Quality of machined products is often related to the shapes of surfaces that are constrained by geometric tolerances. In this case, statistical quality monitoring should be used to quickly detect unwanted deviations from the nominal pattern. The majority of the literature has focused on statistical profile monitoring, while there is little research on surface monitoring. This paper faces the challenging task of moving from profile to surface monitoring. To this aim, different parametric approaches and control charting procedures are presented and compared with reference to a real case study dealing with cylindrical surfaces obtained by lathe-turning. In particular, a novel method presented in this paper consists of modeling the manufactured surface via Gaussian processes models and monitor the deviations of the actual surface from the target pattern estimated in Phase I. Regardless of the specific case study in this paper, the proposed approach is quite general and can be easily extended to deal with different kinds of surface or profiles.

In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

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