Mit explorativer Datenanalyse und Data Mining zu hoch effizienten polymeren Tribo-Schicht-Systemen

2021 ◽  
Author(s):  
Anna Buling ◽  
Laura Dongmo Guetse ◽  
Jörg Zerrer
Keyword(s):  
Data Mining ◽  
Energy Efficiency ◽  
Process Parameters ◽  
Film Formation ◽  
Machine Building ◽  
Polymeric Coating ◽  
Coating Process ◽  
Coating System ◽  
Wear Protection ◽  
Lightweight Metals

Since current developments in machine building and automotive industry are dealing with the amplification of energy efficiency and sustainability of components, the reduction of friction and wear losses plays the most important key role. A further aspect of energy saving by mass reduction can be taken into account by substituting steel by lightweight metals. To fulfill these requirements, this study focuses on the development of a tribo-coating system, based on PEEK (poly-ether-ether-ketone) as a base coating material for Al substrates. The coating is applied by using laser radiation to increase the energy efficiency of the coating process on the one hand and to reduce thermal stress on the component on the other hand. Furthermore, the laser process improves the mechanical prosperties of the polymeric coating. In the first step the correlation between the coating process parameters and the resulting coating morphology accompanied by its mechanical properties and the tribological behavior was elucidated by using explorative data analysis. Here, the influence of different wear and/or friction reducing additives and their variable concentrations was also taken into account, while the tribological response of the resulting coating systems was examined and valuated under dry sliding conditions. Using data mining, the most dominant correlations between the process parameters and the tribological answer of the coating system could be found. Utilizing these findings, the process parameters for different additives in the PEEK dispersions could be optimized, and a multilayer system was established, which combines high corrosion and wear protection accompanied by a tribo-film formation resulting in low friction and an increased lifetime of the coating system.

scholarly journals Formulation of Sustainable Water-Based Cutting Fluids with Polyol Esters for Machining Titanium Alloys

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 773
Author(s):  
Elisabet Benedicto ◽  
Eva María Rubio ◽  
Laurent Aubouy ◽  
María Ana Sáenz-Nuño
Keyword(s):  
Molecular Structure ◽  
Tool Wear ◽  
Titanium Alloys ◽  
Film Formation ◽  
Cutting Fluid ◽  
Cutting Fluids ◽  
Lubricating Film ◽  
Wear Protection ◽  
Oil In Water ◽  
Polyol Esters

The machinability of titanium alloys still represents a demanding challenge and the development of new clean technologies to lubricate and cool is greatly needed. As a sustainable alternative to mineral oil, esters have shown excellent performance during machining. Herein, the aim of this work is to investigate the influence of esters’ molecular structure in oil-in-water emulsions and their interaction with the surface to form a lubricating film, thus improving the efficiency of the cutting fluid. The lubricity performance and tool wear protection are studied through film formation analysis and the tapping process on Ti6Al4V. The results show that the lubricity performance is improved by increasing the formation of the organic film on the metal surface, which depends on the ester’s molecular structure and its ability to adsorb on the surface against other surface-active compounds. Among the cutting fluids, noteworthy results are obtained using trimethylolpropane trioleate, which increases the lubricating film formation (containing 62% ester), thus improving the lubricity by up to 12% and reducing the torque increase due to tool wear by 26.8%. This work could be very useful for fields where often use difficult-to-machine materials—such as Ti6Al4V or γ-TiAl – which require large amounts of cutting fluids, since the formulation developed will allow the processes to be more efficient and sustainable.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

scholarly journals Machinability and Energy Efficiency in Micro-EDM Milling of Zirconium Boride Reinforced with Silicon Carbide Fibers

Materials ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 3920 ◽  
Author(s):  
Mariangela Quarto ◽  
Giuliano Bissacco ◽  
Gianluca D’Urso
Keyword(s):  
Energy Efficiency ◽  
Process Parameters ◽  
Advanced Materials ◽  
Cavity Surface ◽  
Main Process ◽  
Zirconium Boride ◽  
Electrode Wear ◽  
Micro Edm ◽  
Process Performance ◽  
Sic Fibers

Several types of advanced materials have been developed to be applied in many industrial application fields to satisfy the high performance required. Despite this, research and development of process suited to machine are still limited. Due to the high mechanical properties, advanced materials are often considered as difficult to cut. For this reason, EDM (Electrical Discharge Machining) can be defined as a good option for the machining of micro components made of difficult to cut electrically conductive materials. This paper presents an investigation on the applicability of the EDM process to machine ZrB2 reinforced by SiC fibers, with assessment of process performance and energy efficiency. Different fractions of the additive SiC fibers were taken into account to evaluate the stability and repeatability of the process. Circular pocket features were machined by using a micro-EDM machine and the results from different process parameters combinations were analyzed with respect to material removal, electrode wear and cavity surface quality. Discharges data were collected and characterized to define the actual values of process parameters (peak current, pulse duration and energy per discharge). The characteristics of the pulses were used to evaluate the machinability and to investigate the energy efficiency of the process. The main process performance indicators were calculated as a function of the number of occurred discharges and the energy of a single discharge. The results show interesting aspects related to the process from both the performances and the removal mechanism point of view.

Improving energy efficiency of buildings using data mining technologies

2014 ◽  
Author(s):  
Gerhard Zucker ◽  
Jasmine Malinao ◽  
Usman Habib ◽  
Thomas Leber ◽  
Anita Preisler ◽  
...  
Keyword(s):  
Data Mining ◽  
Energy Efficiency ◽  
Using Data

Experimental Investigation of Microfluidic Feature Manufacturing by Digital Light Processing Stereolithography

2020 ◽  
Author(s):  
Lara Rebaioli ◽  
Irene Fassi
Keyword(s):  
Process Parameters ◽  
Low Cost ◽  
Dimensional Accuracy ◽  
Machine Building ◽  
Single Step ◽  
Main Process ◽  
Single Chip ◽  
Digital Light Processing ◽  
And Performance ◽  
User Friendly

Abstract Lab on Chips (LOCs) are devices, mostly based on microfluidics, that allow to perform one or several chemical, biochemical or biological analysis in a miniaturized format on a single chip. The Additive Manufacturing processes, and in particular the Digital Light Processing stereolithography (DLP-SLA), could quickly produce a complete LOC with high resolution 3D features in a single step, i.e. without the need for assembly processes, and using low cost and user-friendly desktop machines. However, the potential of DLP-SLA to produce non-planar channels or channels with complex sections has not been fully investigated yet. This study proposes a benchmark artifact (including also some channels with their axis lying in a plane parallel to the machine building platform) aiming at assessing the capability and performance of DLP-SLA for manufacturing microfeatures for microfluidic devices. A proper experimental campaign was performed to evaluate the effect of the main process parameters (namely, layer thickness and exposure time) on the process performance. The results pointed out that both the process parameters influence the quality and dimensional accuracy of the analyzed features.

Data Mining for Estimating Fatigue Strength Based on Composition and Process Parameters

2019 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake
Keyword(s):  
Data Mining ◽  
Fatigue Strength ◽  
Process Parameters ◽  
Fatigue Testing ◽  
Absolute Error ◽  
Processing Parameters ◽  
Coefficient Of Determination ◽  
Low Alloy Steels ◽  
Strength Based ◽  
Alloy Steels

Abstract Accurately estimating the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and often fatal consequences of fatigue failures. The main objective of this manuscript is to perform data mining on the fatigue dataset for steel available from the National Institute of Material Science (NIMS) MatNavi. The cross-industry process for data mining (CRISP-DM) approach was followed in the paper, in order to gain meaningful insights from the dataset and to estimate the fatigue strength of carbon and low alloy steels, using composition and processing parameters. Of the six steps of the CRISP-DM approach, special emphasis has been placed on steps 2 to 5 (i.e. data understanding, data preparation, modeling and evaluation). In step 4 (i.e. modeling), a range of machine learning (parametric and non-parametric) is explored to predict the fatigue strength, based on the composition and process parameters. Various algorithms were trained and tested on the dataset and finally evaluated, using metrics such as root mean square error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2) and Explained Variance Score (EVS).

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