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Purpose The purpose of this paper is to propose a methodology to quantify the error on wear volume evaluation using optical interferometry with image analysis (OI+IA), to establish a lower threshold for wear mapping in practical applications. Design/methodology/approach A three-dimensional surface wear map is quantified by measuring the same area of a surface before and after a wear process using optical interferometry. Then, by subtracting the matching images, the wear map (volume of wear) is obtained. To access the error related to wear mapping, the difference between several consecutive measurements of the same unworn surface was performed and deeply investigated. Findings The paper shows that the difference between two consecutive measurements of the same unworn surface, which ideally should be zero, is not. Thus, the magnitude of this “wear map” is the error. The main causes of such uncertainties are because of sample motion in a subpixel scale; a combination between surface roughness with the selected resolution; and numerical errors on the relocation process that is used to match the surfaces before subtracting them. Practical implications The proposed methodology allows one to define the lower threshold for wear map analysis using OI+IA. To know the limitation of OI+IA for wear mapping prevents misevaluation of the so-called almost-zero-wear. Originality/value This paper covers and identifies main uncertainties and numerical errors related to optical interferometry assisted by image analysis for wear mapping. Several other papers deal with uncertainties of OI; however, this paper proposes a simple methodology to evaluate the lower threshold for wear mapping. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0354

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Many modern real-world designs rely on the optimization of multiple competing goals. For example, most components designed for the aerospace industry must meet some conflicting expectations. In such applications, low weight, low cost, high reliability, and easy manufacturability are desirable. In some cases, bounds for these requirements are not clear, and performing mono-objective optimizations might not provide a good landscape of the required optimal design choices. For these cases, finding a set of Pareto optimal designs might give the designer a comprehensive set of options from which to choose the best design. This article shows the main features and functionalities of an open source package, developed by the present authors, to solve constrained multi-objective problems. The package, named moko (acronym for Multi-Objective Kriging Optimization), was built under the open source programming language R. Popular Kriging based multiobjective optimization strategies, as the expected volume improvement and the weighted expected improvement, are available in the package. In addition, an approach proposed by the authors, based on the exploration using a predicted Pareto front is implemented. The latter approach showed to be more efficient than the two other techniques in some case studies performed by the authors with moko.

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