Distributed dynamic elastic nets: A scalable approach for regularization in dynamic manufacturing environments

In this paper, we focus on the task of learning influential parameters under unsteady and dynamic environments. Such unsteady and dynamic environments often occur in the ramp-up phase of manufacturing. We propose a novel regularization-based framework, called Distributed Dynamic Elastic Nets (DDEN), for this problem and formulate it as a convex optimization objective. Our approach solves the optimization problem using a distributed framework. Consequently it is highly scalable and can easily be applied to very large datasets. We implement a L-BFGS based solver in the Apache Spark framework. For validating our algorithm, we consider the issue of scrap reduction at an assembly line during the ramp-up phase of a manufacturing plant. By considering the logistic regression as a sample model, we evaluate the performance of our approach although extensions of the proposed regularizer to other classification and regression techniques is straightforward. Through experiments on data collected at a functioning manufacturing plant, we show that the proposed method not only reduces model variance but also helps preserve the relative importance of features in dynamic conditions compared to standard approaches. The experiments further show that the classification performance of DDEN if often better than logistic regression with standard elastic nets for datasets from dynamic and unsteady environments. We are collaborating with manufacturing units to use this process for improving production yields during the ramp-up phase. This work serves as a demonstration of how data mining can be used to solve problem in manufacturing.