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Technologies
Special Issues
Modern Methods for Measuring the Functional Characteristics of Surfaces
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Modern Methods for Measuring the Functional Characteristics of Surfaces

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Innovations in Materials Processing".

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 18679

Special Issue Editors

Prof. Dr. Grzegorz Królczyk

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Department of Manufacturing Engineering and Production Automation, Faculty of Mechanical Engineering, Opole University of Technology, 5 Mikolajczyka Street, 45-271 Opole, Poland
Interests: surface metrology; optimization of difficult-to-cut materials; sensor technology; metrology; measurement uncertainty; environmental measurement; optimization of geometrical and physical parameters of surface integrity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Richard Leach

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Guest Editor
Manufacturing Metrology Team, Faculty of Engineering, University of Nottingham, Advanced Manufacturing Building, Jubilee Campus, Nottingham NG8 1BB, UK
Interests: basics of metrology; dimensional metrology; surface metrology; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michal Wieczorowski

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Guest Editor
Faculty of Mechanical Engineering and Management, Poznan University of Technology, Poznan, Poland
Interests: surface metrology; topography; coordinate measuring technique; computed tomography; scanning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface topography has a profound influence on the function of a surface. In industrial practice, geometric product specification is an important issue. The measurement and characterization of the geometric features of machined parts is important when trying to determine the functional properties of surfaces, and also in the control of process parameters during manufacturing. However, there are many other areas of science, engineering and even the arts where surface topography is critical to function.

The aim of this Special Issue is to provide an international forum for the dissemination of scientific information on surface metrology—submission from all fields involving the measurement and characterization of surface topography, including archaeology, art conservation, anthropology, biology, biomedical engineering, chemistry, civil engineering, food science, forensics, geodetics, geology, material science, mechanical engineering, manufacturing, metrology, nanotechnology, tribology, and others. The Special Issue covers the modelling, design, and characterization of surfaces and the relationship between surface properties and their applications. This Special Issue accepts high-quality articles containing original research results and review papers regarding metrology, development, and application of the science and technology of measurement, instrumentation and characterisation.

Prof. Dr. Grzegorz Krolczyk
Prof. Dr. Richard Leach
Prof. Dr. Michal Wieczorowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (3 papers)

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Research

21 pages, 8376 KiB  
Article
Gathering and Analyzing Surface Parameters for Diet Identification Purposes
by Arthur Francisco, Noël Brunetière and Gildas Merceron
Technologies 2018, 6(3), 75; https://doi.org/10.3390/technologies6030075 - 11 Aug 2018
Cited by 5 | Viewed by 5330
Abstract
Modern surface acquisition devices, such as interferometers and confocal microscopes, make it possible to have accurate three-dimensional (3D) numerical representations of real surfaces. The numerical dental surfaces hold details that are related to the microwear that is caused by food processing. As there [...] Read more.
Modern surface acquisition devices, such as interferometers and confocal microscopes, make it possible to have accurate three-dimensional (3D) numerical representations of real surfaces. The numerical dental surfaces hold details that are related to the microwear that is caused by food processing. As there are numerous surface parameters that describe surface properties and knowing that a lot more can be built, is it possible to identify the ones that can separate taxa based on their diets? Until now, the candidates were chosen from among those provided by metrology software, which often implements International Organization for Standardization (ISO) parameters. Moreover, the way that a parameter is declared as diet-discriminative differs from one researcher to another. The aim of the present work is to propose a framework to broaden the investigation of relevant parameters and subsequently a procedure that is based on statistical tests to highlight the best of them. Many parameters were tested in a previous study. Here, some were dropped and others added to the classical ones. The resulting set is doubled while considering two derived surfaces: the initial one minus a second order and an eighth order polynomial. The resulting surfaces are then sampled—256 samples per surface—making it possible to build new derived parameters that are based on statistics. The studied dental surfaces belong to seven sets of three or more groups with known differences in diet. In almost all cases, the statistical procedure succeeds in identifying the most relevant parameters to reflect the group differences. Surprisingly, the widely used Area-scale fractal complexity (Asfc) parameter—despite some improvements—cannot differentiate the groups as accurately. The present work can be used as a standalone procedure, but it can also be seen as a first step towards machine learning where a lot of training data is necessary, thus making the human intervention prohibitive. Full article
(This article belongs to the Special Issue Modern Methods for Measuring the Functional Characteristics of Surfaces)
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15107 KiB  
Article
Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation
by Matthias Eifler, Felix Ströer, Sebastian Rief and Jörg Seewig
Technologies 2018, 6(1), 3; https://doi.org/10.3390/technologies6010003 - 22 Dec 2017
Cited by 8 | Viewed by 6067
Abstract
Standard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal [...] Read more.
Standard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal networks (ANN) are one approach for the recognition or classification of technical surfaces. However the required set of training data for ANN is often not available, especially when data acquisition is time consuming or expensive—as e.g., measuring surface topography. Thus, generation of artificial (simulated) data becomes of interest. An approach from time series analysis is chosen and examined regarding its suitability for the description of technical surfaces: the ARMAsel model, an approach for time series modelling which is capable of choosing the statistical model with the smallest prediction error and the best number of coefficients for a certain surface. With a reliable model which features the relevant stochastic properties of a surface, a generation of training data for classifiers of artificial neural networks is possible. Based on the determined ARMA-coefficients from the ARMAsel-approach, with only few measured datasets many different artificial surfaces can be generated which can be used for training classifiers of an artificial neural network. In doing so, an improved calculation of the model input data for the generation of artificial surfaces is possible as the training data generation is based on actual measurement data. The trained artificial neural network is tested with actual measurement data of surfaces that were manufactured with varying manufacturing methods and a recognition rate of the according manufacturing principle between 60% and 78% can be determined. This means that based on only few measured datasets, stochastic surface information of various manufacturing principles can be extracted in a way that a distinction of these surfaces is possible by an ANN. The ARMAsel approach is proven to provide the relevant stochastic information for the training of the ANN with artificially generated lapped, reamed, ground, horizontally milled, milled and turned surface profiles. Full article
(This article belongs to the Special Issue Modern Methods for Measuring the Functional Characteristics of Surfaces)
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4266 KiB  
Article
An Approach for the Simulation of Ground and Honed Technical Surfaces for Training Classifiers
by Sebastian Rief, Felix Ströer, Simon Kieß, Matthias Eifler and Jörg Seewig
Technologies 2017, 5(4), 66; https://doi.org/10.3390/technologies5040066 - 14 Oct 2017
Cited by 8 | Viewed by 6117
Abstract
Training of neural networks requires large amounts of data. Simulated data sets can be helpful if the data required for the training is not available. However, the applicability of simulated data sets for training neuronal networks depends on the quality of the simulation [...] Read more.
Training of neural networks requires large amounts of data. Simulated data sets can be helpful if the data required for the training is not available. However, the applicability of simulated data sets for training neuronal networks depends on the quality of the simulation model used. A simple and fast approach for the simulation of ground and honed surfaces with predefined properties is being presented. The approach is used to generate a diverse data set. This set is then applied to train a neural convolution network for surface type recognition. The resulting classifier is validated on the basis of a series of real measurement data and a classification rate of >85% is achieved. A possible field of application of the presented procedure is the support of measurement technicians in the standard-compliant evaluation of measurement data by suggestion of specific data processing steps, depending on the recognized type of manufacturing process. Full article
(This article belongs to the Special Issue Modern Methods for Measuring the Functional Characteristics of Surfaces)
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