Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge

This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.Comment: IEEE Big Data 2016 Conferenc

Assessment of oral health among seafarers in Mundra Port, Kutch, Gujarat: a cross-sectional study

Background: Seafarer is a person who navigates waterborne vessels or assists as a crewmember in their operation and maintenance in all tough weather, but little research has been done to identify conditions that may lead to assess seafarer general health as well as oral health. Aim: To assess oral diseases including dental caries and periodontal conditions among seafarer’s population arrived in Mundra Port, Kutch, Gujarat, India. Materials and methods: A descriptive cross-sectional survey was conducted to assess oral health condition of seafarer community of Mundra Taluka of Kutch District, Gujarat, India, from July 2014 to September 2014. Results: Total of 385 subjects participated in the survey. Adverse habits show the overall 72.3% prevalence among the study population. Occurrence rate of caries, periodontal disease and prosthetic status were 88%, 75.1% and 6.5%, respectively. The best predictors for Decayed Missing Filled Teeth (DMFT), Community Periodontal Index (CPI) and prosthetic status were oral hygiene practices, adverse habit and educational status. Conclusions: Findings of the present study suggest that oral health condition of seafarer community was relatively poor, with high caries prevalence and poor periodontal health. This epidemiological survey has provided baseline information to underpin the implementation of oral health programmes

A dataset of synthetic hexagonal close packed 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation for two sets of constitutive parameters

This data article presents a data set comprised of 54 synthetic 3D equiaxed polycrystalline microstructures, the microstructural descriptors for each grain and the stress fields resulting from two sets of crystal plasticity simulations mimicking uniaxial tensile deformation to a total strain of 2%. This is related to the research article entitled “Applied Machine Learning to predict stress hotspots II: Hexagonal Close Packed Materials” (Mangal and Holm, 2018). The microstructures were created using an open source Dream.3D software tool and the crystal plasticity simulations were carried out using elasto-viscoplastic fast Fourier transform (EVPFFT) method. Eight different kinds of HCP textures are represented with stochastically different microstructures with varying texture intensity for each texture kind. For each texture kind, between six and nine stochastically different microstructures with varying texture intensity (measured by multiples of random density (MRD)) are created. This dataset is freely available in two Mendeley Data archives “Synthetic HCP 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation: Part One” and “Synthetic HCP 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation: Part Two” located at http://dx.doi.org/10.17632/kt8hfg4t2p.1 and http://dx.doi.org/10.17632/nsfn6tw295.1 respectively for any academic, educational, or research purposes