Graph Analytics For Smart Manufacturing

Emergence in the high-resolution sensing and imaging technologies have allowed us to track the variability in manufacturing processes occurring at every conceivable resolution of interest. However, representation of the underlying manufacturing processes using streaming sensor data remains a challenge. Efficient representations are critical for enabling real-time monitoring and quality assurance in smart manufacturing. Towards this, we present graph-based methods for efficient representation of the image data gathered from advanced manufacturing processes. In this dissertation, we first focus on experimental studies involving the finishing of complex additively manufactured components and discuss the important phenomenological details of the polishing process. Our experimental studies point to a material redistribution theory of polishing where material flows in the form of thin fluid like layers, eventually bridging up the neighboring asperities. Subsequently, we use the physics of the process gathered from this study to develop a random planar graph approach to represent the evolution of the surface morphology as gathered from electron microscopic images during mechanical polishing. In the next half of the dissertation, we focus on unsupervised image segmentation using graph cuts by iteratively estimating the image labels by solving the max-flow problem while optimally estimating the tuning parameters using maximum a posteriori estimation. We also establish the consistency of the posterior estimates. Applications of the method in benchmark and manufacturing case studies show more than 90% improvement in the segmentation performance as compared to state-of-the-art unsupervised methods. While the characterization of the advanced manufacturing processes using image and sensor data is increasingly sought after, it is equally important to perform characterization rapidly. The last chapter of this dissertation is set to focus on the rapid characterization of the salient microstructural phases present on a metallic workpiece surface via a nanoindentation-based lithography process. A summary of the contributions and directions of future work are also presented

Enhancement of Mahalanobis–Taguchi system via rough sets based feature selection

The current research presents a methodology for classification based on Mahalanobis Distance (MD) and Association Mining using Rough Sets Theory (RST). MD has been used in Mahalanobis Taguchi System (MTS) to develop classification scheme for systems having dichotomous states or categories. In MTS, selection of important features or variables to improve classification accuracy is done using Signal-to-Noise (S/N) ratios and Orthogonal Arrays (OAs). OAs has been reviewed for limitations in handling large number of variables. Secondly, penalty for over-fitting or regularization is not included in the feature selection process for the MTS classifier. Besides, there is scope to enhance the utility of MTS to a classification-cum-causality analysis method by adding comprehensive information about the underlying process which generated the data. This paper proposes to select variables based on maximization of degree-of-dependency between Subset of System Variables (SSVs) and system classes or categories (R). Degree-of-dependency, which reflects goodness-of-model and hence goodness of the SSV, is measured by conditional probability of system states on subset of variables. Moreover, a suitable regularization factor equivalent to L0 norm is introduced in an optimization problem which jointly maximizes goodness-of-model and effect of regularization. Dependency between SSVs and R is modeled via the equivalent sets of Rough Set Theory. Two new variants of MTS classifier are developed and their performance in terms of accuracy of classification is evaluated on test datasets from five case studies. The proposed variants of MTS are observed to be performing better than existing MTS methods and other classification techniques found in literature

Towards green automated production line with rotary transfer and turrets: a multi-objective approach using a binary scatter tabu search procedure

International audienceThis paper deals with a much less contemplated topic of carbon footprint (CFP) reduction in automated machining lines. A multi-objective problem for the simultaneous optimisation of line cost and carbon footprint while satisfying all the constraints of an automated production line with rotary transfer and turrets is presented. A framework for accounting and reduction of CFP and, hence, the energy consumption of the production line is put forward. This framework investigates the environmental impact of greenhouse gas emitted and energy consumed by the system-level processes during the machining and non-machining operations. Moreover, a binary scatter tabu search process for multi-objective optimisation (BSSPMO), a meta-heuristic based on scatter and tabu search procedure, is used for approximating the efficient frontier. To demonstrate the validation of the proposed approach, a case study is presented, and the numerical results are analysed. Though the trade-off between line cost and CFP is not a perfect Pareto, but it gives a very important conclusion about the trend of how these two critical parameters varies and the range in which it can be controlled