End-of-life vehicle (ELV) recycling is a process that spends energy and could be an energy source as well. This part of energy recovering depends on many different factors related to the broad and local aspects of ELV recycling. The ELV recycling process is consuming energy from different energy sources (electrical, fossil), however, this consumption is lower in relation to energy consumption during the production of new vehicle parts from the very beginning. This article attempts to promote an integrated approach in the analysis of the problem of energy recovery through ELV recycling. Authors aim to analyze the ELV recycling process as an energy generator and to present possibilities for its energy recovery. The research analyses are based on the empirical investigation of ELV recycling in the Republic of Serbia, as a developing country, and on defined statistical model presenting the impact of ELV recycling on energy generation, spending, and conservation during one-year intervals. Research results showed that the higher ELV generation rates may led to a higher energy recovery, and environmental and socio-economic sustainability.

Given the application of a multiple regression and artificial neural networks (ANNs), this paper describes development of models for predicting surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling enhancement steel EN 42CrMo4, thermally treated to the hardness level of 28 HRC, using cruciform blade twist drills made of high speed steel with hardness level of 64-68 HRC. The model was developed using process parameters (nominal diameters of twist drills, speed, feed, and angle of installation of work pieces) as input variables varied at three levels by Taguchi design of experiment and measured experimental data for a torque and arithmetic mean deviation of a surface roughness for different values of flank wear of twist drills. The comparative analysis of the models results and the experimental data, acquired for the inputs at the moment when a wear span reaches a limit value corresponding to a moment of the drills blunting, demonstrates that the neural network model gives better results than the results obtained in the application of multiple linear and nonlinear regression models.

Recently, besides regression analysis, artificial neural networks (ANNs) are increasingly used to predict the state of tools. Nevertheless, simulations trained by cutting modes, material type and the method of sharpening twist drills (TD) and the drilling length from sharp to blunt as input parameters and axial drilling force and torque as output ANN parameters did not achieve the expected results. Therefore, in this paper a family of artificial neural networks (FANN) was developed to predict the axial force and drilling torque as a function of a number of influencing factors. The formation of the FANN took place in three phases, in each phase the neural networks formed were trained by drilling lengths until the drill bit was worn out and by a variable parameter, while the combinations of the other influencing parameters were taken as constant values. The results of the prediction obtained by applying the FANN were compared with the results obtained by regression analysis at the points of experimental results. The comparison confirmed that the FANN can be used as a very reliable method for predicting tool condition.

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Purpose -The purpose of this present study is to find a scientific method for the evaluation of environmental impacts according to the requirement 4.3.1. Design/methodology/approach -To realize the objectives, the authors worked with a representative sample from certified ISO 14001 organizations. The data aim to identify and evaluate (according to the organization's methodology) significant environmental impacts. In this study, the authors created two models for the evaluation of environmental impacts based on an artificial neural network applied in the pilot organization and compared the results obtained from these models with those obtained by applying an analytic hierarchy process (AHP) method. AHP is part of an multi-criteria decision making method and provides good multi-criteria support for decision making for problems that can be structured hierarchically. Findings -This paper presents a new approach that uses a backpropagation neural network to evaluate environmental impacts regardless of the organization type. Originality/value -This paper presents a unique approach for the reliable and objective evaluation of environmental impacts.

This paper establish a model for improvement of business processes performances based on quality management system (QMS) through a comparison with top organizational performances INTRODUCTIONThe main objective of this paper is to find an analogy through a comparison between an organization and human body function and to develop an appropriate model for performance improvement of the organization [1]. Applying this model points out crucial points in organization (product/service) which should be improved by priority and tend to gain BE.There are numerous studies that deal with the research of benefits and disadvantages in systems with implemented QMS. Premises on the insignificance of the system of quality regarding the improvement of performance are based on allegations that by that system, procedures are over-emphasized through an excessive care of implementation or non-coverage by procedures and real quality is neglected [2] to [4]. However, most research works point to real benefits of the ISO 9001 implementation, contrary to those who claim that the price of implementation and maintenance of QMS is bigger than profits realized by it [5] to [8]. There are negative premises in literature related with total quality management (TQM) model regarding influence on organizational performances, as it is also the case with the ISO 9001 model. Such premises point to its inapplicability, and therefore, in this paper and in the idea of association of the ISO 9001 and the BE model in direction of the improvement, comparison with performances of organizations that have won an award for excellence as a measure of level of the TQM implementation, was pointless. Therefore, the authors [9] and [10] have chosen to point here to pessimistic attitudes and to promote optimistic premises through review and analysis of literary sources related to that subject. Premises that TQM has no efficiency regarding organizational performances have been included. These premises are accompanied by research works that indicate the difficulty or near impossibility of establishing a relation between TQM and organizational values and the belief that such a relation is unreal [11] and [12].There are many studies that indicate how TQM model implemented into organizational management is not just effective but also efficient even in terms of financial results of the organization [13] to [17].

The paper presents the results of tests and analysis of strength of traditional Montenegrin chair using FEM (The Finite Element Method) according to the procedure described in the relevant European standards. Stresses in the critical points of the structure were obtained and compared with values of permissible stresses for appropriate material. The influence of design parameters of chair on distribution and intensity of the stresses was considered, so it was selected the appropriate combination of parameters that presents the optimal solution from the aspect of the mentioned criterion. The obtained solution is compared with a volume equivalent model of usual chair with four legs. Based on the results we trained neural network in order to make it enable selection of design parameters of the traditional Montenegrin chairs that provide a satisfactory solution

The paper presents a comparative analysis of the models for predicting machined surface quality developed by the application of multiple regression and artificial neural networks. The models were developed using experimental data for the mean arithmetic deviation of surface roughness and the axial cutting force obtained by implementing the Taguchi experiment plan. Comparative analysis of the models has shown that artificial neural networks give the best results in terms of predicting the mean arithmetic deviation of surface roughness on the basis of process parameters and axial cutting force. Keyword-models, axial force, prediction, surface roughness I. INTRODUCTION Experimental research seeks to establish dependence between quality of the machined surface and parameters of the cutting process. Prediction of the machined surface quality through the mean arithmetic deviation of the surface roughness (R a) is made by using a multiple regression mathematical model and by applying models based on artificial neural networks that connect machining process input parameters with the quality of the machined surface. Çiçek, Kivak and Samtaş [1], using the Taguchi experiment design in a drilling operation on austenitic stainless steel AISI 316 with high-speed steel (HSS) twist drills, conventionally and cryogenic, varying feed f (mm/o) and the cutting speed v c (m/min) at two levels, develop a regression model that combines the indicated parameters with the machined surface quality through the mean arithmetic deviation R a (μm), with a 96,3% coefficient of determination. Rodrigues et al. [2] use a regression analysis to obtain a mathematical model that links the spindle speed n (rpm), feed f (mm/rev) and cutting depth a (mm) with the machined surface quality through the mean arithmetic deviation R a (μm) by conducting a full experiment plan and varying of the mentioned parameters at three levels in a turning operation on structural steel with high-speed steel (HSS) tools. The adjusted coefficient of determination in this case is 66,1% which indicates a strong connection between the machined surface quality and the mentioned parameters. Raghunandan, Bhandarkar and Pankaj [3], based on the data obtained using the Taguchi experiment design in a truning operation on EN-19 material with cemented carbide inserts, come up with a model linking a mean arithmetic deviation of surface roughness R a (μm), cutting speed v c (m/min), feed f (mm/rev) and cutting depth a (mm). The adjusted coefficient of determination, which describes the given connection, in this case is 52,8%. Ficici, Koksal and Karacadag [4] investigate the effect of tool modification (twist drill cutting edge grinding in μm), cutting speed v c (m/min) and feed f (mm/rev), using Taguchi experiment design in a drilling operation on austenitic stainless steel AISI 304 with high-speed steel (HSS) twist drills. Development of the regression model links the stated parameters with the machined surface quality through the mean arithmetic deviation R a (μm) and the implementati...