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Abstract

In this study, a preliminary evaluation was made of the applicability ofthe signalsof the cutting forces, vibration and acoustic emission in

diagnosis of the hardness and microstructure of ausferritic ductile iron and tool edge wear rate during its machining. Tests were performed

on pearlitic-ferritic ductile iron and on three types of ausferritic ductile iron obtained by austempering at 400, 370 and 320⁰C for 180

minutes. Signals of the cutting forces (F), vibration (V) and acoustic emission (AE) were registered while milling each type of the cast iron

with a milling cutter at different degrees of wear. Based on individual signals from all the sensors, numerous measures were determined

such as e.g. the average or maximum signal value. It was found that different measures from all the sensors tested depended on the

microstructure and hardness of the examined material, and on the tool condition. Knowing hardness of the material and the cutting tool

edge condition, it is possible to determine the structure of the material .Simultaneous diagnosis of microstructure, hardness, and the tool

condition is probably feasible, but it would require the application of a diagnostic strategy based on the integration of numerous measures,

e.g. using neural networks.

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Authors and Affiliations

D. Myszka
S. Bombiński
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Abstract

The combination of the austempered ductile iron mechanical properties strongly depend on the parameters used on the austempering cycle. On this study, the influence of austempering time and austenitizing temperature on the properties of a ductile iron were evaluated. A metallic bath of Zamak at 380°C was used as an austempering mean. A set of ductile iron blocks were austenitized at 900°C for 90 minutes and submitted to different austempering times in order to determine the best combination of microstructural and mechanical properties. After the definition of the time of austempering, the reduction of the austenitizing temperature was evaluated. The best combination of properties was obtained with austenitizing at 860°C and austempering during 60 minutes.

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Authors and Affiliations

L. Pereira
M.R. Bellé
L.F. Seibel Júnior
W.M. Pasini
R.F. Do Amaral
V. Karlinski de Barcellos
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Abstract

The paper presents recent developments concerning the formation of surface layer in austempered ductile iron castings. It was found that the traditional methods used to change the properties of the surface layer, i.e. the effect of protective atmosphere during austenitising or shot peening, are not fully satisfactory to meet the demands of commercial applications. Therefore, new ways to shape the surface layer and the surface properties of austempered ductile iron castings are searched for, to mention only detonation spraying, carbonitriding, CVD methods, etc.

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Authors and Affiliations

D. Myszka
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Abstract

The results presented in this paper are a continuation of the previously published studies. The results of hest treatment of ductile iron with

content 3,66%Si and 3,80% Si were produced. The experimental castings were subjected to austempering process for time 30, 60 and 90

minutes at temperature 300o

C. The mechanical properties of heat treated specimens were studied using tensile testing and hardness

measurement, while microstructures were evaluated with conventional metallographic observations. It was again stated that austempering

of high silicone ferritic matrix ductile iron allowed producing ADI-type cast iron with mechanical properties comparable with standard

ADI.

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Authors and Affiliations

A. Krzyńska
A. Kochański
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Abstract

Ductile iron casts with a higher silicone content were produced. The austempering process of high silicone ductile iron involving different

austempering times was studied and the results presented. The results of metallographical observations and tensile strength tests were

offered. The obtained results point to the fact that the silicone content which is considered as acceptable in the literature may in fact be

exceeded. The issue is viewed as requiring further research.

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Authors and Affiliations

A. Kochański
A. Krzyńska
T. Radziszewski
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Abstract

The influence of the hold time of the austempering heat treatment at 280°C on the microstructure and corrosion resistance in NaCl-based media of austempered ductile iron was investigated using X-ray diffraction, micro-hardness measurements, corrosion tests and surface observations. Martensite was only found in the sample which was heat treated for a short period (10 minutes). Corrosion tests revealed that this phase does not play any role in the anodic processes. Numerous small pits were observed in the α-phase which is the precursor sites in all samples (whatever the value of the hold time of the austempering heat treatment).

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Authors and Affiliations

H. Krawiec
V. Vignal
J. Lelito
A. Krystianiak
E. Tyrała
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Abstract

The research article address, the mechanical properties such as fatigue, impact strength and tribological properties of Austempered ductile iron (ADI) has been investigated. The samples of ADI iron were austenitized at 927°C for 2 hrs and later it was under austempering process for 2 hrs at a temperature range of 240°C to 400°C. Experiments under axial loading has been carried out on three different compositions (without Ni(X), 0.22 wt % Ni (X1), 0.34 wt. % Ni (X2). Fabricated test bars were converted in to as per ASTM standard samples for different tests. In order to study the influence of chunky nickel morphology studies on fatigue life and impact strength were carried out on a second set of specimens without any microstructural defect. Metallurgical analyses were performed on all the samples of heat treated samples (AF – Ausferrite, MB – Mixed bainite, M – Martensite, RA – Retained Austenite and N-Nodule) were found and compared. It was found that a mean content of 22% of chunky nickel in the microstructure (with respect to total Ni content) influence considerably the fatigue and impact strength properties of the cast iron. Moreover tribological properties of the specimens were also studied under dry sliding conditions at various sliding speed and load. The wear resistance and coefficient of friction were found to increase with increase in load and sliding speed.

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Authors and Affiliations

T. Ramkumar
S. Madhusudhanan
I. Rajendran
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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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