Special Issue "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: 20 June 2018

Special Issue Editors

Guest Editor
Prof. Dr. Grzegorz Królczyk

Faculty of Mechanical Engineering, Opole University of Technology, 5 Mikolajczyka Street, 45-271 Opole, Poland
Website | E-Mail
Interests: sustainable machining; surface metrology; MQL; dry cutting
Guest Editor
Prof. Dr. Richard Leach

Faculty of Engineering, The University of Nottingham, B92, Coates Building, University Park, Nottingham, NG7 2RD, UK
Website | E-Mail
Interests: surface metrology; advanced manufacturing metrology; additive manufacturing
Guest Editor
Prof. Dr. Michal Wieczorowski

Faculty of Mechanical Engineering and Management, Poznan University of Technology, 3 Piotrowo St., 60-965 Poznan, Poland
Interests: surface metrology; surface topography; measurement systems

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 papers will be 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 quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

View options order results:
result details:
Displaying articles 1-2
Export citation of selected articles as:


Open AccessArticle Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation
Technologies 2018, 6(1), 3; doi:10.3390/technologies6010003
Received: 30 November 2017 / Revised: 18 December 2017 / Accepted: 20 December 2017 / Published: 22 December 2017
PDF Full-text (15107 KB) | HTML Full-text | XML Full-text
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

Figure 1

Open AccessFeature PaperArticle An Approach for the Simulation of Ground and Honed Technical Surfaces for Training Classifiers
Technologies 2017, 5(4), 66; doi:10.3390/technologies5040066
Received: 19 September 2017 / Revised: 5 October 2017 / Accepted: 11 October 2017 / Published: 14 October 2017
Cited by 1 | PDF Full-text (4266 KB) | HTML Full-text | XML Full-text
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

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Comparative analysis of surface topography of custom CAD/CAM zirconia abutments by means of optical profilometry 
Author: Peter Gehrke   
Abstract: Objective: CAD/CAM generated ceramic implant abutments have recently attracted interest due to their superior customization possibilities and aesthetic advantages. Despite their widespread clinical use, little information is currently available on their surface topography, however. The transmucosal portion of the abutment shoulder is of particular interest, as it ideally supports soft tissue but minimizes mechanical plaque retention. The aim of this in vitro study was to topographically characterize the trans- and subgingival roughness of CAD/CAM zirconia abutments from different manufacturers and compare them with zirconia stock abutments. Material and Method: The surface topography of eight CAD/CAM zirconia implant abutments (tests) and two prefabricated zirconia stock abutments (controls) was determined using focus variation microscopy. Two points on the abutment shoulder were subjected to profilometric examination. 2D and 3D parameters of roughness were obtained and compared. Results: The surface roughness of all the test abutments exceeded the recommended threshold of Ra = 0.2 µm and therefore exhibited an increased risk of mechanical plaque retention. Obvious differences in surface structure were apparent, allowing conclusions to be drawn about the manufacturing method and subsequent reworking processes. Conclusion: Manually reworking the trans- and submucosal area of the investigated CAD/CAM zirconia abutments appears necessary to fulfil the conditions for optimal surface topography. The Sa value as arithmetic mean, taking the maximum height (Sz value) and surface excess (Sdr) into account, is an essential parameter for assessing the surface topography of implant abutments.
Keywords: CAD/CAM abutments; zirconia; surface roughness; soft tissue adhesion; focus variation microscopy

Title: General concept of measurements of form deviations of 3D rotary elements with the use of the adaptive strategy.
Authors: Krzysztof Stępień, Dariusz Janecki, Stanisław Adamczak
Affiliation: Kielce University of Technology
Abstract: Measurements of form deviations of 3D elements can be conducted with the use of various strategies, differing in a number and distribution of sampling points located on an investigated surface. Low number of sampling points or measured sections can lead to the situation that some surface irregularities are not detected by a measuring system. An application of higher density of sampling points, in turn, results in significant lengthening of measurement time, which is undesirable, if one takes into account requirements of modern manufacturing processes. This is authors propose to develop a novel, adaptive measurement strategy. Proposed adaptive strategy consists of two stages: a preliminary measurement and additional measurements. During the preliminary measurement an investigated area is scanned along preselected trajectory. If measurement results show that there is significant change of sensor readings in a certain fragment of an investigated surface, then we conduct additional measurements in the cross-section and in the longitudinal section that go across the area where the change of sensor readings occurred.
Keywords: form deviation, adaptive strategy, measurement


Back to Top