scholarly journals Solving the Issue of Discriminant Roughness of Heterogeneous Surfaces Using Elements of Artificial Intelligence

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2620
Author(s):  
Milena Kubišová ◽  
Vladimír Pata ◽  
Dagmar Měřínská ◽  
Adam Škrobák ◽  
Miroslav Marcaník
Keyword(s):  
Neural Networks ◽  
Surface Quality ◽  
Statistical Parameter ◽  
Measured Data ◽  
Metallic Materials ◽  
Heterogeneous Surfaces ◽  
Roughness Parameters ◽  
Technical Practice ◽  
Hidden Layer

This work deals with investigative methods used for evaluation of the surface quality of selected metallic materials’ cutting plane that was created by CO2 and fiber laser machining. The surface quality expressed by Rz and Ra roughness parameters is examined depending on the sample material and the machining technology. The next part deals with the use of neural networks in the evaluation of measured data. In the last part, the measured data were statistically evaluated. Based on the conclusions of this analysis, the possibilities of using neural networks to determine the material of a given sample while knowing the roughness parameters were evaluated. The main goal of the presented paper is to demonstrate a solution capable of finding characteristic roughness values for heterogeneous surfaces. These surfaces are common in scientific as well as technical practice, and measuring their quality is challenging. This difficulty lies mainly in the fact that it is not possible to express their quality by a single statistical parameter. Thus, this paper's main aim is to demonstrate solutions using the cluster analysis methods and the hidden layer, solving the problem of discriminant and dividing the heterogeneous surface into individual zones that have characteristic parameters.

Considerations on Cutting Regime Influence of NC Laser Cutting Machine Tool on Processed Surface Quality

2015 ◽  
Vol 760 ◽  
pp. 475-481
Author(s):  
Cristina Biris ◽  
Octavian Bologa ◽  
Claudia Girjob ◽  
Sever Gabriel Racz
Keyword(s):  
Surface Quality ◽  
Experimental Research ◽  
Laser Cutting ◽  
Cutting Parameters ◽  
Metallic Materials ◽  
Response Functions ◽  
Roughness Parameters ◽  
Cutting Machine ◽  
Factors Of Influence

This paper presents the study of the influence of cutting parameters on surface quality in laser cutting of metallic materials. In this paper, it is shown which of the cutting parameters have the greatest influence on the quality of the processed surfaces by measuring various roughness parameters. After the experimental research was carried out a ranking of the factors of influence on the response functions was made, also graphs of dependency to various parameters of roughness were made.

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

2020 ◽  
Vol 234 (5) ◽  
pp. 732-751 ◽  
Author(s):  
R Hartl ◽  
B Praehofer ◽  
MF Zaeh
Keyword(s):  
Neural Networks ◽  
Friction Stir Welding ◽  
Surface Quality ◽  
Convolutional Neural Networks ◽  
Visual Inspection ◽  
Process Data ◽  
Friction Stir ◽  
Artificial Neural ◽  
Topography Analysis

Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

Research about Surface Quality of Polytetrafluoroethylene (Teflon) after Inner Turning

2015 ◽  
Vol 809-810 ◽  
pp. 213-219
Author(s):  
Cristina Ileana Pascu ◽  
Iulian Popescu ◽  
Alexandru Vintilescu
Keyword(s):  
Surface Quality ◽  
Cutting Process ◽  
Uniform Structure ◽  
Internal Diameter ◽  
Outer Diameter ◽  
Roughness Parameters ◽  
High Originality

For this study with high originality, a teflon bushing with outer diameter of 58 mm and 32 mm internal diameter has been turned. After inner turning with various cutting feeds, the part has been cut in half to measure roughness. The roughness parameters Ra, Rz and Rq have beeen measured with an electronic roughness tester Mitutoyo, Japan, SJ-201 P. The micro-asperity images for each of the 6 samples performed are presented in the paper. Diagrams with the variations of roughness parameters are also given in this study. The trend is the increasing of roughness values with feed increasing, but some abnormalities also appear in samples 3 and 4 for Ra and Rz. At sample 5 for Rz parameter and sample 6 for Rq parameter a decreasing of values occurs, although geometrically there should be with ascending tendencies. It follows that in teflon turning the cutting process is not uniform, although there are no marks on the blade. However, because this material is not metallic, it does not have a uniform structure, which influences the resulted roughness. This is the cause of the anomalies established by these experiments.

scholarly journals Problems of blackening of steel parts in technical practice

2018 ◽  
Vol 244 ◽  
pp. 01012 ◽  
Author(s):  
Sylvia Kuśmierczak ◽  
Martin Makovský
Keyword(s):  
Electron Microscopy ◽  
Surface Quality ◽  
Complex Analysis ◽  
Light And Electron Microscopy ◽  
Great Influence ◽  
Production Flow ◽  
Degradation Processes ◽  
Technical Practice ◽  
Surface Oxides

Problems of degradation are mainly solved in finished products, in a number of cases it is manifested during production. A great influence on the degradation processes of the products has the surface quality and surface finish. The article deals with the problem of blackening of steel, which has been treated with alkaline blackening. The throttle shaft made of automatic steel will be analyzed. Degradation was manifested by a change in surface quality after alkaline blackening. The methods of light and electron microscopy in combination with complex analysis of production flow and technology conditions were used for analysis. Based on the results of analyzes it was found that the degradation processes are closely related to the quality of preparation of the surface. Owing to the incorrect pretreatment of the surface, oxides occurred locally. At the sites of oxides, after alkaline blackening, the surface layer was peeled off, resulting in deterioration in the quality of the product.

New Ways at the Fine Grinding

2013 ◽  
Vol 581 ◽  
pp. 255-260 ◽  
Author(s):  
Martin Novák
Keyword(s):  
Surface Quality ◽  
Traditional Approach ◽  
Surface Profile ◽  
Ground Surface ◽  
Cutting Parameters ◽  
Grinding Wheel ◽  
Roughness Parameters ◽  
Machined Parts ◽  
Machining Operations

The traditional approach to grinding is to operate within the limits of surface quality. The requirements for surface quality in grinding are higher than those in other common machining operations such as turning and milling. The surface quality of machined parts is very important for precise production and assembly. When we focus on roughness parameters after grinding, we can establish the limits of these parameters for typical grain materials: Al2O3, SiC, CBN, SG and others. Increasing demands on accuracy and quality of production leads to research concerned with the properties of these materials and the surface quality after grinding. This paper shows new possibilities for the ground surface with focus on surface roughness obtained under varying combinations of cutting conditions. The influence of the grinding wheel, cutting parameters and coolant on higher surface quality is assessed by roughness parameters Ra, Rz, Rt and the Material portion of a surface profile. These high-precision ground surfaces are shown to have a Nanometres (10-9) unit topography demonstrating that the process is able to replace other finishing technologies such as superfinishing or honing.

scholarly journals ANN Architecture Specifications for Modelling of Open-Cell Aluminum under Compression

2020 ◽  
Vol 2020 ◽  
pp. 1-26 ◽  
Author(s):  
Marek Dudzik ◽  
Anna Małgorzata Stręk
Keyword(s):  
Neural Networks ◽  
Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Compression Tests ◽  
Stress Strain ◽  
Open Cell ◽  
External Data ◽  
Hidden Layer

The knowledge on strength properties of porous metals in compression is essential in tailored application design, as well as in elaboration of general material models. In this article, the authors propose specification details of the ANN architecture for adequate modelling of the phenomenon of compressive behaviour of open-cell aluminum. In the presented research, an algorithm was used to build different structures of artificial neural networks (ANNs), which approximated stress-strain relations of an aluminum sponge subjected to compression. Next, the quality of the built approximations was appraised. The mean absolute relative error (MARE), coefficient of determination between outputs and targets R2, root mean square error (RMSE), and mean square error (MSE) were assumed as criterial measures for the assessment of the fitting quality. The studied neural networks (NNs) were two-layer feedforward networks with different numbers of neurons in the hidden layer. A set of experimental stress-strain data from quasistatic uniaxial compression tests of open-cell aluminum of various apparent densities was used as data for training of neural networks. Analysis was performed in two modes: in the first one, all samples were taken for training, and in the second case, one sample was left out during training in order to play the role of external data for testing the trained network later. The taken out samples were maximum and minimum density samples (for extrapolation) and one random from within the density interval. The results showed that good approximation on the engineering level MARE<5% was reached for teaching networks with ≥7 neurons in the hidden layer for the first studied case and with ≥8 neurons for the second. Calculations on external data proved that 8 neurons are enough to actually obtain MARE<10%. Moreover, it was shown that the quality of approximation can be significantly improved to MARE≈7% (tested on external data) if the initial region of the stress-strain relation is modelled by an additional network.

The Effect of Treating Semiproducts of Polymeric Materials with Surfactants on the Quality of their Machining

2018 ◽  
Vol 45 (2) ◽  
pp. 75-77
Author(s):  
O. Yu. Erenkov
Keyword(s):  
Surface Quality ◽  
Polymeric Materials ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Preliminary Treatment ◽  
Roughness Parameters ◽  
Depth Of Penetration ◽  
Surface Active ◽  
Active Substances

The results of experimental investigations of surface quality after the lathe machining of polymeric materials subjected to preliminary treatment with surface-active substances are presented. It has been demonstrated in experiments that the preliminary treatment of semiproducts with surfactants ensures an improvement in quality of the surface machined by turning. This is indicated by a reduction in the roughness parameters by a factor of 2–4.5, and here the minimum roughness is achieved at a depth of cut equal to 1.0–1.15 of the depth of penetration of the surfactant into the material.

Comparison of Experimental Stamping Punch Machinability Made out of Unconventional Materials

2014 ◽  
Vol 635 ◽  
pp. 81-84
Author(s):  
Peter Ižol ◽  
Dagmar Draganovská ◽  
Juraj Hudák ◽  
Miroslav Tomáš ◽  
Jozef Beňo
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Experimental Work ◽  
Roughness Parameters ◽  
Scallop Height ◽  
Shaped Surface ◽  
Final Surface ◽  
Surface Roughness Parameters ◽  
Cam Software

The paper describes experimental stamping punch production from the view machinability and quality of stamping punch active surfaces. The tin car-body stamping punch has been chosen as a subject of experimental work. Two unconventional materials such as Textit J and Fibroflex 5 have been chosen for stamping punch. Milling strategies have been proposed, optimized and verified using CAM software SolidCAM considering the maximum Scallop Height. The final surface quality on selected areas of stamping punch shaped surface has been evaluated by surface roughness parameters Ra and Rz. These have been compared to Scallop Height set in CAM software. Manufactured areas on shaped surface were optically evaluated as well.

Measurement and Evaluation of Ferrite Materials Surface Quality

2013 ◽  
Vol 721 ◽  
pp. 130-134
Author(s):  
Wen Long Liu ◽  
Bin Lin ◽  
Li Peng Sun
Keyword(s):  
Surface Quality ◽  
Service Performance ◽  
Direct Influence ◽  
Qualitative And Quantitative Analysis ◽  
Roughness Parameters ◽  
Preliminary Conclusion ◽  
Qualitative And Quantitative ◽  
Ferrite Devices ◽  
Measurement And Evaluation

A measurement on the topography and 3D roughness parameters of the ferrite surface has been taken by using a 3D optical profiler. Based on the analysis of the experimental data and the contradistinction between the ferrite devices with different quality and service performance, one preliminary conclusion has been obtained that the surface quality of ferrite materials can be evaluated by using the 3D roughness parameters, and this method is applicable and stable. With qualitative and quantitative analysis, it could be predicted that the surface quality has a direct influence on the service performance of ferrite devices.

scholarly journals The Influence of the Material Type and the Placement in the Print Chamber on the Roughness of MJF-Printed 3D Objects

Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 49
Author(s):  
Damian Dzienniak
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Thermoplastic Polyurethane ◽  
Measuring System ◽  
Patient Specific ◽  
Roughness Parameters ◽  
Polyamide 11 ◽  
3D Objects ◽  
Material Type

This paper describes a surface-roughness study performed on samples manufactured additively using the Multi Jet Fusion (MJF) technology. The samples were divided into three groups based on the material used in the process: polypropylene (PP), thermoplastic polyurethane (TPU), and polyamide 11 (PA11). Subsequently, they were tested by means of a roughness-measuring system, which made it possible to determine the typical surface roughness parameters (Ra, Rq, Rz). The tests were designed to examine whether the placement and orientation of 3D objects while printing, in connection with the material used, can significantly influence the surface quality of MJF-printed objects. The results show that the TPU samples have a surface roughness much higher than the PP and PA11 ones, which exhibit roughness levels very similar to each other. It can also be concluded that surfaces printed vertically (along the Z-axis) tend to be less smooth—similarly to the surfaces of objects made of TPU located in the central zones of the print chamber during printing. This information may be of value in cases where low surface roughness is preferred (e.g., manufacturing patient-specific orthoses), although this particular study does not focus on one specific application.

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