Predictive Manufacturing: A Classification Strategy to Predict Product Failures

Predicting failures can be considered a meaningful insight for the optimal planning of an industrial manufacturing process. In this era of advanced sensor technologies, when the collection of data from each step of the manufacturing process is common practice and advanced analytical skills enable us to efficiently analyze heterogeneous data streams, predicting failure becomes an achievable task. In this article: first, we propose a data mining strategy to deal with heterogeneous streams of data to predict failures in the production process; second, we aim to build a novel predictive manufacturing analytics model that employs a big data approach to predicting product failures; third, we illustrate the issue of high dimensionality, along with statistically redundant information; and, finally, our proposed method will be compared against the well-known classification methods (SVM, K-nearest neighbor, artificial neural networks). The results from real data show that our predictive manufacturing analytics approach, using genetic algorithms and Voronoi tessellations, is capable of predicting product failure with reasonable accuracy. The potential application of this method contributes to accurately predicting product failures, which would enable manufacturers to reduce production costs without compromising product quality.