Samik Dutta - Academia.edu (original) (raw)

Papers by Samik Dutta

Research paper thumbnail of A synchronized coupled position control architecture for a 3$$\bar{P}E$$ positioning stage parallel manipulator (PSPM)

Journal of the Brazilian Society of Mechanical Sciences and Engineering

Research paper thumbnail of Deciphering the interdependent impact of process parameters in friction stir welding - Part I: an overview of the challenges and way forward

Materials and Manufacturing Processes

Research paper thumbnail of Quantifying corrosion inhibition on mild steel surface using run length statistics-based texture analysis

Journal of Adhesion Science and Technology

Research paper thumbnail of A Critical Review on the Trends Toward Effective Online Monitoring of Defects in Friction Stir Welding of Aluminum Alloys

Lecture Notes in Mechanical Engineering, 2021

Research paper thumbnail of A Critical Review on the Trends Toward Effective Online Monitoring of Defects in Friction Stir Welding of Aluminum Alloys

Research paper thumbnail of Artificial Intelligence and Machine Learning in Manufacturing

Springer Series in Advanced Manufacturing, 2021

The diagnosis of manufacturing processes and systems, prediction of machine health for corrective... more The diagnosis of manufacturing processes and systems, prediction of machine health for corrective measures are mainly achieved through various machine learning techniques. In the previous chapters, discussions were held around the signal and image processing techniques, using which meaningful information was gathered from the raw data. The results are validated by correlating with the experiments.

Research paper thumbnail of On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

Precision Engineering, 2016

Abstract In this paper, a method for on-machine tool condition monitoring by processing the turne... more Abstract In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.

Research paper thumbnail of In-situ workpiece perception: A key to zero-defect manufacturing in Industry 4.0 compliant job shops

Research paper thumbnail of A holistic design and modelling approach applied to the development of ultra precision CMM for micro and nano technology

Research paper thumbnail of An Wavelet-based Characterization of Fractographs

Research paper thumbnail of Study of Accuracy of a Long-Travel-Range, Ultra-High-Resolution, Hybrid-Drive Translational Stage using Laser Interferometer

Research paper thumbnail of Influence study of machining parameters on micro level surface finish of OFHC copper

Research paper thumbnail of Application of digital image processing in tool condition monitoring: A review

CIRP Journal of Manufacturing Science and Technology, 2013

Research paper thumbnail of Investigation on laser forming of open cell aluminum foam

Journal of Laser Applications

Open cell aluminum foam having high porosity has the potential to increase the efficiency of a he... more Open cell aluminum foam having high porosity has the potential to increase the efficiency of a heat exchanger and also to be used for diverse other functions. However, being prone to fail easily under tensile mechanical load, their thermal forming using a laser has been proposed in the literature. This work investigates the effect of laser parameters, orientation-position-curvature of scan path, the number of scans, and foam thickness on the bending angle achieved while forming 95% porous pure aluminum (99.7% aluminum) open cell foam plates using a diode laser. Furthermore, the capability of laser forming to produce developable and nondevelopable surfaces out of this foam has been demonstrated. Higher line energy gave a higher bending angle. Under the same line energy, the combination of higher power-higher scan speed produced a higher bending angle. In contradiction to laser forming of the sheet metal, no saturation or reduction in bending angle per scan pass was observed with an i...

Research paper thumbnail of In-situ process reliability monitoring strategy for friction stir welding machine

Materials Today: Proceedings

Research paper thumbnail of Tool wear classification based on machined surface images using convolution neural networks

Sādhanā, 2021

Among several factors that are having a profound impact on the overall machining process efficien... more Among several factors that are having a profound impact on the overall machining process efficiency, cutting tool wear is the most significant one. Monitoring and identification of cutting tool wear state well before to its failure is important to achieve superior machining quality and profitable production. With the recent advancements in computational hardware, significant amount of research is being carried out on using deep learning techniques, in specific, convolution neural networks (CNN) for developing cutting tool wear monitoring system. Although, few researchers reported the use of CNN as a pathway to tool wear classification problems with significant results, the fundamental methodology adopted by these techniques still needs to be investigated. Hence, in the present work, a deep CNN architecture is designed by choosing appropriate hyper-parameters and a CNN model is developed by selecting proper training parameters for cutting tool wear classification. Machined surface images acquired during turning operation performed on mild steel components under dry condition by uncoated carbide inserts as cutting tool are used as input data to the CNN model for predicting the tool condition. The proposed model, whose classification performance is independent of machining conditions, has capability to extract the features and classify the cutting tool among the two classes (i.e., unworn and worn classes). Accuracies of 96.3% and 99.9% are realized for classification of tool flank wear from raw and minimally pre-processed (contrast enhanced) machined surface images, respectively.

Research paper thumbnail of Laser forming of difficult-to form Al-SiC composite foam – Experimental and numerical analyses

Optics & Laser Technology, 2022

Research paper thumbnail of Digital Twin – Fundamental Concepts to Applications in Advanced Manufacturing

Springer Series in Advanced Manufacturing, 2022

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Research paper thumbnail of Sensor Electronics for Digital Twin

Springer Series in Advanced Manufacturing, 2021

For building a digital twin, sensors and associated electronic components will be at the core as ... more For building a digital twin, sensors and associated electronic components will be at the core as they would sense and gather necessary information from the physical machine.

Research paper thumbnail of Digital Twin Application

Springer Series in Advanced Manufacturing, 2021

This chapter introduces the concept of digital twin in details. The explanations given in Chap. 1... more This chapter introduces the concept of digital twin in details. The explanations given in Chap. 1 about building a digital twin model are revisited and the ideas are elaborated. Digital twins proposed in different routes of manufacturing are also discussed.

Research paper thumbnail of A synchronized coupled position control architecture for a 3$$\bar{P}E$$ positioning stage parallel manipulator (PSPM)

Journal of the Brazilian Society of Mechanical Sciences and Engineering

Research paper thumbnail of Deciphering the interdependent impact of process parameters in friction stir welding - Part I: an overview of the challenges and way forward

Materials and Manufacturing Processes

Research paper thumbnail of Quantifying corrosion inhibition on mild steel surface using run length statistics-based texture analysis

Journal of Adhesion Science and Technology

Research paper thumbnail of A Critical Review on the Trends Toward Effective Online Monitoring of Defects in Friction Stir Welding of Aluminum Alloys

Lecture Notes in Mechanical Engineering, 2021

Research paper thumbnail of A Critical Review on the Trends Toward Effective Online Monitoring of Defects in Friction Stir Welding of Aluminum Alloys

Research paper thumbnail of Artificial Intelligence and Machine Learning in Manufacturing

Springer Series in Advanced Manufacturing, 2021

The diagnosis of manufacturing processes and systems, prediction of machine health for corrective... more The diagnosis of manufacturing processes and systems, prediction of machine health for corrective measures are mainly achieved through various machine learning techniques. In the previous chapters, discussions were held around the signal and image processing techniques, using which meaningful information was gathered from the raw data. The results are validated by correlating with the experiments.

Research paper thumbnail of On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

Precision Engineering, 2016

Abstract In this paper, a method for on-machine tool condition monitoring by processing the turne... more Abstract In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.

Research paper thumbnail of In-situ workpiece perception: A key to zero-defect manufacturing in Industry 4.0 compliant job shops

Research paper thumbnail of A holistic design and modelling approach applied to the development of ultra precision CMM for micro and nano technology

Research paper thumbnail of An Wavelet-based Characterization of Fractographs

Research paper thumbnail of Study of Accuracy of a Long-Travel-Range, Ultra-High-Resolution, Hybrid-Drive Translational Stage using Laser Interferometer

Research paper thumbnail of Influence study of machining parameters on micro level surface finish of OFHC copper

Research paper thumbnail of Application of digital image processing in tool condition monitoring: A review

CIRP Journal of Manufacturing Science and Technology, 2013

Research paper thumbnail of Investigation on laser forming of open cell aluminum foam

Journal of Laser Applications

Open cell aluminum foam having high porosity has the potential to increase the efficiency of a he... more Open cell aluminum foam having high porosity has the potential to increase the efficiency of a heat exchanger and also to be used for diverse other functions. However, being prone to fail easily under tensile mechanical load, their thermal forming using a laser has been proposed in the literature. This work investigates the effect of laser parameters, orientation-position-curvature of scan path, the number of scans, and foam thickness on the bending angle achieved while forming 95% porous pure aluminum (99.7% aluminum) open cell foam plates using a diode laser. Furthermore, the capability of laser forming to produce developable and nondevelopable surfaces out of this foam has been demonstrated. Higher line energy gave a higher bending angle. Under the same line energy, the combination of higher power-higher scan speed produced a higher bending angle. In contradiction to laser forming of the sheet metal, no saturation or reduction in bending angle per scan pass was observed with an i...

Research paper thumbnail of In-situ process reliability monitoring strategy for friction stir welding machine

Materials Today: Proceedings

Research paper thumbnail of Tool wear classification based on machined surface images using convolution neural networks

Sādhanā, 2021

Among several factors that are having a profound impact on the overall machining process efficien... more Among several factors that are having a profound impact on the overall machining process efficiency, cutting tool wear is the most significant one. Monitoring and identification of cutting tool wear state well before to its failure is important to achieve superior machining quality and profitable production. With the recent advancements in computational hardware, significant amount of research is being carried out on using deep learning techniques, in specific, convolution neural networks (CNN) for developing cutting tool wear monitoring system. Although, few researchers reported the use of CNN as a pathway to tool wear classification problems with significant results, the fundamental methodology adopted by these techniques still needs to be investigated. Hence, in the present work, a deep CNN architecture is designed by choosing appropriate hyper-parameters and a CNN model is developed by selecting proper training parameters for cutting tool wear classification. Machined surface images acquired during turning operation performed on mild steel components under dry condition by uncoated carbide inserts as cutting tool are used as input data to the CNN model for predicting the tool condition. The proposed model, whose classification performance is independent of machining conditions, has capability to extract the features and classify the cutting tool among the two classes (i.e., unworn and worn classes). Accuracies of 96.3% and 99.9% are realized for classification of tool flank wear from raw and minimally pre-processed (contrast enhanced) machined surface images, respectively.

Research paper thumbnail of Laser forming of difficult-to form Al-SiC composite foam – Experimental and numerical analyses

Optics & Laser Technology, 2022

Research paper thumbnail of Digital Twin – Fundamental Concepts to Applications in Advanced Manufacturing

Springer Series in Advanced Manufacturing, 2022

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Research paper thumbnail of Sensor Electronics for Digital Twin

Springer Series in Advanced Manufacturing, 2021

For building a digital twin, sensors and associated electronic components will be at the core as ... more For building a digital twin, sensors and associated electronic components will be at the core as they would sense and gather necessary information from the physical machine.

Research paper thumbnail of Digital Twin Application

Springer Series in Advanced Manufacturing, 2021

This chapter introduces the concept of digital twin in details. The explanations given in Chap. 1... more This chapter introduces the concept of digital twin in details. The explanations given in Chap. 1 about building a digital twin model are revisited and the ideas are elaborated. Digital twins proposed in different routes of manufacturing are also discussed.