Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction | Paper Citation Record

Observation 9a15d879-df1c-4241-9ff4-df4ac8251e46 · outbound

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

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Process monitoring, diagnosis and control of additive manufacturing

Reference 1

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Observation 6f0dd588-73ff-4e00-b90c-21d4345fc71c · outbound

This paper cites Intelligent additive manufacturing architecture for enhancing uniformity of surface roughness and mechanical properties of laser powder bed fusion components.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Intelligent additive manufacturing architecture for enhancing uniformity of surface roughness and mechanical properties of laser powder bed fusion components

Reference 2

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Observation ea35d05f-2a30-456f-86db-39c62cbb7244 · outbound

This paper cites Online distortion simulation using generative machine learning models: A step toward digital twin of metallic additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Online distortion simulation using generative machine learning models: A step toward digital twin of metallic additive manufacturing

Reference 3

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Observation dd579724-120d-4db3-9ec6-dff72e57ef73 · outbound

This paper cites In- terpretable machine learning approach for exploring process-structure- property relationships in metal additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction In- terpretable machine learning approach for exploring process-structure- property relationships in metal additive manufacturing

Reference 4

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Observation 2e3fd191-14bd-4837-a28b-6a0654ead8de · outbound

This paper cites An integrated process–structure–property modeling framework for additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction An integrated process–structure–property modeling framework for additive manufacturing

Reference 5

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Observation e40d070d-3afa-4c77-b58d-0daff6b2a35d · outbound

This paper cites A deep-learning-based surro- gate model for thermal signature prediction in laser metal deposition.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction A deep-learning-based surro- gate model for thermal signature prediction in laser metal deposition

Reference 6

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Observation 020b8250-7101-42a8-ad4f-6b537aa6e5a4 · outbound

This paper cites Dataset of process-structure-property feature relationships for alsi10mg material fabricated using laser powder bed fusion additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Dataset of process-structure-property feature relationships for alsi10mg material fabricated using laser powder bed fusion additive manufacturing

Reference 7

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Observation c1021d0d-515e-4506-9213-d2c7c7836d2a · outbound

This paper cites Metal am process-structure-property relational linkages using gaussian process surrogates.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Metal am process-structure-property relational linkages using gaussian process surrogates

Reference 8

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Observation 45c000a6-0843-45ed-82ea-e69e03362fea · outbound

This paper cites Learning and predicting shape deviations of smooth and non-smooth 3d geometries through mathe- matical decomposition of additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Learning and predicting shape deviations of smooth and non-smooth 3d geometries through mathe- matical decomposition of additive manufacturing

Reference 9

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This paper cites Surrogate-based model chains for establishing process-structure-property linkages with quantified uncer- tainties in metal additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Surrogate-based model chains for establishing process-structure-property linkages with quantified uncer- tainties in metal additive manufacturing

Reference 10

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Observation 80008598-570d-4cfe-91be-3bc3cfc9ef4e · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Semi-supervised classification with graph convolutional networks

Reference 11

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Observation 364a962a-cf70-4981-b205-22d73c990484 · outbound

This paper cites Physics-informed weakly- supervised learning for quality prediction of manufacturing processes.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Physics-informed weakly- supervised learning for quality prediction of manufacturing processes

Reference 12

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Observation 280a0d60-e21f-48e1-b69b-e94377fedbe1 · outbound

This paper cites Graph attention networks.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Graph attention networks

Reference 13

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Observation c457baae-fd75-4029-a53c-cce1c8f7a96d · outbound

This paper cites Revolutionizing 3d electronics: Single-step femtosecond laser fabrication of conductive embedded structures and circuitry.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Revolutionizing 3d electronics: Single-step femtosecond laser fabrication of conductive embedded structures and circuitry

Reference 14

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Observation 0ae54611-5d38-481d-b7f1-f29f4a43f45a · outbound

This paper cites Dataset of process- structure-property feature relationship for laser powder bed fusion addi- tive manufactured ti-6al-4v material.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Dataset of process- structure-property feature relationship for laser powder bed fusion addi- tive manufactured ti-6al-4v material

Reference 15

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Observation 571c5faa-7b5a-43e3-9f33-f8bb08c4c9a2 · outbound

This paper cites Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys

Reference 16

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Observation 08610df5-5bab-4f10-9325-1df3d8254bc6 · outbound

This paper cites Aachen-heerlen annotated steel microstruc- ture dataset.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Aachen-heerlen annotated steel microstruc- ture dataset

Reference 17

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Observation a7ecf577-5157-4704-bd7c-9917516905c4 · outbound

This paper cites Thermography of the metal bridge structures fabricated for the 2018 additive manufacturing benchmark test series (am-bench 2018).

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Thermography of the metal bridge structures fabricated for the 2018 additive manufacturing benchmark test series (am-bench 2018)

Reference 18

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Observation c06e5545-ec4a-4dd9-9a49-9bc34d98ec1a · outbound

This paper cites A compilation of experimental data on the mechanical properties and microstructural features of ti-alloys.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction A compilation of experimental data on the mechanical properties and microstructural features of ti-alloys

Reference 19

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Observation 93c21c29-6ec5-424b-888b-ba37c09b02b4 · outbound

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

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Process monitoring, diagnosis and control of additive manufacturing

Reference 20

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Observation 12d113d2-edeb-4780-8e98-fb3e13f8efb5 · outbound

This paper cites A deep-learning- based surrogate model for thermal signature prediction in laser metal deposition.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction A deep-learning- based surrogate model for thermal signature prediction in laser metal deposition

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:00:55.200936Z

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Observation a87a8eee-6778-4332-8953-fe98173fb4f8 · outbound

This paper cites Physics-informed weakly-supervised learning for quality prediction of manufacturing processes.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Physics-informed weakly-supervised learning for quality prediction of manufacturing processes

Reference 22

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Observation 148ea4ca-f5ec-458f-93e2-40095b6ebacf · outbound

This paper cites Architecture-driven physics-informed deep learning for temperature prediction in laser powder bed fusion additive manufacturing with limited data.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Architecture-driven physics-informed deep learning for temperature prediction in laser powder bed fusion additive manufacturing with limited data

Reference 23

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Observation d3e90a37-2097-4f26-bdfc-f31afe9d60ee · outbound

This paper cites Ten- sile strength prediction in directed energy deposition through physics- informed machine learning and shapley additive explanations.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Ten- sile strength prediction in directed energy deposition through physics- informed machine learning and shapley additive explanations

Reference 24

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Observation 64fc1351-c3fc-4e56-a944-96ffbcef92aa · outbound

This paper cites A physics-informed convolu- tional neural network with custom loss functions for porosity prediction in laser metal deposition.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction A physics-informed convolu- tional neural network with custom loss functions for porosity prediction in laser metal deposition

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.656796Z

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Observation a4f02c33-b05a-4629-ac23-3f2f85b511cf · outbound

This paper cites A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition

Reference 26

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Observation 0f832fce-5928-4641-9fbe-840e6b488ba7 · outbound

This paper cites New insight into the multivariate relationships among process, structure, and properties in laser powder bed fusion alsi10mg.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction New insight into the multivariate relationships among process, structure, and properties in laser powder bed fusion alsi10mg

Reference 27

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Observation b0d4e000-3dbd-4b4e-8b8a-30154d03e3dd · outbound

This paper cites Data-driven analysis of process, structure, and properties of additively manufactured inconel 718 thin walls.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Data-driven analysis of process, structure, and properties of additively manufactured inconel 718 thin walls

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.654601Z

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Observation a685a096-0c76-483e-9d74-beedf6d73536 · outbound

This paper cites Machine learning of microstructure–property relationships in materials leveraging microstructure representation from foundational vision transformers.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Machine learning of microstructure–property relationships in materials leveraging microstructure representation from foundational vision transformers

Reference 29

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Observation e5f2cc29-885d-43e6-83b8-f5be5909bd57 · outbound

This paper cites Graph-based variation propagation network for modeling and prediction of hybrid multi-stage manufacturing systems.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Graph-based variation propagation network for modeling and prediction of hybrid multi-stage manufacturing systems

Reference 30

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Observation 77ec7ef3-f753-4536-9dc6-7c0b262f3758 · outbound

This paper cites Inductive representation learning on large graphs.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Inductive representation learning on large graphs

Reference 31

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Observation a42fcb96-dac3-488b-9586-73734dee61c9 · outbound

This paper cites Hyperdimensional computing: An introduction to comput- ing in distributed representation with high-dimensional random vectors.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Hyperdimensional computing: An introduction to comput- ing in distributed representation with high-dimensional random vectors

Reference 32

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Observation 861d9244-b3b6-4795-9344-41dc10a8c49d · outbound

This paper cites Efficient biosignal processing using hyperdimensional computing: Network templates for combined learning and classification of exg signals.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Efficient biosignal processing using hyperdimensional computing: Network templates for combined learning and classification of exg signals

Reference 33

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Observation 3c9d3283-d5d4-4328-b630-3b07ad30a096 · outbound

This paper cites Neural computation for robust and holo- graphic face detection.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Neural computation for robust and holo- graphic face detection

Reference 34

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Observation a9d0247f-5e61-46db-802d-f2288d2a336e · outbound

This paper cites Brain-inspired computing for in-process melt pool characterization in additive manufacturing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Brain-inspired computing for in-process melt pool characterization in additive manufacturing

Reference 35

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Observation a291c5aa-adbd-4072-b8a2-0c685f753707 · outbound

This paper cites Hierarchical representation and interpretable learning for accelerated quality monitoring in machin- ing process.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Hierarchical representation and interpretable learning for accelerated quality monitoring in machin- ing process

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.659086Z

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Observation 5d84234e-68ce-4189-a7c4-1b22dc4c4795 · outbound

This paper cites Graphd: Graph-based hyperdimensional mem- orization for brain-like cognitive learning.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Graphd: Graph-based hyperdimensional mem- orization for brain-like cognitive learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.695513Z

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Observation d2243c79-6409-4840-b546-794a3291ec1e · outbound

This paper cites Composition-based Multi-Relational Graph Convolutional Networks.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Composition-based Multi-Relational Graph Convolutional Networks

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:00:56.719052Z

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Observation 11e01977-eb2d-4201-8b9f-b575f8c03496 · outbound

This paper cites A theoretical perspective on hyperdimensional computing.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction A theoretical perspective on hyperdimensional computing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.681767Z

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Observation eae54c0f-7574-4f88-9ca8-a0d3d51a7af0 · outbound

This paper cites Adaptive document image binarization.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Adaptive document image binarization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.685699Z

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Observation 3b99bfc3-b927-4c70-8c98-ae38f8202e61 · outbound

This paper cites Porespy: A python toolkit for quantitative analysis of porous media images.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Porespy: A python toolkit for quantitative analysis of porous media images

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.652385Z

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Observation 4a3265c3-0c8f-4140-ad0b-c0e83f7a25c7 · outbound

This paper cites Measurement of sheet resistivities with the four-point probe.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Measurement of sheet resistivities with the four-point probe

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.644071Z

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Observation ab77b63d-3ff7-42de-9c9d-5eb39de3cbcc · outbound

This paper cites Correction factor tables for four-point probe resistivity measurements on thin, circular semiconductor samples.

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction Correction factor tables for four-point probe resistivity measurements on thin, circular semiconductor samples

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T11:19:33.646473Z

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