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Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. June 2025, 147(6): 061001.
Paper No: MANU-24-1389
Published Online: February 11, 2025
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. March 2025, 147(3): 031010.
Paper No: MANU-24-1226
Published Online: February 11, 2025
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. March 2025, 147(3): 031011.
Paper No: MANU-24-1374
Published Online: February 11, 2025
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. June 2025, 147(6): 061003.
Paper No: MANU-24-1440
Published Online: February 11, 2025
Journal Articles
Matthew Ebert, Ronnie F. P. Stone, John Koithan, Wenchao Zhou, Matt Pharr, Yuri Estrin, Ergun Akleman, Zhenghui Sha, Vinayak Krishnamurthy
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. June 2025, 147(6): 061002.
Paper No: MANU-24-1396
Published Online: February 11, 2025
Includes: Supplementary data
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. March 2025, 147(3): 031009.
Paper No: MANU-24-1598
Published Online: February 11, 2025
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 1 ( a ) Macroscopic image of the 143 samples printed with 27 parameter variations attached to the build plate. ( b ) Flat coupon design on a 5-mm tall support structure, with the respective ZY and XY planes labeled. More about this image found in ( a ) Macroscopic image of the 143 samples printed with 27 parameter variat...
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 2 A block diagram for active-learning-driven grid search for hyperparameter tuning More about this image found in A block diagram for active-learning-driven grid search for hyperparameter t...
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 3 XRD spectrum from samples, on the ZY plane, with a VED of 46, 65, and 87 J/mm 3 displaying a mixed, (200) and (111) texture More about this image found in XRD spectrum from samples, on the ZY plane, with a VED of 46, 65, and 87 ...
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 4 ( a ) Intensity ratio of the (111) to (100) peaks versus VED of all samples tested. ( b ) Three true stress–strain curves from the 46-, 65-, and 87-J/mm 3 sample groups. ( c ) Representative samples 46-A, 65-A, and 87-A J/mm 3 true stress–strain results, displaying the highest mixed (1:1)... More about this image found in ( a ) Intensity ratio of the (111) to (100) peaks versus VED of all samples...
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 5 EBSD IPFs of the (111) dominated texture (10A) at VED of 87 J/mm 3 , mixed (100)/(111) texture (05A) at VED of 46 J/mm 3 , and (100) dominated texture (13A) at VED of 65 J/mm 3 ( a – c ) XY and ( d – f ) ZY planes, with the measured grain size and column width and length labeled and th... More about this image found in EBSD IPFs of the (111) dominated texture (10A) at VED of 87 J/mm 3 , mixed ...
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 6 Confusion matrices for ( a ) the three-class classification of the (111) dominant, mixed, and (100) dominant texture for all 103 samples and ( b ) the two-class classification of the (100) dominant and non-(100) texture nominal samples. Each demonstrates the properly classified samples alon... More about this image found in Confusion matrices for ( a ) the three-class classification of the (111) do...
Image
in In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 7 True stress–strain curves for ( a ) nominal samples, with a VED of 54 J/mm 3 , with the minimum, maximum, and mixed (111)–(100) peak intensity ratio and ( b ) the nominal samples with the respective work hardening rates also shown More about this image found in True stress–strain curves for ( a ) nominal samples, with a VED of 54 J/mm ...
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 1 Working mechanism of the proposed method: ( a ) training procedure and ( b ) online prediction procedure More about this image found in Working mechanism of the proposed method: ( a ) training procedure and ( b ...
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 2 An example of the proposed continual learning model structure More about this image found in An example of the proposed continual learning model structure
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 3 Example figure demonstrating ( a ) bad machining; ( b ) good machining; ( c ) the raw AE signal from one of the LIPMM experiments; ( d ) the first segment; and ( e ) the second segment More about this image found in Example figure demonstrating ( a ) bad machining; ( b ) good machining; ( c...
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 4 AE segments from different process parameters and quality: ( a ) segment 1 from a bad-quality experiment under para-1; ( b ) segment 1 from a bad-quality experiment under para-2; and ( c ) segment 2 from a good-quality experiment under para-1 More about this image found in AE segments from different process parameters and quality: ( a ) segment 1 ...
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 5 Structure of the proposed 4Conv2h-AcouNet model More about this image found in Structure of the proposed 4Conv2h-AcouNet model
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 6 Training procedure of the proposed continual learning model: ( a ) scenario I, ( b ) scenario II, and ( c ) scenario III More about this image found in Training procedure of the proposed continual learning model: ( a ) scenario...
Image
in Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes
> Journal of Manufacturing Science and Engineering
Published Online: February 11, 2025
Fig. 7 Task prediction process for incoming segments (para-4 data). ( a ) Task relevance with task 1 versus relevance threshold of task 1. ( b ) Task relevance with task 2 versus relevance threshold of task 2. ( c ) Task relevance with task 3 versus relevance threshold of task 3. More about this image found in Task prediction process for incoming segments (para-4 data). ( a ) Task rel...
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