Slovenian Tool and Die Development Center

Celje, Slovenia

Slovenian Tool and Die Development Center

Celje, Slovenia

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Svecko R.,University of Maribor | Hancic A.,Slovenian Tool and Die Development Center | Kusic D.,University of Ljubljana | Grum J.,University of Maribor
12th International Conference of the Slovenian Society for Non-Destructive Testing: Application of Contemporary Non-Destructive Testing in Engineering, ICNDT 2013 - Conference Proceedings | Year: 2013

Clustering of numerical measurement data into clusters offers a great potential to find similar objects, which are normally neglected when observing a production process. In this paper, the clustering results obtained with the use of basic fuzzy c-means algorithm on acoustic emission (AE) signal amplitudes are presented. AE signal amplitudes were captured during total of six production cycles of standard polypropylene test specimens under two processing conditions. The final results revealed that the objective function minimization during iterations is very dependent on the number of selected clusters. In case of higher number of clusters the fuzzy cmeans algorithm clearly needs more iteration runs to cluster the input dataset.


Kek T.,University of Ljubljana | Kusic D.,Slovenian Tool and Die Development Center | Finc M.,Bohinjska Bistrica | Grum J.,University of Ljubljana
Research in Nondestructive Evaluation | Year: 2016

ABSTRACT: Ten adult readers, advanced in their control of two languages, Korean and English, were recruited for a study of academic literacy practices to examine the various linguistic repertoires on which they drew. Analysis of their language use revealed many instances of translanguaging, that is, a flexible reliance on two languages to serve one’s immediate needs. All participants engaged in various cognitive reading strategies associated with translanguaging. These translanguaging practices in academic literacy were interpreted through the lens of Hornberger’s (2013) bilingual continua model, providing a perspective on the complex interplay of bilingual content, context, media, and development. © 2016 American Society for Nondestructive Testing.


Hancic A.,Slovenian Tool and Die Development Center | Kosel F.,University of Ljubljana | Kuzman K.,University of Ljubljana | Slabe J.M.,Slovenian Tool and Die Development Center
Composites Part B: Engineering | Year: 2012

The mechanical response of wood- and cellulose-filled polymers and its comparison to analytical models is studied in this article. To model the elasto-plastic response of the wood-plastic composite (WPC), two explicit semi-analytical micromechanical methods were used: Mori-Tanaka Method (MTM) and Generalised Method of Cells (GMC). For experimental purpose, several test specimens composed of matrix polypropylene (PP) or polystyrene (PS) and filled with wood or cellulose short fibres of different length to width aspect ratio and various volume fractions were injection moulded. Tensile testing was then used to gain experimental data, which were then compared to the calculated prediction of proposed micromechanical models to test their applicability. The comparison of results show that both methods can accurately predict the response of the composite in the elastic area; however Mori-Tanaka Method can achieve better results when forecasting plastic deformations of wood-plastic composites. © 2011 Elsevier Ltd. All rights reserved.


Kek T.,University of Ljubljana | Kusic D.,Slovenian Tool and Die Development Center
Emerging Technologies in Non-Destructive Testing VI - Proceedings of the 6th International Conference on Emerging Technologies in Nondestructive Testing, ETNDT 2016 | Year: 2016

This paper presents the experimental results regarding acoustic emission signals measured during the injection moulding of those standard test specimens commonly used for examining the shrinkage behaviour of various thermoplastic materials. In daily industrial production of different plastic products we often have to deal with various errors that practically occur on the mold primarily as a result of tool wear and tear, improper storage and improper settings on the injection molding machine. In the testing phase of plastic materials we use many times different inserts that are made from standard tool steels, such as OCR12VM. In case of tool steel inserts after some years of usage a few micro-cracks can occur in the early stage, which can later quickly spread according to the applied loading. With the help of different non-destructive testing methods we know that we can most certainly detect possible formation of cracks on the tool steel inserts. The acoustic emission was measured on an injection mould with the visible sign of a crack on the cavity’s surface, using two contact PZT sensors under normal and increased injection pressure loads. In this paper, we focused exclusively on the acoustic emission signal acquisition by using two resonant 150 kHz piezoelectric AE sensors on such tool steel inserts that are already affected by macro-cracks. On such tool steel insert the obtained acoustic emission results were compared with those obtained from a brand new tool steel insert. The final obtained acoustic emission results on the crack defected tool steel insert revealed as expected that the energy and intensity of the captured AE signals is higher compared with the ones that were captured on the brand new engraving insert under same processing conditions. © 2016 Taylor & Francis Group, London.


Kek T.,University of Ljubljana | Kusic D.,Slovenian Tool and Die Development Center | Grum J.,University of Ljubljana
Applied Sciences (Switzerland) | Year: 2016

This paper presents measurements of acoustic emission (AE) signals during the injection molding of polypropylene with new and damaged mold. The damaged injection mold has cracks induced by laser surface heat treatment. Standard test specimens were injection molded, commonly used for examining the shrinkage behavior of various thermoplastic materials. The measured AE burst signals during injection molding cycle are presented. For injection molding tool integrity prediction, different AE burst signals' descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented to define a feature subset in an appropriate multidimensional space to characterize the integrity of the injection molding tool and the injection molding process steps. The feature subset was used for neural network pattern recognition of AE signals during the full time of the injection molding cycle. The results confirm that acoustic emission measurement during injection molding of polymer materials is a promising technique for characterizing the integrity of molds with respect to damage, even with resonant sensors. © 2016 by the authors.


Kek T.,University of Ljubljana | Kusic D.,Slovenian Tool and Die Development Center | Hancic A.,Slovenian Tool and Die Development Center | Grum J.,University of Ljubljana
NDT in Progress 2015 - 8th International Workshop of NDT Experts, Proceedings | Year: 2015

This paper presents the experimental results regarding acoustic emission signals measured during the injection molding of those standard test specimens commonly used for examining the shrinkage behaviour of various thermoplastic materials. In daily industrial production of different plastic products we often have to deal with various errors that practically occur on the mold primarily as a result of tool wear and tear, improper storage and improper settings on the injection molding machine. In the testing phase of plastic materials we use many times different inserts that are made from standard tool steels, such as OCR12VM. In case of tool steel inserts after some years of usage a few micro-cracks can occur in the early stage, which can later quickly spread according to the applied loading. With the help of different non-destructive testing methods we know that we can most certainly detect possible formation of cracks on the tool steel inserts. The acoustic emission was measured on an injection mold with the visible sign of a crack on the cavity's surface, using two contact PZT sensors under normal and increased injection pressure loads. In this paper, we focused exclusively on the acoustic emission signal acquisition by using two resonant 150 kHz piezoelectric AE sensors on such tool steel inserts that are already affected by macro-cracks. On such tool steel insert the obtained acoustic emission results were compared with those obtained from a brand new tool steel insert. The final obtained acoustic emission results on the crack defected tool steel insert revealed as expected that the energy and intensity of the captured AE signals is higher compared with the ones that were captured on the brand new engraving insert under same processing conditions.


Svecko R.,University of Maribor | Kusic D.,Slovenian Tool and Die Development Center | Kek T.,University of Ljubljana | Sarjas A.,University of Maribor | And 2 more authors.
Sensors (Switzerland) | Year: 2013

This paper presents an improved monitoring system for the failure detection of engraving tool steel inserts during the injection molding cycle. This system uses acoustic emission PZT sensors mounted through acoustic waveguides on the engraving insert. We were thus able to clearly distinguish the defect through measured AE signals. Two engraving tool steel inserts were tested during the production of standard test specimens, each under the same processing conditions. By closely comparing the captured AE signals on both engraving inserts during the filling and packing stages, we were able to detect the presence of macro-cracks on one engraving insert. Gabor wavelet analysis was used for closer examination of the captured AE signals' peak amplitudes during the filling and packing stages. The obtained results revealed that such a system could be used successfully as an improved tool for monitoring the integrity of an injection molding process. © 2013 by the authors; licensee MDPI, Basel, Switzerland.


Kobold D.,Slovenian Tool and Die Development Center | Gantar G.,Envita | Pepelnjak T.,University of Ljubljana
Mechanika | Year: 2012

This paper deals with the FEM simulations of anisotropic flow during bulk forming (forging) of magnesium AZ80 wrought alloy at warm conditions. Anisotropic characteristics are described by the classical formulation of Hill's (1948) quadratic anisotropic yield law. To define reliable FEM models capable of carrying out numerical simulations of complex bulk forming operations in a reasonable amount of computing time, a simplified approach for determining Hill's anisotropic coefficients as constants is proposed. On the basis of the determined Hill's anisotropic coefficients and the mechanical properties, the results of an extensive FEM study of lab-scale and industrial-scale forging are shown. FEM results are also compared to the actual obtained results. It is shown that the approach presented can be successfully used in industrial practice.


Kobold D.,Slovenian Tool and Die Development Center | Pepelnjak T.,University of Ljubljana | Gantar G.,Slovenian Tool and Die Development Center | Kuzman K.,University of Ljubljana
Strojniski Vestnik/Journal of Mechanical Engineering | Year: 2010

Light-weight and environmentally friendly materials with good mechanical properties are much appreciated in various modern applications. Weight reduction can improve the performance of many components while reducing the fuel consumption of vehicles. Magnesium is one of the most popular weight-reducing materials because of its low density, good mechanical properties, large natural reserves and good machining properties. The strength, stiffness and favourable metallographic structure of products can be improved by a forging process in which components are shaped from feedstock slugs by applying compressive force through various forging dies. However, widespread usage of forging technology in industrial practice is very rare in comparison to casting, due to the specific deformation characteristics of magnesium having a hexagonal close packed basal crystal structure. This paper deals with the determination of the influence of the most important process parameters on the deformation process of magnesium alloys. On the basis of extensive experimental study, anisotropic flow and the impact of the most important input process factors on the plastic deformation of AZ80 wrought alloy are considered. The results presented in this paper are directly related to industrial practice and have significant potential as a case study for the further development of FEM models capable of predicting anisotropic material flow during applied plastic deformation. The studies presented in the paper also make possible defining recommended technological parameters of the forging process. © 2010 Journal of Mechanical Engineering. All rights reserved.


Svecko R.,University of Maribor | Kusic D.,Slovenian Tool and Die Development Center
Expert Systems with Applications | Year: 2015

The precise positional controls of piezoelectric actuators (PEA) are problematic due to highly-nonlinear hysteresis behavior which is inherent in piezoelectric materials. In existing PEA positional control applications that are based only on neural networks, the obtained control response results are insufficient for practical usage. In this paper we apply a combined approach by using a feedforward neural network (FNN) jointly with a BAT search algorithm in order to improve the positional control of an X-PEA mechanism model by also taking into account the hysteresis behavior. The proposed positional controller was successfully implemented and it was capable of significantly improving the overall control response result of an X-PEA mechanism model by minimizing the overshoot value and steady-state error, and decreasing the settling time. In addition, the BAT search algorithm can also be used for training the FNN, optimizing the FNN topology and reducing the computational complexity. The presented simulation results confirmed that the proposed positional controller with combined approach provides better results compared to the classical FNN control approach. ©2015 Elsevier Ltd. All rights reserved.

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