The paper was published in a leading high-ranking journal dedicated to Additive Manufacturing topics. This paper aims to present a comparison between selective laser sintering and injection moulding technology for the production of small batches of plastic products. The comparison is based on analysing the time–cost efficiencies of each manufacturing process regarding the size of the series for the selected product sample. Both technologies are described, and the times and costs of those individual processes needed to create a final product are assessed when using each of the manufacturing processes. The study shows that the time-cost efficiency of the selected laser sintering technology increases according to the complexity of the product, and decreases with increasing series size and product volume. The presented analysis could be used as a general guideline for a decision-making process regarding the more efficient manufacturing method. In addition, the results show the viability of using selective laser sintering during the early stages of production when fast product availability is required, regardless of the series size. Also, some complementary effects are discussed of using both technologies in the serial production of the same part.
COBISS.SI-ID: 19647766
This article was published in a journal from the database Journal Citation Reports (JCR). In the year of paper publication, the Impact Factor of the journal was 3,667, and the journal ranked in the first quarter (A1) of journals in this research field according to the Impact Factor. The journal is published by Springer US. In this work, the process parameters` optimization problems of the abrasive waterjet machining process are solved using a recently proposed metaheuristic optimization algorithm named Jaya algorithm, and its posteriori version, named as the Multi-Objective Jaya (MO-Jaya) algorithm. The results of Jaya and MO-Jaya algorithms are compared with the results obtained by other well-known optimization algorithms such as simulated annealing, Particle Swam Optimization, Firefly Algorithm, Cuckoo Search Algorithm, Black Hole Algorithm and Bio-geography Based Optimization. A hypervolume performance metric is used to compare the results of the MO-Jaya algorithm with the results of a non-dominated sorting Genetic Algorithm and non-dominated sorting teaching–learning-based optimization algorithm. The results of Jaya and MO-Jaya algorithms are found to be better as compared to the other optimization algorithms. In addition, a multi-objective decision-making method named PROMETHEE method is applied in this work, in order to select a particular solution outof the multiple Pareto-optimal solutions provided by the MO-Jaya algorithm which best suits the requirements of the process planner.
COBISS.SI-ID: 20973846
This article was awarded in 2018 by the IJPR Editor as one of the best articles in the IJPR magazine. In this paper, an analytical model is presented for determination of the throughput performance of Shuttle Based Storage and Retrieval Systems (SBS/RS) in a double-deep storage rack system. This system is used in practice when the required throughput capacity is smaller in comparison with the relatively high warehouse volume. The analytical model is based on a combination of fixed crane with two independent elevators lifting tables, which provide the movement in the vertical direction, and, thus, feeding the shuttle vehicles. Automatic shuttle vehicles are assigned to each tier of the storage rack and provide the movement in the horizontal direction. A real velocity-time characteristic of the lift/shuttle carrier has been applied in the derivation of the model. Based on the real velocity-time characteristic and theoretical basis, expressions have been proposed. for calculation of the mean time for single and dual command cycles, and, thus, throughput performance of SBS/RS.
COBISS.SI-ID: 18821142
The scientific monograph was published by a distinguished international publisher, Nova Science Publishers, New York, USA. In this scientific monograph, a model is presented for the throughput and energy related performance calculations for Shuttle-Based Storage and Retrieval Systems. Shuttle-Based Storage and Retrieval Systems are a special design of autonomous vehicle storage and retrieval systems that are used in practice for handling totes (plastic containers). The system consists of the elevator with elevators lifting table and shuttle carriers. The shuttle carrier is an automatic vehicle that transports totes from the input/output buffer position to any randomly selected storage locations in the storage rack. The elevator with lifting table provides vertical movement for totes to reach any randomly selected inout/output buffer position (tier) in the storage rack. Compared to classical crane-based automated storage and retrieval systems, Shuttle-Based Storage and Retrieval Systems have several advantages, such as: High throughput performance, high flexibility, high scalability, etc. The objective of the proposed model is to find out the best Shuttle-Based Storage and Retrieval Systems` design according to the throughput and energy related performances.
COBISS.SI-ID: 512741693
Highlights: This achievement is very important in several respects: (a) The research involves a state-of-the-art Additive Manufacturing LENS technology for laser metal cladding and product manufacturing, (b) Modelling and optimization of the manufacturing process were carried out using advanced Artificial Intelligence techniques, (c) The study provided significant guidelines for the practical use of the new LENS technology in real industrial environments, and the production of technologically advanced products, (d) The research was carried out in cooperation with EMO Orodjarna d.o.o. in Celje, Slovenia, which is one of the largest toolmaking companies in Slovenia; (e) The paper is highly cited, as it has had 17 citations in the WoS database within 2.5 years; (f) The paper was published in a top international journal with high Impact Factors entitled "Materials and manufacturing processes", published by one of the world's largest and most reputable publishing houses Taylor & Francis, Philadelphia, USA. Current journal rankings: WoS IF = 2.669 (Q2); Scopus SNIP = 1.399 (Q1); SCImago SJR = 0.95 (Q1). Research details: Laser deposition of materials represents a modern additive technology that has a number of advantages over remaining technologies for depositing metallic materials. Besides a low-energy input, a quality bond, and minimal heat-affected zone, this technology is also characterised by the good mechanical properties of the deposited material that are a result of rapid cooling. Despite the prospects, this technology is still at the development phase. New materials and techniques for determining optimal process parameters are being introduced. In this article, we developed a system for modelling (predicting) the properties of the deposited material and used Design of Experiments (DOE) for the laser cladding process parameter selection. Based on the experimental data obtained during the cladding process, models were made for predicting the volume and roughness of the deposited material. Genetic programming was used for modelling the process. Then, a set of several thousand possible combinations (settings) of the machine parameters was produced on the basis of the obtained model. The most appropriate machine (process) parameters were selected in terms of deposition speed, powder efficiency, and surface roughness. These parameters were determined by non dominated sorting. The results offer the operator of the machine a set of appropriate process parameters that enable the production of high quality products. (The research was carried out in cooperation with EMO orodjarna d.o.o. v Celje, Slovenia).
COBISS.SI-ID: 18605334
It is necessary to emphasise that the achievement was published in an extremely well recognised and appreciated journal entitled Precision engineering-journal of international societies for precision engineering and nanotechnology-Elselvier. It is ranked in the top quarter of the best journals from the considered field 18/85 and, in 2017, reached the Impact Factor of 2,582. Since 2017, the achievement has been quoted once. The journal was first published in 1979, and it has been known to many of its readers as the Journal of the American Society of Precision Engineering. The significance of the achievement is reflected in the elaboration of the surface roughness control system for the end milling process with the corresponding simulation model, which simulates the roughness of the machined surface in real time based on measured cutting forces. The effects of introducing the achievement into the industrial environment are lower processing costs, higher product quality, lower ejection, less use of resources (lubricants), and lower consumption of energy inputs in highly expanded high-speed milling machining operations. This paper presents a surface roughness control of end milling with an associated simulation block diagram. The objective of the proposed surface roughness control is to assure the desired surface roughness by adjusting the cutting parameters and maintaining the cutting force constant. An experimentally validated surface roughness control simulator is employed for simulation purposes. Its structure combines Genetic Programming (GP), Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) based models. The focus of this research is to develop a reliable method to predict surface roughness average during the end milling process. An ANFIS is applied to predict the effect of cutting parameters (spindle speed, feed rate and axial/radial depth of cut) and cutting force signals on surface roughness. Machining experiments conducted using the proposed method indicate that by using appropriate cutting force signals, the surface roughness can be predicted within 3 % of the actual surface roughness for various end-milling conditions.
COBISS.SI-ID: 19278870
The paper presents a new approach for determining the current, actual physicochemical condition of mineral hydraulic oil, and the prediction of its remaining useful life. The assessment of the current oil condition is based on the on-line condition monitoring method of all the most important parameters of the oil - on-line condition monitoring, including online parameter control, monitoring trends, alarming and automatic action. Adaptive prediction of the remaining useful life-time of the oil is based on our own developed thermal load test using the same fluid that, based on the initial material parameters of the fluid and the adaptive model of changes, allows prediction of the remaining useful life-time of the oil. The approach is suitable for hydraulic systems with large amounts of hydraulic oil or for machines and devices of exceptional strategic importance.
COBISS.SI-ID: 17886230
The paper presents the results of research related to the use of ionic liquids in hydraulic systems. Developed ionic liquids have excellent lubrication and other properties; they are environmentally friendly and allow the energy-efficient operation of the hydraulic system. The article compares those basic properties of ionic liquids and conventional hydraulic oil that are typical and important for use as hydraulic fluids. In all aspects their properties are much better than oils and are compatible with materials of hydraulic devices.
COBISS.SI-ID: 21990678
The findings presented in this article provide guidelines for robotic milling. A robot`s milling accuracy varies greatly over its workspace, which makes the planning phase of robotic milling very important, i.e. how to place a workpiece into a robot`s workspace, and how to generate tool paths. On the other hand, workpiece positioning into a CNC machine’s workspace is a simple task. Advanced CNC machines are equipped with standardised clamping systems, allowed workpiece dimensions are listed in the machine’s documentation, and tolerance levels of the produced parts are known. This gives users plenty of information and good confidence that they are choosing the best machine for a specific task. If part tolerances allow it, industrial robots can be an alternative to specialised CNC machines, but important information is missing to produce accurate parts. For a standard industrial robot the mechanism`s layout, its dimensions and its reachable workspace are known, but accuracy levels over the robot’s workspace are not. If a workpiece should be milled within certain accuracy limits, the robot’s documentation offers no information on how close it can be located to the borders of the robot’s workspace. This article approaches the mentioned problem with a novel methodology. Based on experimental data, we found that a standard 6 DOF industrial robot’s reachable workspace can be divided into two regions, one with suitable milling accuracy and another with rapidly decreasing milling accuracy. To isolate the suitable accuracy region, a regional nondominated sorting algorithm was applied, and an accuracy contour was extracted, separating the regions . In the second part of the article, a genetic search algorithm based on regional nondominated sorting was applied to find the biggest arbitrary shaped workpiece’s size, position and orientation in the suitable milling accuracy region of the robot’s workspace. Robotic milling is already present in some of the leading technological companies in Slovenia.
COBISS.SI-ID: 20693782
The presented research removes common constraints regarding the design of layout of flexible manufacturing system, and the subsequent search for a good solution is left solely to artificial intelligence. The proposed system is composed of a creative subsystem which can use different evolutionary optimization methods, and a subsystem for evaluating layouts. In the presented work the subsystem for creation uses a particle swarm optimization method for the creation/modification of solution sets. Evaluation of solution quality is made using intelligent search of the shortest travel paths within the layout. This system has proved to be innovative since it proposes very good solutions which are oriented to the main task of the system and are not simplified because of human limitations.
COBISS.SI-ID: 13896470