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Abstract

An overview of the bibliography regarding the connection of knowledge about precious metal alloys and aspects of the use of computer aided technologies to the optimization of the jewelry casting processes is presented. An analysis of the usability of selected CAx systems was made: 1) for spatial design, called Rhinoceros 6 and 2) CAE system: NovaFlow & Solid (NF&S). The authors describe own research including data acquisition and evaluation of temperature variations during solidification of the selected Au-Ag-Cu alloy, with the identification of the phase transformations of this alloy. The intensity of heat exchange was changed (cooling of specimens under ambient temperature conditions – "normal" intensity and with the furnace – very slow cooling). The problem of completing the simulation database was pointed out and analyzed. Examples of simulations of casting selected jewelry (ring and signet) were given and compared with the result of the experiment realized in real conditions. It was confirmed that the optimization by combining experimental and simulation studies allows for the acquisition of new knowledge, and also facilitates the creation of new artistic designs of jewelry as well as performing the feasibility check, and then optimizing the chosen technology.

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Authors and Affiliations

Z. Ignaszak
J. Wojciechowski
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Abstract

Selective laser melting is one of the additive manufacturing technologies that is used to produce complex-shaped components for applications in the automotive industry. The purpose of the changes in the design, technology, and material tests was to make a steering gear housing using the SLM method. The steering gear housing was produced by the pressure casting method using an AlSi9Cu3(Fe) alloy. The construction of this housing is adapted to the specifics of left-hand traffic. The change in technology was related to the change of the position of the steering system from right-hand to left-hand and the demand for a limited number of gear housings. It was necessary to make a virtual model of the housing on the basis of the part that was removed from the vehicle. In SLM technology, the AlSi10Mg aluminum alloy was used as a raw material in the form of CL 32Al gas-atomized powder. After the SLM process was completed, the housings were subjected to heat treatment. The AlSi10Mg alloy fabricated by the SLM method after heat treatment is characterized by good plasticity and an average value of tensile strength. The last stage was to check the geometry of the SLM housing with a 3D scanner. As a result, a map of the dimensional deviations from the nominal values was obtained. This data was used to modify the CAD model before the next fabrication process.
The use of 3D printing technology allowed for the quick production of elements. The time to develop the technology and the production of the first two gear housings based on a 3D model was seven days.
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Bibliography

[1] Additive Manufacturing  General Principes  Terminology (2015). ISO/ASTM 2900:2015. BSI: London, UK.
[2] Frazier, W.E. (2014). Metal additive manufacturing: A review. Journal of Materials Engineering and Performance. 23, 1917-1928. DOI: 10.1007/s11665-014-0958-z.
[3] Sercombe, T.B. & Li, X. (2016). Selective laser melting of aluminum and aluminum metal matrix composites. Review. Materials Technology. 31(2), 77-85. DOI: 10.1179/1753555715Y.0000000078.
[4] Yadroitsev, I., Yadroitsava, I., Bertrand, P. & Smurov, I. (2012). Factor analysis of selective laser melting process parameters and geometrical characteristics of synthesized single tracks. Rapid Prototyping Journal. 18(3), 201-208. DOI: 10.1108/13552541211218117.
[5] Olakanmi, E.O. (2013). Selective laser sintering/melting (SLS/SLM) of pure Al, Al-Mg, and Al-Si powders: Effect of processing conditions and powder properties. Journal of Materials Processing Technology. 213(8), 1387-1405. DOI: 10.1016/j.jmatprotec.2013.03.009.
[6] Gibson, I., Rosen, D.W. & Stucker, B. (2010). Additive Manufacturing Technologies, Rapid Prototyping to Direct Digital Manufacturing. Springer New York Heidelberg Dordrecht London. DOI: 10.1007/978-1-4419-1120-9.
[7] Kempen, K., Thijs, L., Van Humbeeck, J. & Kruth, J.P. (2015). Processing AlSi10Mg by selective laser melting: parameter optimisation and material characterization. Materials Science and Technology. 31(8), 917-923, DOI: 10.1179/1743284714Y.0000000702.
[8] Aboulkhair, N.T., Everitt, N.M., Ashcroft, I. & Tuck, C.N. (2014). Reducing porosity in AlSi10Mg parts processed by selective laser melting. Additive Manufacturing. 1-4, 77-86. DOI: 10.1016/j.addma.2014.08.001.9.
[9] Read, N., Wang, W. & Essa, K. & Attallah, M. (2015). Selective laser melting of AlSi10Mg alloy: Process optimisation and mechanical properties development. Materials & Design. 65, 417-424. DOI: 10.1016/J.MATDES.2014.09.044.
[10] Lam, L.P., Zhang, D.Q., Liu, Z.H. & Chua, C.K. (2015). Phase analysis and microstructure characterisation of AlSi10Mg parts produced by Selective Laser Melting. Virtual and Physical Prototyping. 10 (4), 207-215. DOI: 10.1080/17452759.2015.1110868.
[11] EOS Material data sheet, EOS Aluminium AlSi10Mg. www.eos.info/03_system-related-assets/material-related-contents/metal-materials-and-examples/metal-material- datasheet/aluminium/alsi10mg-9011-0024-m400_flexline_material_data_sheet_03-18_en.pdf.
[12] Concept Laser a GE Additive Company, CL 32 Al. Aluminium alloy. www.ge.com/additive/sites/default/files/ 2018-12/CL 32AL_DS_DE_US_v1.pdf.
[13] Li, W., Li, S., Liu, J., Zhang, Y., Wei, Q., Yan, C. & Shi, Y. (2016). Effect of heat treatment on AlSi10Mg alloy fabricated by selective laser melting: Microstructure evolution, mechanical properties and fracture mechanism. Materials Science and Engineering A. 663, 116-125. DOI: 10.1016/j.msea.2016.03.088.
[14] Thijs, L., Kempen, K., Kurth, J.P. & Van Humbeeck, J. (2013). Fine-structured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder. Acta Materialia. 61(5), 1809-1819. DOI: 10.1016/j.actamat.2012.11.052.
[15] Brandl, E., Heckenberger, U., Holzinger, V. & Buchbinder, D. (2012). Additive manufactured AlSi10Mg samples using Selective Laser Melting (SLM): Microstructure, high cycle fatigue, and fracture behavior. Materials & Design. 34, 159-169. DOI: 10.1016/j.matdes.2011.07.067.
[16] Piekło, J., Garbacz-Klempka, A., Żuczek, R. & Małysza, M. (2019). Computational modeling of fracture toughness of Al-Si, and Al-Zn-Mg-Cu alloys with detected porosity. Journal of Materials Engineering and Performance. 28, 1373-1381. DOI: 10.1007/s11665-019-03899-2.
[17] Zych, J., Piekło, J., Maj, M., Garbacz-Klempka, A. & Piękoś, M. (2019). Influence of structural discontinuities on fatigue life of 4XXX0-series aluminum alloys. Archives of Metallurgy and Materials. 64(2), 765-771. DOI: 10.24425/amm.2019.127611.
[18] Leary, M., Maconachie, T., Sarker, A. & Faruque, O. (2019). Mechanical and thermal characterisation of AlSi10Mg SLM block suport structures. Materials and Design. 183(5), 108-138. DOI: 10.1016/j.matdes.2019.108138.
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Authors and Affiliations

J. Piekło
1
ORCID: ORCID
A. Garbacz-Klempka
1
ORCID: ORCID

  1. AGH University of Science and Technology, Faculty of Foundry Engineering, Reymonta 23 Str., 30-059 Kraków, Poland
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Abstract

A Cu-1Cr-0.1Zr alloy has been subjected to ECAP processing via route Bc and aging at 250-800°C. Electron BackScatter diffraction (EBSD), Transmission Electron Microscopy (TEM) and X-Ray Diffraction Line Profile Analysis (XRDLPA) techniques have been used to unveil some peculiarities of the grain and subgrain structure with a special emphasis on the comparison of the grain size estimated by the three techniques. For the alloy ECAP processed and aged up to 16 passes, the grain size (from EBSD, 0.2 < d < 5 μm), subgrain size (from TEM, d ~ 0.75 μm) and “apparent” average crystallite size (from XRDLPA, d < 0.25 μm) are manifestly different. The results were compared to the published data and analyzed based on the fundamental aspects of these techniques.
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Authors and Affiliations

K. Abib
1
ORCID: ORCID
B. Alili
1
ORCID: ORCID
T. Baudin
2
ORCID: ORCID
A.-L. Helbert
2
ORCID: ORCID
F. Brisset
2
ORCID: ORCID
L. Litynska-Dobrzynska
3
ORCID: ORCID
P. Zieba
3
ORCID: ORCID
D. Bradai
1
ORCID: ORCID

  1. University Of Sciences And Technology Houari Boumediene, Faculty Of Physics, Bp 32 El Alia, Bab Ezzouar, Algiers, Algeria
  2. University Paris-Saclay, Icmmo, 91405, Orsay, France
  3. Institute Of Meta llurgy And Mat erials Science, Polish Academy Of Sciences, 25 Reymonta Str., 30-059 Krakow, Poland
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Abstract

In1993–1997 the occurrence of predatory syrphids inthe cabbage aphid (Brevicoryne brassicae L.) colonies was observed on the nine different cabbage vegetables: savoy cabbage cv. Vertus, white cabbage cv. Amager, red cabbage cv. Langendijker, brussels sprout cv. Maczuga, cauliflower cv. Pionier, blue kohlrabi cv. Masłowa, white kohlrabi cv. Delikates, kale cv. Zielony Kędzierzawy and broccoli cv. Piast. The number of aphids as well as amount of aphidophagous Syrphidae feeding in their colonies on the different cabbage vegetables were compared. The species compositionwas also estimated. Eight species of Syrphidae were collected: Episyrphus balteatus (Deg.), Sphaerophoria scripta (L.), S. rueppelli (Wied.) S. menthastri (L.), Metasyrphus corollae (Fabr.), Scaeva pyrastri (L.), Syrphus vitripennis (Meig.), Paragus quadrifasciatus (Meig.). In the all years of observation the dominant species were E. balteatus (Deg.), S. scripta (L.) and S. rueppelli (Wied.). The highest number of syrphid larvae were collected from cabbage aphid colonies on the white and savoy cabbage.

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Authors and Affiliations

Beata Jankowska
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Abstract

Evolutionary computing and algorithms are well known tools of optimisation that are utilized for various areas of analogue electronic circuits design and diagnosis. This paper presents the possibility of using two evolutionary algorithms - genetic algorithm and evolutionary strategies - for the purpose of analogue circuits yield and cost optimisation. Terms: technologic and parametric yield are defined. Procedures of parametric yield optimisation, such as a design centring, a design tolerancing, a design centring with tolerancing, are introduced. Basics of genetic algorithm and evolutionary strategies are presented, differences between these two algorithms are highlighted, certain aspects of implementation are discussed. Effectiveness of both algorithms in parametric yield optimisation has been tested on several examples and results have been presented. A share of evolutionary algorithms computation cost in a total optimisation cost is analyzed.

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Authors and Affiliations

P. Jantos
J. Rutkowski

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