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Abstract

Work was done as a part of the project " New generation haulage system of highly productive longwall systems" aiming to develop and implement a new longwall shearer system called KOMTRACK. The widely used EICOTRACK feed system developed forty years ago is not adapted to modern longwall shearers' power. Within the project, an innovative, flexible feed system with a modular structure was created with the possibility of continuous adjustment to the carbon wall's unevenness. Newly-developed three cast steels variants have been initially selected to fabricate this system's elements. The material's final selection was realized based on the tensile tests, Charpy impact tests, Brinell hardness surveys, and wear resistance measurements. Results analysis allowed to select cast steel marked as "2", which fulfilled all requirements and was used in further casting trials.
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Bibliography

[1] Pirowski Z. (2020). A new generation feed system for high-performance longwall shearers. Stage 4; Kraków: Report 2019 – Łukasiewicz Research Network – Foundry Research Institute, 64-69.
[2] Pieczora, E., Zachura, A., Pirowski, Z., Pysz, S., Kurdziel, P., Żyła, P., Kotulski, W. (2015). Flextrack - innovative longwall shearer feed system. Part 1, Modern methods of coal and hard rock mining. Kraków: AGH University of Science and Technology. 185-194. ISBN 978-83-930353-5-9.
[3] Zachura, A., Pieczora, E., Pysz, S., Żuczek, R., Pirowski, Z., Kurdziel, P., Żyła, P., Kotulski, W. (2015). Flextrack - innovative longwall shearer feed system. Part 1, Modern methods of coal and hard rock mining. Kraków: AGH University of Science and Technology, 195-203. ISBN 978-83-930353-5-9.
[4] Pirowski, Z., Uhl, W., Jaśkowiec, K., Pysz, S., Gazda, A., Kotulski, W., Kurdziel, P., Zachura, A. (2015). Innovative FLEXTRACK feed system - selection of materials (casting al-loys), in: A. Klich, A. Kozieł: Innovative techniques and technologies for mining. Safety - Efficiency - Reliability - KOMTECH 2015, KOMAG Institute of Mining Technology, 223-236. ISBN 978-83-60708-90-3.
[5] Pysz, P., Żuczek, R., Pirowski, Z., Uhl, W., Kotulski, W., Żyła, P., Kurdziel, P., Zachura, A. (2015). Innovative FLEXTRACK feed system - development of the technology of making cast segments of the toothed elements and the guider, in: A. Klich, A. Kozieł: Innovative techniques and technologies for mining. Safety - Efficiency - Reliability - KOMTECH 2015, KOMAG Institute of Mining Technology, 237-249. ISBN 978-83-60708-90-3.
[6] Pirowski, Z., Uhl, W., Jaśkowiec, K., Krzak, I., Wójcicki, M., Gil, A., Kotulski, W., Kurdziel, P., Pieczora, E., Zachura, A. (2015). Innovative FLEXTRACK feed system - quality assessment of the manufactured prototype castings, in: A. Klich, A. Kozieł: Innovative techniques and technologies for mining. Safety - Efficiency - Reliability - KOM-TECH 2015, KOMAG Institute of Mining Technology, 250. ISBN 978-83-60708-90-3.
[7] Kalita, M. (2019). Designing process of a toothed segment of the KOMTRACK haulage system. New Trends in Production Engineering. 2(1), 121-129. https://doi.org/10.2478/ntpe-2019-0013.
[8] Nieśpiałowski, K., Kalita, M., Rawicki, N, (2019). System for tensioning the toothed path of the longwall shearer's feed system, Scientific and Technical Conference: KOMTECH Innovative Mining Technologies – IMTech. [9] Pirowski, Z. (2015). Thermal Analysis in the Technological “Step” Test of H282 Nickel Alloy. Archives of Foundry Engineering. 15(1), 87-92. DOI: 10.1515/afe-2015-0016.
[10] Pirowski, Z., Warmuzek, M., Radzikowska, J. (2012). Test casting Inconel 740 alloy, 70th World Foundry Congress, 560-565.
[11] Rakoczy, Ł., Grudzień, M., Cygan, R. & Zielińska-Lipiec, A. (2018). Effect of cobalt aluminate content and pouring temperature on macrostructure, tensile strengh and creep rupture of Inconel 713C castings. Archives of Metallurgy and Meterials. 63(3), 1537-1545. https://doi.org/10.24425/123845.
[12] Pirowski, Z., Jaśkowiec, K., Tchórz, A., Krzak, I., Sobczak, J., Purgert, R. (2016). Technological conversion applicable for manufacturing elements from Nickel superalloy H282, 72nd World Foundry Congress, 223-224.
[13] Grudzień-Rakoczy, M., Rakoczy, Ł., Cygan, R., Kromka, F., Pirowski, Z. & Milkovic, O. (2020). Fabrication and characterization of the newly developed superalloys based on Inconel 740. Materials. 13, 2362. https://dx.doi.org/10.3390%2Fma13102362.
[14] Rakoczy, Ł., Grudzień-Rakoczy, M. & Cygan, R. (2019). The influence of shell mold composition on the as-cast macro-and micro-structure of thin-walled IN713C superalloy castings. Journal of Materials Engineering and Performance. 28(7), 3974-3985. https://doi.org/10.1007/s11665-019-04098-9.
[15] Grudzień, M., Cygan, R., Pirowski, Z. & Rakoczy, Ł. (2018). Transactions of the Foundry Research Institute. 58, 39-45. https://dx.doi.org/10.7356/iod.2018.04.
[16] Pirowski, Z. & Gościański, (2013). Casting Alloys for Agricultural Tools Operating under the Harsh Conditions of Abrasive Wear. TEKA Commission of Motorization and Energetics in Agriculture. 13(1), 119-126 ISSN 1641-773.
[17] Pirowski, Z., Gwiżdż, A. & Kranc, M. (2012). Cast Agricultural Tools Operating in Soil. Tekhnika ta energetika APK. 170(1), 378-385. ISSN 2222-8618.
[18] Gościański, M. & Pirowski, Z. (2009). Construction and Technology of Production of Casted Shares for Rotating and Field Ploughs, TEKA Commission of Motorization and Energetics in Agriculture. - O.L. PAN, 9(9), 231-239. ISSN 1641-7739.
[19] Pirowski, Z., Olszyński, J., Turzyński, J. & Gościański, M. (2006). Elements of agricultural ma-chinery working in soil made of modern casting materials. Motrol. 8, 169-180. (in Polish).
[20] Pirowski, Z. (2014). Evaluation of High-temperature Physico-chemical Interactions Between the H282Alloy Melt and Ceramic Material of the Crucible. Archive of Foundry Engineering. 14(4), 83-90. https://doi.org/10.2478/afe-2014-0091.
[21] Wang, Z., Huang, B., Chen, H., Wang, CH. & Zhao, X. (2020). The Effect of Quenching and Partitioning Heat Treatmenton the Wear Resistance of Ductile Cast Iron Journal of Materials Engineering and Performance. 29, 4370-4378. https://doi.org/10.1007/s11665-020-04871-1.
[22] Srinivasu, R., Sambasiva Rao A., Madhusudhan Reddy G., K. Srinivasa Rao, K. (2015). Friction stir surfacing of cast A356 aluminiumesilicon alloy with boron carbide and molybdenum disulphide powders. Defence Technology. 11(2), 140-146.
[23] Heldin, M., Heinrichs, J., Jacobson, S. (2021). On the critical roles of initial plastic deformation and material transfer on the sliding friction between metals. Wear. 477(Spec.203853). DOI: 10.1016/j.wear.2021.203853, Published JUL 18.
[24] Grzesik, W., Zalisz, Z., Krol, S. & Nieslony, P. (2006). Investigations on friction and wear mechanisms of the PVD-TiAlN coated carbide in dry sliding against steels and cast iron. Wear. 261(11-12), 1191-1200.
[25] Holmberg, K., Matthews, A., Dowson, D. (Ed.) (1998). Coating Tribology. Properties, Techniques and Applications in Surface Engineering. Tribology Series. 28, Elsevier, Amsterdam.
[26] PN-88/H-83160; Wear-resistant cast steel - Grades. (in Polish). [27] NF A 32-058/1984: Produits de founderie aciers et fontes blanches moules resistant a l'usure par abrasion.

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Authors and Affiliations

D. Wilk-Kołodziejczyk
1 2
ORCID: ORCID
Z. Pirowski
2
ORCID: ORCID
M. Grudzień-Rakoczy
2
ORCID: ORCID
A. Bitka
2
ORCID: ORCID
K. Chrzan
2
ORCID: ORCID

  1. AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland
  2. Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland
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Abstract

The article was created as a result of the work TECHMATSTRATEG 1 program “Modern Material Technologies” as part of the project with the acronym INNOBIOLAS entitled “Development of innovative working elements of machines in the forestry sector and biomass processing based on high-energy surface modification technologies of the surface layer of cast elements”; agreement No. TECHMATSTRATEG1/348072/2/NCBR/2017.
The article discusses the procedure for selecting casting materials that can meet the high operational requirements of working tools of mulching machines: transfer of high static and dynamic loads, resistance to tribological wear, corrosion resistance in various environments. The mulching process was briefly described, then the alloys were selected for experimental tests, model alloys were made and perform material tests were carried out in terms of functional and technological properties. The obtained results allowed to select the alloy where the test castings were made.
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Authors and Affiliations

Z. Pirowski
1
ORCID: ORCID
A. Bitka
1
ORCID: ORCID
M. Grudzień-Rakoczy
1
ORCID: ORCID
M. Małysza
1
ORCID: ORCID
S. Pysz
1
ORCID: ORCID
P. Wieliczko
1
ORCID: ORCID
D. Wilk-Kołodziejczyk
1 2
ORCID: ORCID

  1. Center of Casting Technology, Łukasiewicz Research Network – Krakow Institute of Technology Contribution, Poland
  2. AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Al. Mickiewicza 30. 30-059 Kraków, Poland
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Abstract

A classical algorithm Tabu Search was compared with Q Learning (named learning) with regards to the scheduling problems in the Austempered Ductile Iron (ADI) manufacturing process. The first part comprised of a review of the literature concerning scheduling problems, machine learning and the ADI manufacturing process. Based on this, a simplified scheme of ADI production line was created, which a scheduling problem was described for. Moreover, a classic and training algorithm that is best suited to solve this scheduling problem was selected. In the second part, was made an implementation of chosen algorithms in Python programming language and the results were discussed. The most optimal algorithm to solve this problem was identified. In the end, all tests and their results for this project were presented.
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Bibliography

[1] Yang, L., Jiang, G., Chen, X., Li, G., Li, T. & Chen, X. (2019). Design of integrated steel production scheduling knowledge network system. Claster Comput. 10197-10206.
[2] Żurada, J. Barski, M., Jędruch, W. (1996). Artificial Neural Networks. Fundamentals of theory and application. Warszawa: PWN. (in Polish).
[3] Janiak, A. (2006). Scheduling in computer and manufacturing systems. Warszawa: Wydawnictwa Komunikacji i Łączności.
[4] Smutnicki, C. (2002). Scheduling algorithms. Warszawa: Akademicka Oficyna Wydawnicza EXIT. (in Polish).
[5] Coffman, E.G. (1980). Task scheduling theory. Warszawa: Wydawnictwa Naukowo-Techniczne. (in Polish).
[6] Janczarek, M. (2011). Managing production processes in the enterprise. Lublin: Lubelskie Towarzystwo Naukowe. (in Polish).
[7] Szeliga, M. (2019) Practical machine learning. Warszawa: PWN. (in Polish).
[8] Raschka, S. (2018) Python machine learning. Gliwice: Helion. (in Polish).
[9] Choi, H-S, Kim, J-S. & Lee, D-H. (2011). Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line. Expert System with Application. 38, 3514-3521.
[10] Agarwal, A., Pirkul, H. & Jacob, V.S. (2003). Augmented neutral network for task scheduling. European Journal of Operational Research. 151, 481-502.
[11] Jain, A.S. & Meeran, S. (1998). Jop-shop scheduling using neutral networks. International Journal of Production Research. 36(5), 1249-1272
[12] Fonseca-Reyna, Y.C., Martinez-Jimenez, Y. & Nowe, A. (2017). Q-Learning algorithm performance for m-machine, n-jobs flow shop scheduling problems to minimize makespan, Revista Investigacion Operacional. 38(3), 281-290.
[13] Dewi, Andriansyah, & Syahriza, (2019). Optimization of flow shop scheduling problem using classic algorithm: case study, IOP Conf. Series: Materials Science and Engineering 506.
[14] Putatunda, K. (2001) Development of austempered ductile cast iron (ADI) with simultaneous high yield strength and fracture toughness by a novel two-step austempering process. Material Science and Engineering A. 315, 70-80.
[15] Dayong Han, Hubei Key, Qiuhua Tang; Zikai Zhang; Jun Cao, (2020). Energy-efficient integration optimization of production scheduling and ladle dispatching in steelmaking plants. IEEE Access. 8, 176170-176187.
[16] Perzyk, M. (2017). The use of production data mining methods in the diagnosis of the causes of product defects and disruptions in the production process. Utrzymanie Ruchu. 4, 45-47. (in Polish).
[17] Perzyk, M., Dybowski, B. & Kozłowski, J. (2019). Introducing advanced data analytics in perspective of industry 4.0 in a die casting foundry. Archives of Foundry Engineering. 19(1), 53-57.
[18] Yescas, M. (2003). Prediction of the Vickers hardness in austempered ductile irons using neural networks. International Journal of Cast Metals Research. 15(5), 513-521.
[19] Report on the contract no. U / 227/2014 implemented at the Foundry Research Institute. (in Polish).
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Authors and Affiliations

D. Wilk-Kołodziejczyk
1 2
ORCID: ORCID
K. Chrzan
2
ORCID: ORCID
K. Jaśkowiec
2
ORCID: ORCID
Z. Pirowski
2
ORCID: ORCID
R. Żuczek
2
ORCID: ORCID
A. Bitka
2
ORCID: ORCID
D. Machulec
3
ORCID: ORCID

  1. AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland
  2. Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland
  3. AGH University of Science and Technology, Kraków, Poland
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Abstract

The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.
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Authors and Affiliations

D. Wilk-Kołodziejczyk
1 2
ORCID: ORCID
K. Jaśkowiec
2
ORCID: ORCID
A. Bitka
2
ORCID: ORCID
Z. Pirowski
2
ORCID: ORCID
M. Grudzień-Rakoczy
2
ORCID: ORCID
K. Chrzan
2
ORCID: ORCID
M. Małysza
2
ORCID: ORCID
M. Doroszewski
1
ORCID: ORCID

  1. AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Al. Mickiewicza 30, 30-059 Kraków, Poland
  2. Centre of Casting Technology, The Łukasiewicz Research Network – Cracow Technology Institute, Poland

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