Kriging models have been extensively used to predict spatial data in geostatistics and, more recently, to approximate the output of deterministic simulation. This paper presents a novel application of kriging in the field of industrial metrology. Exploiting the recognized predictive capability of kriging models, we use them to drive the online construction of sequential plans for inspecting industrial parts on coordinate measuring machines. These machines are universally adopted to check the compliance of parts to dimensional and geometric specifications. In two analyzed case studies kriging-based inspection plans outperform fixed-sample plans widespread in industry.

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The aim of this paper is to frame Data Science, a fashion and emerging topic nowadays in the context of business and industry. We open with a discussion about the origin of Data Science and its requirement for a challenging mix of capability in data analytics, information technology, and business know‐how. The mission of Data Science is to provide new or revised computational theory able to extract useful information from the massive volumes of data collected at an accelerating pace. In fact, besides the traditional measurements, digital data obtained from images, text, audio, sensors, etc complement the survey. Then, we review the different and most popular methodologies among the practitioners of Data Science research and applications. In addition, because the emerging field requires personnel with new competences, we attempt to describe the Data Scientist profile, one of the sexiest jobs of the 21st Century according to Davenport and Patil. Most people are aware of the need to embrace Data Science, but they feel intimidated that they do not understand it and they worry that their jobs will disappear. We want to encourage them: Data Science is more likely to add value to jobs and enrich the lives of working people by helping them make better, more informed business decisions. We conclude this paper by presenting examples of Data Science in action in business and industry, to demonstrate the collection of specialist skills that must come together for this new science to be effective.

Non‐stochastic simulation models, such as finite element or computational fluid dynamics, often support real experiments in industrial research. It has become a common practice to provide a meta‐model as computer experiments can be highly complex and time‐consuming, and the design space is often broad. The meta‐model is an approximation of the computer experiments response adapted both globally and locally on the design space, in order to capture local minima/maxima. The Kriging model, first proposed in Geostatistics, is doubtlessly the most popular meta‐model because of its recognized ability to provide high‐quality predictions. The underlying correlation structure can be evaluated either by estimating the parameters of correlation or by means of a variogram. In this paper, the performance of the Kriging model is compared with an Artificial Neural Network meta‐model in order to determine which model guarantees higher accuracy in predicting the result of four‐dimensional computational fluid dynamics experiments for low pressure turbines where energy loss values are provided. Copyright © 2016 John Wiley & Sons, Ltd.

This article focuses on the inference on the errors in manufactured parts controlled by using measurements devices. The characterization of the part surface topographies is core in several applications. A broad set of properties (tribological, optical, biological, mechanical, etc.) depends on the micro-and macrogeometry of the parts. Moreover, parts usually show typical deterministic geometric deviation pattern, referred to as manufacturing signatures, due to the specific manufacturing processes and process setup parameters adopted for their production. In several situations, the measurements may also be affected by systematic errors due to the measurement process, that might be caused, for example, by a poor part alignment during the measurement process. Measurement techniques and characterization methods have been standardized in the International Standard ISO 25178, defining parameters characterizing the surface topography and supplying methods and formula adapt to deal with this issue computationally. In the present article, we consider a type of spatial dependence between measured values at different points that suggest the use of the variogram to identify patterns in the parts. We offer a comparison, based on a real set of measures, between the latter approach and the conventional as a test of the efficient performance of our findings.

Computer experiments, Latin hypercube, Mean square prediction error, Gaussian linear prediction,

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Abstract. Manufactured parts are affected by size and form errors, which need to be assessed against geometrical and dimensional tolerances in order to meet the functional requirements they have been conceived for. The compliance assessment of workpieces with specifications depends on measurements and is unavoidably affected by several uncertainty contributions. According to the geometrical product specifications and verification (GPS), measurement uncertainty consists of method and implementation uncertainty. Literature proposes different approaches for the evaluation of implementation uncertainty, however a standardized method has not been yet achieved. This paper makes an analysis of the various elements to be considered when choosing a specific approach: in the implementation uncertainty of circular features. The most common manufacturing signatures affecting circular profiles were considered, together with the number of points necessary for a reliable estimation of implementation uncertainty.