11 Analyzing the dimensional stability in direct ink written composite ink: a machine learning approach
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Amit Rai Dixit
Abstract
Direct ink writing (DIW) is a burgeoning additive manufacturing (AM) technique esteemed for its cost-effectiveness. The meticulous control of process parameters, encompassing printing speed (V), the distance between the printing bed and nozzle bottom (h), pulses per microliter for material extrusion (p), and extrusion multiplier (m), holds paramount significance in ensuring precise dimensional accuracy in DIW-produced models. Insights about the extent of impact of these four parameters are significant to maintain dimensional accuracy. In this work, k-means clustering, an unsupervised machine learning algorithm from the Scikit-learn machine learning library of Python has been applied to identify clusters in an experimental dataset that predicts the dimensional accuracy of printed PDMS-CNT ink. In this research, experimental data of the four-dimensional input matrix is taken to estimate the impact of the individual process parameters on the cross-sectional deviation. The elbow method and silhouette coefficient were used to find out the optimum number of clusters for higher distinguishability. The high distinguishability ensured that the future experimental runs can be used to predict the appropriate cluster and thus the dimensional accuracy of printed PDMS-CNT ink.
Abstract
Direct ink writing (DIW) is a burgeoning additive manufacturing (AM) technique esteemed for its cost-effectiveness. The meticulous control of process parameters, encompassing printing speed (V), the distance between the printing bed and nozzle bottom (h), pulses per microliter for material extrusion (p), and extrusion multiplier (m), holds paramount significance in ensuring precise dimensional accuracy in DIW-produced models. Insights about the extent of impact of these four parameters are significant to maintain dimensional accuracy. In this work, k-means clustering, an unsupervised machine learning algorithm from the Scikit-learn machine learning library of Python has been applied to identify clusters in an experimental dataset that predicts the dimensional accuracy of printed PDMS-CNT ink. In this research, experimental data of the four-dimensional input matrix is taken to estimate the impact of the individual process parameters on the cross-sectional deviation. The elbow method and silhouette coefficient were used to find out the optimum number of clusters for higher distinguishability. The high distinguishability ensured that the future experimental runs can be used to predict the appropriate cluster and thus the dimensional accuracy of printed PDMS-CNT ink.
Chapters in this book
- Frontmatter I
- Acknowledgments V
- Preface VII
- Contents XI
- List of contributors XV
- 1 3D-printed antennas 1
- 2 The recent developments in 3D bioprinting: a general bibliometric study and thematic investigation 39
- 3 Additive manufacturing of compositionally complex alloys: trends, challenges, and future perspectives 61
- 4 Adoptability of additive manufacturing process: design perceptive 77
- 5 Advanced bioprinting processes using additive manufacturing technologies: revolutionizing tissue engineering 95
- 6 Comparative analysis of thermal characteristics and optimizing laminar flow within medical-grade 3D printers for fabrication of sterile patientspecific implants (PSIs) using computational fluid dynamics 119
- 7 Review of 4D printing and materials enabling Industry 4.0 for implementation in manufacturing: an Indian context 143
- 8 Processing of smart materials by additive manufacturing and 4D printing 181
- 9 A comprehensive review on effect of DMLS process parameters and post-processing on quality of product in biomedical field 197
- 10 Finite element method investigation on delamination of 3D printed hybrid composites during the drilling operation 223
- 11 Analyzing the dimensional stability in direct ink written composite ink: a machine learning approach 235
- 12 Recent applications of rapid prototyping with 3D printing: a review 245
- 13 3D printing insight: techniques, application, and transformation 259
- 14 Additive manufacturing and 4D printing applications for Industry 4.0-enabled digital biomedical and pharmaceutical sectors 289
- 15 Application of three-dimensional printing in medical, agriculture, engineering, and other sectors 311
- 16 Recent developments in 3D printing: a critical analysis and deep dive into innovative real-world applications 335
- 17 Exploring design strategies for enhanced 3D printing performance 353
- Biographies 371
- Index 375
Chapters in this book
- Frontmatter I
- Acknowledgments V
- Preface VII
- Contents XI
- List of contributors XV
- 1 3D-printed antennas 1
- 2 The recent developments in 3D bioprinting: a general bibliometric study and thematic investigation 39
- 3 Additive manufacturing of compositionally complex alloys: trends, challenges, and future perspectives 61
- 4 Adoptability of additive manufacturing process: design perceptive 77
- 5 Advanced bioprinting processes using additive manufacturing technologies: revolutionizing tissue engineering 95
- 6 Comparative analysis of thermal characteristics and optimizing laminar flow within medical-grade 3D printers for fabrication of sterile patientspecific implants (PSIs) using computational fluid dynamics 119
- 7 Review of 4D printing and materials enabling Industry 4.0 for implementation in manufacturing: an Indian context 143
- 8 Processing of smart materials by additive manufacturing and 4D printing 181
- 9 A comprehensive review on effect of DMLS process parameters and post-processing on quality of product in biomedical field 197
- 10 Finite element method investigation on delamination of 3D printed hybrid composites during the drilling operation 223
- 11 Analyzing the dimensional stability in direct ink written composite ink: a machine learning approach 235
- 12 Recent applications of rapid prototyping with 3D printing: a review 245
- 13 3D printing insight: techniques, application, and transformation 259
- 14 Additive manufacturing and 4D printing applications for Industry 4.0-enabled digital biomedical and pharmaceutical sectors 289
- 15 Application of three-dimensional printing in medical, agriculture, engineering, and other sectors 311
- 16 Recent developments in 3D printing: a critical analysis and deep dive into innovative real-world applications 335
- 17 Exploring design strategies for enhanced 3D printing performance 353
- Biographies 371
- Index 375
