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Abstract
Additive manufacturing technologies are a key point of the current era of Industry 4.0, promoting the production of mechanical components via the addition of subsequent layers of material. Then, they may be also used to produce surfaces tailored to achieve a desired mechanical contact response. In this work, we develop a method to prototype profiles optimizing a suitable trade-off between two different target mechanical responses. The mechanical design problem is solved relying on both physical assumptions and optimization methods. An algorithm is proposed, exploiting an analogy between genetics and the multiscale characterization of roughness, where various length-scales are described in terms of rough profiles, named chromosomes. Finally, the proposed algorithm is tested on a representative example, and the topological and spectral features of roughness of the optimized profiles are discussed.
Highlights
The role of the interface between different material constituents/phases is a main research topic in this current era of Industry 4.0, which is radically changing perspectives of industry about the technological manufacturing of components and/or materials [1, 2]
It reports the genomes used in the initialization of the Globally Convergent Method of Moving Asymptotes (GCMMA) algorithm in Steps (16) and (17) of Algorithm 1
Looking at the contact area-load curves reported in Figure 6(b), one can notice that the mechanical responses of all these three solutions diverge consistently from the target curve yt(2) in the range of contact pressures between p = 0.2 × 10−4 N/m and p = 0.9 × 10−4 N/m
Summary
The role of the interface between different material constituents/phases is a main research topic in this current era of Industry 4.0, which is radically changing perspectives of industry about the technological manufacturing of components and/or materials [1, 2]. One often requires the design of surface textures able to satisfy desired target responses and/or to allow rapid manufacturing and morphological changes. This can be useful, e.g., for the in-line control of mechanical components [3, 4]. Most approaches to design roughness are based either on mimicking natural surfaces [7, 8], or on performing parametric studies via artificially generated surfaces [9, 10] In this context, the present work proposes a method to design roughness with the aim of matching two given mechanical contact target responses as close as possible.
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