a DMAIC-based Six-Sigma quality management framework for additive manufacturing, organized around the five DMAIC phases. It reviews sensing and metrology, data analytics including multifractal analysis and deep learning, ontology/knowledge graphs, DOE, thermal simulation, and formulates layerwise corrective-action selection as a constrained Markov decision process (CMDP). The central claim, stated in Section VII, is that this data-driven framework will improve production-scale viability of AM by enabling layer-by-layer defect anticipation and correction.

Significance. The manuscript is best read as a review/research roadmap rather than a validated implementation. Its strength is the synthesis of a broad literature and the clear identification of open problems in AM quality management. The CMDP formulation in Section VI is standard and provides a useful formalization for sequential decision-making. However, the evidence for the central production-scale claim is an internally consistent chain of the authors' own prior publications; no independent replication, out-of-sample validation, or numerical demonstration of the CMDP is provided. If the framework were validated, the contribution would be significant; as it stands, the claim is speculative.

major comments (4)
  1. [Section VI.B (CMDP formulation)] The CMDP is presented as the core control contribution, but no specific values are given for action costs c_M, c_L, c_W, transition probabilities P_t^a(s_{t+1}|s_t), quality upper bounds h, or horizon T. The text states that the transition 'can be estimated from rich data' (Section VI.A) but provides no estimator, identifiability conditions, or sensitivity analysis. The claim that prior work 'demonstrated the optimal control policy' is not substantiated in this manuscript by any numerical results, algorithm pseudocode, or benchmark. Without this, the optimal policy is not demonstrated and the closed-loop control claim is unsupported.
  2. [Section IV.C (DNN defect detection)] The DNN accuracy of 92.50±1.03% is reported for a single drag-link build with intentional defects. The paper itself highlights geometry-, layout-, and machine-dependent thermal history (Sections II.B and V.B). No out-of-sample, cross-machine, cross-geometry, or cross-build-layout validation is provided. The DNN's predicted probabilities are not shown to be calibrated; they are used as the CMDP state distribution x_t in Section VI.B. If the risk estimates are uncalibrated, the CMDP policy optimizes against noise. This is a load-bearing gap for the production-scale claim.
  3. [Section VII (Discussion and Conclusions)] The central claim that the framework 'will impact production-scale viability of AM' is a strong predictive assertion. The evidence chain consists almost entirely of the authors' own prior publications (e.g., [56,57,69-75,78,79,97,105,111,112]). No independent external replication or third-party dataset is cited for the key components. The paper should either present such evidence or explicitly reframe the contribution as a research agenda, with the production-scale claim presented as a hypothesis rather than an established outcome.
  4. [Section V.A (Table 1)] The adjusted R^2 values of 94.76%, 91.75, 89.97, 94.85, and 68.00 are reported without sample size, confidence intervals, or cross-validation. The 68.00 value for melt pool depth is substantially lower, and the ontological explanation is anecdotal. These numbers are used to argue for ontology-guided sensing, but the statistical basis is incomplete. This is less central than the issues above, but it should be addressed for the Improve-phase claims.
minor comments (4)
  1. [Section VI.B] The statement 'If we delete the quality constraint ... the rows of Q_t will be independent and not correlated' is imprecise. In an unconstrained MDP, the optimization separates into per-layer decisions when solved by backward induction, but the state evolution x_{t+1}=M_t x_t still couples the layers. Clarify the intended meaning.
  2. [Section II.B] The illustrative calculation of build-level defect probability assumes independent layers. The text immediately acknowledges this limitation, but it would be helpful to mark the result as a lower-bound/illustrative example.
  3. [Section V.C] The sentence 'the graph theory approach converged to within 90% of Goldak’s solution within 10% of the computation time' is ambiguous. Specify whether this means 90% accuracy, 10% error, or 10% of the runtime, and define the convergence metric.
  4. [Abstract and Section I] The phrase 'design, develop, and implement' overstates the actual content; the paper is a review/vision. Suggest revising to 'propose a framework' or 'outline a methodology'.

Circularity Check

0 steps flagged

No significant circularity: the paper is a review/proposal whose central claims are forward-looking, not derived from its own inputs.

full rationale

The paper is a position/review article proposing a DMAIC framework for AM quality management, not a derivation chain that converts inputs into predictions. Section VII's production-scale viability claim is an explicitly forward-looking statement ('has the potential to substantially improve...') rather than a conclusion forced by the framework's equations. Each phase (Measure, Analyze, Improve, Control) is supported by a mixture of external references and the authors' prior work. Self-citation is heavy but not load-bearing in the circularity sense: the cited papers are published and describe the methods being surveyed; no uniqueness theorem or ansatz is imported from a self-citation to forbid alternatives. The CMDP in Section VI is a standard constrained-MDP formulation, and its inputs (transition probabilities P_t^a, state distributions x_t) are explicitly acknowledged as estimates: 'The transition can be estimated from rich data collected in AM processes, but is influenced by the uncertainty in sensor measurements and process conditions.' That is a limitation about calibration and validation, not a circular reduction. The 94.76% adjusted R^2 in Section IV.B is an in-sample goodness-of-fit summarized as 'strong correlation between process conditions and build quality'; although the word 'predict' is used, this is ordinary regression language and is not presented as an out-of-sample prediction, nor is it load-bearing for the central thesis. No equation in the paper is defined in terms of the result it is said to predict, and the proposed DMAIC loop is not claimed to have been validated end-to-end. Accordingly, no specific circular step can be exhibited, and the appropriate finding is no significant circularity.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

The central claim does not rest on newly invented physical entities, particles, forces, or conserved quantities. Its formal skeleton (MDP/CMDP) requires user-supplied costs, thresholds, and transition probabilities that are not specified here. The main scientific assumptions are: transferability of Six Sigma to AM, reliability of in-situ defect-risk estimates, Markovian layer evolution, and—explicitly flagged by the authors—an independence assumption in the pedagogical example.

free parameters (3)
  • Action costs c_M (machining), c_L (laser repair), c_W (wait) = not specified
    The CMDP formulation in Section VI.B requires these costs, but no values or estimation procedure are given in the paper, so the presented optimization is not numerically executable.
  • Defect-state upper bounds h = not specified
    The quality constraint x_t ≤ h in Section VI.B is a user-chosen threshold governing the policy; no value or selection rule is provided.
  • State transition probabilities P_t^a(s'|s) = not specified
    The MDP/CMDP transition kernel in Section VI must be estimated from data; the paper says it 'can be estimated from rich data' without providing estimates or data.
axioms (5)
  • domain assumption Layer defect events are independent in the illustrative probability calculation
    Section II.B explicitly says 'this example assumes that each layer is independent of each other' immediately before noting AM layers are actually highly correlated; the illustrative arithmetic therefore relies on a simplifying assumption the paper itself flags as false.
  • domain assumption In-situ optical images and DNN-derived defect-risk probabilities provide calibrated inputs for MDP control
    Section IV.C and VI.A claim DNN predicts layerwise defects with reported accuracy and that these risk probabilities can drive state updates; calibration and generalization to new geometries/build layouts are not demonstrated in this manuscript.
  • domain assumption Layer-to-layer defect evolution is Markovian
    Section VI.A formulates the sequential decision problem as an MDP with transition p(s,a,s') depending only on the current state and action, assuming the layer history is summarized by the current estimated defect state.
  • ad hoc to paper Six Sigma DMAIC concepts transfer from high-volume mass manufacturing to low-volume high-mix AM
    This is the paper's premise (Sections II and VII); the paper argues for the transfer but offers no comparative evidence that DMAIC outperforms existing AM quality-management approaches.
  • standard math Normal distribution as model for purely random process variation
    Section II.A uses μ±3σ and μ±6σ PPM calculations assuming normality, standard in the Six Sigma literature.

reviewed 2026-08-01 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Six-sigma Quality Management of Additive Manufacturing." pith.science (2026). https://pith.science/paper/YUHRFPGQ

@misc{pith2026260715430,
  author       = {Pith},
  title        = {Pith review of: Six-sigma Quality Management of Additive Manufacturing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YUHRFPGQ}},
  note         = {Machine review of arXiv:2607.15430}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

In this paper, we propose to design, develop, and implement the new DMAIC methodology for Six-Sigma quality management of AM. First, we define the specific quality challenges arising from AM layer-wise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems, and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE) and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

112 extracted references

  1. [1]

    In-Process Monitoring of Porosity in Additive Manufacturing using Optical Emission Spectroscopy,

    M. Montazeri, A. R. Nassar, A. J. Dunbar and P. Rao, "In-Process Monitoring of Porosity in Additive Manufacturing using Optical Emission Spectroscopy," IISE Transactions, pp. 1-28, 2019

    2019

  2. [2]

    Using the six-sigma metric to measure and improve the performance of a supply chain,

    T. Dasgupta, "Using the six-sigma metric to measure and improve the performance of a supply chain," Total Quality Management & Business Excellence, vol. 14, no. 3, pp. 355-366, 2003

    2003

  3. [3]

    Innovation, Quality Engineering, and Statistics,

    G. E. P. Box and W. H. Woodall, "Innovation, Quality Engineering, and Statistics," Quality Engineering, vol. 24, no. 1, pp. 20-29, 2012

    2012

  4. [4]

    Progress Towards Metal Additive Manufacturing Standardization to Support Qualification and Certification,

    M. Seifi, M. Gorelik, J. Waller, N. Hrabe, N. Shamsaei, S. Daniewicz and J. J. Lewandowski, "Progress Towards Metal Additive Manufacturing Standardization to Support Qualification and Certification," Journal of the Minerals Metals and Materials Society, vol. 69, no. 3, pp. 439-455, 2017

    2017

  5. [5]

    Measurement Science Roadmap for Metal-Based Additive Manufacturing - workshop summary report,

    K. Jurrens, "Measurement Science Roadmap for Metal-Based Additive Manufacturing - workshop summary report," National Institute of Standards and Technology, pp. 1-86, 2013

    2013

  6. [6]

    Three-Dimensional Printing: The Physics and Implications of Additive Manufacturing,

    E. Sachs, M. Cima, J. Cornie, D. Brancazio, J. Bredt, A. Curodeau, T. Fan, S. Khanuja, A. Lauder, J. Lee and S. Michaels, "Three-Dimensional Printing: The Physics and Implications of Additive Manufacturing," CIRP Annals, vol. 42, no. 1, pp. 257-260, 1993

    1993

  7. [7]

    An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling,

    D. Pal, N. Patil, K. Zeng and B. Stucker, "An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling," Journal of Manufacturing Science and Engineering, vol. 136, no. 6, pp. 061022-061022-16, 2014

    2014

  8. [8]

    Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges,

    W. E. King, A. T. Anderson, R. M. Ferencz, N. E. Hodge, C. Kamath, S. A. Khairallah and A. M. Rubenchik, "Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges," Applied Physics Reviews, vol. 2, no. 4, pp. 041304, 2015

    2015

  9. [9]

    Direct measurements of laser absorptivity during metal melt pool formation associated with powder bed fusion additive manufacturing processes,

    M. Matthews, J. Trapp, G. Guss and A. Rubenchik, "Direct measurements of laser absorptivity during metal melt pool formation associated with powder bed fusion additive manufacturing processes," J. Laser Appl., vol. 30, no. 3, pp. 032302, 2018

    2018

  10. [10]

    Multi-sensor investigations of optical emissions and their relations to directed energy deposition processes and quality,

    C. B. Stutzman, A. R. Nassar and E. W. Reutzel, "Multi-sensor investigations of optical emissions and their relations to directed energy deposition processes and quality," Additive Manufacturing, vol. 21, pp. 333-339, 2018

    2018

  11. [11]

    A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing,

    E. W. Reutzel and A. R. Nassar, "A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing," Rapid Prototyping Journal, vol. 21, no. 2, pp. 159-167, 2015

    2015

  12. [12]

    Optical, layerwise monitoring of powder bed fusion,

    B. Foster, E. Reutzel, A. Nassar, B. Hall, S. Brown and C. Dickman, "Optical, layerwise monitoring of powder bed fusion," in Solid Freeform Fabrication Symposium, Austin, TX, 2015, pp. 295-307

    2015

  13. [13]

    Sensing for directed energy deposition and powder bed fusion additive manufacturing at penn state university,

    A. R. Nassara, E. W. Reutzel, S. W. Browna, J. P. Morgana Jr, J. P. Morgana, D. J. Natalea, R. L. Tutwilera, D. P. Fecka and J. C. Banksa, "Sensing for directed energy deposition and powder bed fusion additive manufacturing at penn state university," in Laser 3D Manufacturing III, San Francisco, CA, 2016, pp. 97380R. 37

    2016

  14. [14]

    Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging

    C. Gobert, E. W. Reutzel, J. Petrich, A. R. Nassar and S. Phoha, "Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging." Additive Manufacturing, vol. 21, pp. 517-528, 2018

    2018

  15. [15]

    From Design Complexity to Build Quality in Additive Manufacturing—A Sensor-Based Perspective,

    R. Chen, F. Imani, E. Reutzel and H. Yang, "From Design Complexity to Build Quality in Additive Manufacturing—A Sensor-Based Perspective," IEEE Sensors Letters, 3(1), pp. 1-4, 2019,

    2019

  16. [16]

    Introduction to aerospace materials requirements and the role of additive manufacturing,

    F. Froes, R. Boyer and B. Dutta, "Introduction to aerospace materials requirements and the role of additive manufacturing," Additive Manufacturing for the Aerospace Industry, pp. 1-6, 2019

    2019

  17. [17]

    Qualification and certification of metal additive manufactured hardware for aerospace applications,

    R. Russell, D. Wells, J. Waller, B. Poorganji, E. Ott, T. Nakagawa, H. Sandoval, N. Shamsaei and M. Seifi, "Qualification and certification of metal additive manufactured hardware for aerospace applications," Additive Manufacturing for the Aerospace Industry, pp. 33-66, 2019

    2019

  18. [18]

    Additive manufacturing for biofabricated medical device applications,

    M. P. Francis, N. Kemper, Y. Maghdouri-White and N. Thayer, "Additive manufacturing for biofabricated medical device applications," Additive Manufacturing, pp. 311-344, 2018

    2018

  19. [19]

    Regulatory interfaces surrounding the growing field of additive manufacturing of medical devices and biologic products,

    J. E. Adamo, W. L. Grayson, H. Hatcher, J. S. Brown, A. Thomas, S. Hollister and S. J. Steele, "Regulatory interfaces surrounding the growing field of additive manufacturing of medical devices and biologic products," Journal of Clinical and Translational Science, vol. 2, no. 5, pp. 301-304, 2018

    2018

  20. [20]

    Statistical modal analysis for variation characterization and application in manufacturing quality control,

    W. Huang, J. Liu, V. Chalivendra, D. Ceglarek, Z. Kong and Y. Zhou, "Statistical modal analysis for variation characterization and application in manufacturing quality control," IIE Transactions, vol. 46, no. 5, pp. 497-511, 2014

    2014

  21. [21]

    Predictive Control Considering Model Uncertainty for Variation Reduction in Multistage Assembly Processes,

    J. Zhong, J. Liu and J. Shi, "Predictive Control Considering Model Uncertainty for Variation Reduction in Multistage Assembly Processes," Automation Science and Engineering, IEEE Transactions On, vol. 7, no. 4, pp. 724-735, 2010

    2010

  22. [22]

    Robust Parameter Design of Multiple -Target Systems,

    V. R. Joseph and C. F. Jeff Wu, "Robust Parameter Design of Multiple -Target Systems," Technometrics, vol. 44, no. 4, pp. 338-346, 2002

    2002

  23. [23]

    3D Printing Disrupts Manufacturing: How Economies of One Create New Rules of Competition,

    I. J. Petrick and T. W. Simpson, "3D Printing Disrupts Manufacturing: How Economies of One Create New Rules of Competition," Research-Technology Management, vol. 56, no. 6, pp. 12-16, 2013

    2013

  24. [24]

    Additive manufacturing: scientific and technological challenges, market uptake and opportunities,

    S. A. Tofail, E. P. Koumoulos, A. Bandyopadhyay, S. Bose, L. O’Donoghue and C. Charitidis, "Additive manufacturing: scientific and technological challenges, market uptake and opportunities," Materials Today, vol. 21, no. 1, pp. 22-37, 2018

    2018

  25. [25]

    Quality control and improvement for multistage systems: A survey,

    J. Shi and S. Zhou, "Quality control and improvement for multistage systems: A survey," IIE Transactions, vol. 41, no. 9, pp. 744, 2009

    2009

  26. [26]

    Powders for additive manufacturing processes: Characterization techniques and effects on part properties,

    A. T. Sutton, C. S. Kriewall, M. C. Leu and J. W. Newkirk, "Powders for additive manufacturing processes: Characterization techniques and effects on part properties," in Proceedings of the 26th Annual International Solid Freeform Fabrication Symposium–An Additive Manufacturing Conference, 2016,

    2016

  27. [27]

    Process defects and in situ monitoring methods in metal powder bed fusion: a review,

    M. Grasso and B. M. Colosimo, "Process defects and in situ monitoring methods in metal powder bed fusion: a review," Measurement Science and Technology, vol. 28, no. 4, pp. 044005, 2017

    2017

  28. [28]

    Measurement science needs for real-time control of additive manufacturing powder bed fusion processes,

    M. Mani, S. Feng, B. Lane, A. Donmez, S. Moylan and R. Fesperman, "Measurement science needs for real-time control of additive manufacturing powder bed fusion processes," 2015. 38

    2015

  29. [29]

    A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes,

    M. Mani, B. M. Lane, M. A. Donmez, S. C. Feng and S. P. Moylan, "A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes," Int J Prod Res, vol. 55, no. 5, pp. 1400-1418, 2017

    2017

  30. [30]

    Infrared thermography for laser -based powder bed fusion additive manufacturing processes,

    S. Moylan, E. Whitenton, B. Lane and J. Slotwinski, "Infrared thermography for laser -based powder bed fusion additive manufacturing processes," in AIP Conference Proceedings, 2014, pp. 1191-1196

    2014

  31. [31]

    Review of in -situ process monitoring and in-situ metrology for metal additive manufacturing,

    S. K. Everton, M. Hirsch, P. Stravroulakis, R. K. Leach and A. T. Clare, "Review of in -situ process monitoring and in-situ metrology for metal additive manufacturing," Mater Des, vol. 95, pp. 431-445, 2016

    2016

  32. [32]

    In-process sensing in selective laser melting (SLM) additive manufacturing,

    T. G. Spears and S. A. Gold, "In-process sensing in selective laser melting (SLM) additive manufacturing," Integrating Materials and Manufacturing Innovation, vol. 5, no. 1, pp. 16-40, 2016

    2016

  33. [33]

    A review on process monitoring and control in metal -based additive manufacturing,

    G. Tapia and A. Elwany, "A review on process monitoring and control in metal -based additive manufacturing," Journal of Manufacturing Science and Engineering, vol. 136, no. 6, pp. 060801, 2014

    2014

  34. [34]

    The metallurgy and processing science of metal additive manufacturing,

    W. J. Sames, F. A. List, S. Pannala, R. R. Dehoff and S. S. Babu, "The metallurgy and processing science of metal additive manufacturing," International Materials Reviews, vol. 61, no. 5, pp. 315- 360, 2016

    2016

  35. [35]

    Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations,

    Y. Huang, M. C. Leu, J. Mazumder and A. Donmez, "Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations," Transactions of the ASME, Journal of Manufacturing Science and Engineering, vol. 137, no. 1, pp. 014001-014001-10, 2015

    2015

  36. [36]

    Prediction and Experimental Validation of Part Thermal History in Fused Filament Fabrication Additive Manufacturing Process,

    M. Roy, R. Yavari, C. Zhou, O. Wodo and P. Rao, "Prediction and Experimental Validation of Part Thermal History in Fused Filament Fabrication Additive Manufacturing Process," ASME. J. Manuf. Sci. Eng., vol. 141, no. 12, pp. 121001, 2019

    2019

  37. [37]

    Evolution of solidification texture during additive manufacturing,

    H. Wei, J. Mazumder and T. DebRoy, "Evolution of solidification texture during additive manufacturing," Scientific Reports, vol. 5, pp. 16446, 2015

    2015

  38. [38]

    Additive manufacturing of metallic components --Process, structure and properties,

    T. DebRoy, H. L. Wei, J. S. Zuback, T. Mukherjee, J. W. Elmer, J. O. Milewski, A. M. Beese, A. Wilson-Heid, A. De and W. Zhang, "Additive manufacturing of metallic components --Process, structure and properties," Progress in Materials Science, vol. 92, pp. 112-224, 2018

    2018

  39. [39]

    Laser powder -bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones,

    S. A. Khairallah, A. T. Anderson, A. Rubenchik and W. E. King, "Laser powder -bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones," Acta Materialia, vol. 108, pp. 36-45, 2016

    2016

  40. [40]

    Development of experimental method for in situ distortion and temperature measurements during the laser powder bed fusion additive manufacturing process,

    A. Dunbar, E. Denlinger, J. Heigel, P. Michaleris, P. Guerrier, R. Martukanitz and T. W. Simpson, "Development of experimental method for in situ distortion and temperature measurements during the laser powder bed fusion additive manufacturing process," Additive Manufacturing, vol. 12, pp. 25-30, 2016

    2016

  41. [41]

    Numerical modeling and experimental validation of thermal history and microstructure for additive manufacturing of an Inconel 718 product,

    P. Promoppatum, S. Yao, P. C. Pistorius, A. D. Rollett, P. J. Coutts, F. Lia and R. Martukanitz, "Numerical modeling and experimental validation of thermal history and microstructure for additive manufacturing of an Inconel 718 product," Progress in Additive Manufacturing, vol. 3, no. 1-2, pp. 15-32, 2018

    2018

  42. [42]

    Thermographic process monitoring in powderbed based additive manufacturing,

    H. Krauss, T. Zeugner and M. F. Zaeh, "Thermographic process monitoring in powderbed based additive manufacturing," in AIP Conference Proceedings, 2015, pp. 177-183. 39

    2015

  43. [43]

    Thermographic measurements of the commercial laser powder bed fusion process at NIST,

    B. Lane, S. Moylan, E. P. Whitenton and L. Ma, "Thermographic measurements of the commercial laser powder bed fusion process at NIST," Rapid Prototyping Journal, vol. 22, no. 5, pp. 778-787, 2016

    2016

  44. [44]

    Gibson, D

    I. Gibson, D. Rosen and B. Stucker, Additive Manufacturing Technologies Rapid Prototyping to Direct Digital Manufacturing. 2010. Springer,

    2010

  45. [45]

    In-situ metrology system for laser powder bed fusion additive process,

    W. S. Land II, B. Zhang, J. Ziegert and A. Davies, "In-situ metrology system for laser powder bed fusion additive process," Procedia Manufacturing, vol. 1, pp. 393-403, 2015

    2015

  46. [46]

    P. Boulware, "Final Technical Report to National Institute of Standards and Technology and National Center for Defense Manufacturing and Machining - Measurement Science Innovation Program for Additive Manufacturing: An Evaluation of In-Process Sensing Techniques Through the Use of an Open Architecture Laser Powder Bed Fusion Platform," 2016

    2016

  47. [47]

    In-process monitoring of material cross- contamination defects in laser powder bed fusion,

    M. Montazeri, R. Yavari, P. Rao and P. Boulware, "In-process monitoring of material cross- contamination defects in laser powder bed fusion," Journal of Manufacturing Science and Engineering, vol. 140, no. 11, pp. 111001, 2018

    2018

  48. [48]

    Flaw detection in powder bed fusion using optical imaging,

    M. Abdelrahman, E. W. Reutzel, A. R. Nassar and T. L. Starr, "Flaw detection in powder bed fusion using optical imaging," Additive Manufacturing, vol. 15, pp. 1-11, 2017

    2017

  49. [49]

    Process monitoring of directed -energy deposition of inconel-718 via plume imaging,

    A. Nassar, B. Starr and E. Reutzel, "Process monitoring of directed -energy deposition of inconel-718 via plume imaging," in Solid Freeform Fabrication Symposium (SFF), Austin, TX, 2015, pp. 10-12

    2015

  50. [50]

    Assessment of optical emission analysis for in -process monitoring of powder bed fusion additive manufacturing,

    A. J. Dunbar and A. R. Nassar, "Assessment of optical emission analysis for in -process monitoring of powder bed fusion additive manufacturing," Virtual and Physical Prototyping, vol. 13, no. 1, pp. 14- 19, 2018

    2018

  51. [51]

    NIST Atomic Spectra Database ,

    A. Kramida, Y. Ralchenko and J. Reader, "NIST Atomic Spectra Database ," National Institute of Standards and Technology (NIST), 2017

    2017

  52. [52]

    Effect of Process Parameters on the Surface Roughness of Overhanging Structures in Laser Powder Bed Fusion Additive Manufacturing,

    J. C. Fox, S. P. Moylan and B. M. Lane, "Effect of Process Parameters on the Surface Roughness of Overhanging Structures in Laser Powder Bed Fusion Additive Manufacturing," Procedia CIRP, 45pp. 131-134, 2016,

    2016

  53. [53]

    Tolerancing and Verification of Additive Manufactured Lattice with Supplemental Surfaces,

    G. Ameta, J. Fox and P. Witherell, "Tolerancing and Verification of Additive Manufactured Lattice with Supplemental Surfaces," Procedia CIRP, 75pp. 69-74, 2018,

    2018

  54. [54]

    A collaborative data management system for additive manufacturing,

    Y. Lu, P. Witherell and A. Donmez, "A collaborative data management system for additive manufacturing," in ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec, Canada, 2017, pp. 1-11

    2017

  55. [55]

    Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes,

    C. Kan and H. Yang, "Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes," Qual. Reliab. Eng. Int., vol. 33, no. 8, pp. 2003-2022, 2017

    2003

  56. [56]

    From Design Complexity to Build Quality in Additive Manufacturing - A Sensor-Based Perspective,

    R. Chen, F. Imani, E. Reutzel and H. Yang, "From Design Complexity to Build Quality in Additive Manufacturing - A Sensor-Based Perspective," IEEE Sensors Letters, vol. 3, no. 1, pp. 1-4, 2018

    2018

  57. [57]

    In-situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion using Deep Learning,

    A. Gaikwad, F. Imani, H. Yang, E. Reutzel and P. Rao, "In-situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion using Deep Learning," Smart and Sustainable Manufacturing Systems, pp. 1-39, 2019. 40

    2019

  58. [58]

    Metrology of freeform shaped parts,

    E. Savio, L. De Chiffre and R. Schmitt, "Metrology of freeform shaped parts," CIRP Annals- Manufacturing Technology, vol. 56, no. 2, pp. 810-835, 2007

    2007

  59. [59]

    Quantitative assessments of geometric errors for rapid prototyping in medical applications,

    C. Arrieta, S. Uribe, J. Ramos-Grez, A. Vargas, P. Irarrazaval, V. Parot and C. Tejos, "Quantitative assessments of geometric errors for rapid prototyping in medical applications," Rapid Prototyping Journal, vol. 18, no. 6, pp. 431-442, 2012

    2012

  60. [60]

    Speed and accuracy evaluation of additive manufacturing machines,

    T. Brajlih, B. Valentan, J. Balic and I. Drstvensek, "Speed and accuracy evaluation of additive manufacturing machines," Rapid Prototyping Journal, vol. 17, no. 1, pp. 64-75, 2011

    2011

  61. [61]

    Layer by layer validation of geometrical accuracy in additive manufacturing processes,

    K. Zeng, N. Patil, H. Gu, H. Gong, D. Pal, T. Starr and B. Stucker, "Layer by layer validation of geometrical accuracy in additive manufacturing processes," in Proceedings of the Solid Freeform Fabrication Symposium, Austin, TX, Aug, 2013, pp. 12-14

    2013

  62. [62]

    Advances in three dimensional printing –state of the art and future perspectives,

    D. Dimitrov, K. Schreve and N. De Beer, "Advances in three dimensional printing –state of the art and future perspectives," Rapid Prototyping Journal, vol. 12, no. 3, pp. 136-147, 2006

    2006

  63. [63]

    Variability in the geometric accuracy of additively manufactured test parts,

    A. L. Cooke and J. A. Soons, "Variability in the geometric accuracy of additively manufactured test parts," in Proceedings of 21st Annual International Solid Freeform Fabrication Symposium, Austin, TX, 2010, pp. 1-12

    2010

  64. [64]

    Pursuing successful rapid manufacturing: a users' best - practices approach,

    J. Munguía, J. de Ciurana and C. Riba, "Pursuing successful rapid manufacturing: a users' best - practices approach," Rapid Prototyping Journal, vol. 14, no. 3, pp. 173-179, 2008

    2008

  65. [65]

    Computer aided decision support for fused deposition modeling,

    C. Ziemian and P. Crawn III, "Computer aided decision support for fused deposition modeling," Rapid Prototyping Journal, vol. 7, no. 3, pp. 138-147, 2001

    2001

  66. [66]

    Surface roughness prediction in fused deposition modelling by neural networks

    A. Boschetto, V. Giordano and F. Veniali, "Surface roughness prediction in fused deposition modelling by neural networks." Int J Adv Manuf Technol, vol. 67, 2013

    2013

  67. [67]

    Dynamic modeling and monitoring of contour crafting —An extrusion- based layered manufacturing process,

    S. Bukkapatnam and B. Clark, "Dynamic modeling and monitoring of contour crafting —An extrusion- based layered manufacturing process," Journal of Manufacturing Science and Engineering, vol. 129, no. 1, pp. 135-142, 2007

    2007

  68. [68]

    Defect generation and propagation mechanism during additive manufacturing by selective beam melting,

    A. Bauereiß, T. Scharowsky and C. Körner, "Defect generation and propagation mechanism during additive manufacturing by selective beam melting," J. Mater. Process. Technol., vol. 214, no. 11, pp. 2522-2528, 2014

    2014

  69. [69]

    Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing,

    B. Yao, F. Imani, A. S. Sakpal, E. Reutzel and H. Yang, "Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing," ASME Journal of Manufacturing Science and Engineering, vol. 140, no. 3, pp. 031014, 2017

    2017

  70. [70]

    Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control,

    F. Imani, B. Yao, R. Chen, P. Rao and H. Yang, "Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control," Journal of Manufacturing Science and Engineering, vol. 141, no. 4, pp. 044501-044501-7, 2019

    2019

  71. [71]

    Fractal pattern recognition of image profiles for manufacturing process monitoring and control,

    F. Imani, B. Yao, R. Chen, P. Rao and H. Yang, "Fractal pattern recognition of image profiles for manufacturing process monitoring and control," in ASME 2018 13th International Manufacturing Science and Engineering Conference, 2018,

    2018

  72. [72]

    Layerwise In -Process Quality Monitoring in Laser Powder Bed Fusion,

    F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang and E. Reutzel, "Layerwise In -Process Quality Monitoring in Laser Powder Bed Fusion," Proceedings of ASME International Manufacturing 41 Science and Engineering Conference, Volume 1: Additive Manufacturing; Bio and Sustainable Manufacturing, (51357), pp. V001T01A038, 2018,

    2018

  73. [73]

    Process Mapping and In - Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging,

    F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang and E. Reutzel, "Process Mapping and In - Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging," Journal of Manufacturing Science and Engineering, vol. 140, no. 10, pp. 101009-101014, 2018

    2018

  74. [74]

    Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control,

    F. Imani, R. Chen, E. Diewald, E. Reutzel and H. Yang, "Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control," Journal of Manufacturing Science and Engineering, vol. 141, no. 11, 2019

    2019

  75. [75]

    Image -guided variant shape analysis of layerwise build quality in additive manufacturing,

    F. Imani, R. Chen, E. Diewald, E. W. Reutzel and H. Yang, "Image -guided variant shape analysis of layerwise build quality in additive manufacturing," in ASME 2019 14th International Manufacturing Science and Engineering Conference, 2019,

    2019

  76. [76]

    Influence of position and laser power on thermal history and microstructure of direct laser fabricated Ti–6Al–4V samples,

    L. Qian, J. Mei, J. Liang and X. Wu, "Influence of position and laser power on thermal history and microstructure of direct laser fabricated Ti–6Al–4V samples," Materials Science and Technology, vol. 21, no. 5, pp. 597-605, 2005

    2005

  77. [77]

    Mechanical properties of additive manufactured Ti -6Al-4V using wire and powder based processes,

    E. Brandl, C. Leyens and F. Palm, "Mechanical properties of additive manufactured Ti -6Al-4V using wire and powder based processes," Proceedings of IOP Conference Series: Materials Science and Engineering, vol. 26, no. 1, pp. 012004, 2011

    2011

  78. [78]

    Ontology-Based Laser and Thermal Metamodels for Metal-Based Additive Manufacturing,

    B. Roh, S. R. Kumara, T. W. Simpson and P. Witherell, "Ontology-Based Laser and Thermal Metamodels for Metal-Based Additive Manufacturing," Proceedings of ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Charlotte, North Carolina, pp. 21-24, 2016

    2016

  79. [79]

    Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models,

    P. Witherell, S. Feng, T. W. Simpson, D. B. Saint John, P. Michaleris, Z. Liu, L. Chen and R. Martukanitz, "Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models," Journal of Manufacturing Science and Engineering, vol. 136, no. 6, pp. 061025-061025-9, 2014

    2014

  80. [80]

    Development of a Relational Energy Balance for Additive Manufacturing,

    J. Z. Park, "Development of a Relational Energy Balance for Additive Manufacturing," Penn State MS Thesis, pp. 1-109, 2015

    2015

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.