Parameter selection in non-traditional machining processes using a data mining approach

Dey, Somen; Chakraborty, Shankar

doi:10.5267/j.dsl.2014.12.001

Cited by 6 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Incorporating data mining into nanomaterials research aids in identifying new patterns, trends, and relationships in extensive datasets [ 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 ]. This is key to discovering novel properties of nanomaterials, predicting material behaviors, and guiding the creation of new nanocomposites with desired characteristics.…”

Section: Introductionmentioning

confidence: 99%

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Gomes Souza,

Bhansali,

Pal

et al. 2024

Materials

View full text Add to dashboard Cite

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the “State-of-the-Art and Gaps Extracted from Results and Discussions” section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the “Literature Review Guided by Artificial Intelligence” section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

show abstract

Section: Introductionmentioning

confidence: 99%

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Gomes Souza,

Bhansali,

Pal

et al. 2024

Materials

View full text Add to dashboard Cite

show abstract

“…Many of these devices, however, are often beyond the capabilities of traditional machining techniques. When working with some materials that are characterized by extreme hardness and brittleness, traditional machining methods such as chip formation, abrasion or micro-chipping are not possible [1]. Additionally, the size and precision required to fabricate many next-generation medical devices, especially implantable devices, often preclude traditional machining.…”

Section: Introductionmentioning

confidence: 99%

Femtosecond Laser Ablation of Implantable Materials

McEligot

Hara

Uthamaraj

2019

2019 Design of Medical Devices Conference

View full text Add to dashboard Cite

Femtosecond laser ablation is a powerful tool for the fabrication of medical devices, especially with materials commonly used in implantable micro devices as these materials are often incompatible with traditional machining techniques. Laser machining quality is highly dependent not only on the laser parameters, but also on the material that is being machined and so the process must be characterized for each material of interest. In this work, process characterization is presented for two materials commonly used in implantable micro devices — polyimide and silicon. The ablation threshold and etch rate were determined for femtosecond laser machining with a UV wavelength.

show abstract

Selection of Machining Parameters in Ultrasonic Machining Process Using CART Algorithm

Dandge¹,

Chakraborty

2019

Advanced Engineering Optimization Through Intelligent Techniques

View full text Add to dashboard Cite

Parameter selection in non-traditional machining processes using a data mining approach

Cited by 6 publications

References 21 publications

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

Femtosecond Laser Ablation of Implantable Materials

Selection of Machining Parameters in Ultrasonic Machining Process Using CART Algorithm

Contact Info

Product

Resources

About