Debejyo Chakraborty

General Motors Company | GM · Research & Development

Welding-induced distortion is a major concern in the assembly of automotive components. Finite element-based welding simulation plays an important role in predicting the distortion so that the welding process can be modified during the design phase to alleviate the distortion experienced in production. In this work, gas metal arc welding (GMAW)-ind...

Reducing the dimensional variability of the body-in-white (BIW) in automotive manufacturing is perhaps the most difficult quality control problem due to complex interdependencies amongst the multiple assembly stations that a BIW must pass through in a bodyshop. As increasing quantities of dimensional data are generated in factories, manufacturers f...

The use of advanced machine learning (ML) models for manufacturing could potentially reduce the pre-production testing and validation time for new processes. Once we decide that ML is indeed a suitable tool to apply in smart manufacturing processes, the challenge lies in training, validating, and testing an ML model in a pre-production environment...

Industrial Big Data (IBD) and Artificial Intelligence (AI) are propelling the new era of manufacturing - smart manufacturing. Manufacturing companies can competitively position themselves amongst the most advanced and influential companies by successfully implementing Quality 4.0 practices. Despite the global impact of COVID-19 and the low deployme...

Innovation and the marketplace have been pushing Statistical Process Control (SPC) outside its comfort zone, which requires a mature understanding of the product and process and a methodology for verifying the quality of each manufactured item. Especially when a new technology is proven to work and customer interest is high, companies want manufact...

Discussion of big data has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that bi...

Structural health monitoring is an important problem of interest in many civil infrastructure and aerospace applications. In the last few decades, many techniques have been investigated to address the detection, estimation, and classification of damage in structural components. One of the key challenges in the development of real-world damage ident...

The bandwidth and smoothness of windows play an important role in digital signal processing. In applications such as manufacturing process and quality monitoring, to radar target tracking and cellular communications, the design of appropriate windows is one of the crucial steps. In this paper the generalized normal function is introduced as a smoot...

The estimation of statistical distance between populations is a task of importance for many applications. Conventional methods often rely on the use of a maximum-likelihood (ML) estimator, usually due to its analytical and computational simplicity. However, the ML point estimate provides no information about the uncertainty in the parameters and di...

The effective detection and classification of damage in complex structures is an important task in the realization of structural health monitoring (SHM) systems. Conventional information processing techniques utilize statistical modeling machinery that requires large amounts of 'training' data which is usually difficult to obtain, leading to compro...

Adaptive learning techniques have recently been considered for structural health monitoring applications due to their flexibility and effectiveness in addressing real-world challenges such as variability in the monitoring of environmental and operating conditions. In this paper, an active learning data selection procedure is proposed that adaptivel...

A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data...

The analysis, detection, and classification of damage in complex bolted structures is an important component of structural health monitoring. In this article, an advanced signal processing and classification method is introduced based on time-frequency techniques. The time-varying signals collected from sensors are decomposed into linear combinatio...

Fatigue damage sensing and measurement in aluminum alloys is critical to estimating the residual useful lifetime of a range of aircraft structural components. In this work, we present electrical impedance and ultrasonic measurements in aluminum alloy 2024 that has been fatigued under high cycle conditions. While ultrasonic measurements can indicate...

We have recently proposed a method for classifying waveforms from healthy and damaged structures in a struc-tural health monitoring framework. This method is based on the use of hidden Markov models with preselected feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate the performance of the...

We investigate the use of low frequency (10-70 MHz) laser ultrasound for the detection of fatigue damage. While high frequency ultrasonics have been utilized in earlier work, unlike contacting transducers, laser-based techniques allow for simultaneous interrogation of the longitudinal and shear moduli of the fatigued material. The differential atte...

We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-densit...

The detection and classification of damage in complex materials and structures is essen- tial from both safety and economic perspectives. In this paper, we propose algorithms for the classification of structural damage based on time-frequency techniques. Our approach is based on matching damage features in the time-frequency plane using highly loca...

The ability to effectively detect and classify damage in com- plex materials and structures is an important problem in the area of structural health monitoring (SHM). The goal is to provide indicators about the presence, location, type, or s ever- ity of damage in a structure of interest. In this paper, we review two stochastic damage classificatio...

This paper reports the development of an efficient low power, low cost wireless network system for perimeter security that tracks a point target moving through a network of sensors. The system incorporates a sever-client topology with the cen- tral processor performing the tracking using acoustic data gen- erated by footsteps. Estimation of the tar...