In machining of multi‐layer metal materials used frequently for the manufac‐ ture of transfer sheet‐metal forming tools, the cutting edge is often damaged because of cutting force peaks. Therefore, a neuro‐mechanistic model, pre‐ sented in this paper, has been created for accurate prediction of cutting forces in helical end milling of multidirectional layered materials. The generalized model created takes into account the complex geometry of the helical end milling cutter, the instantaneous chip thickness and the direction of deposit‐ ing of the individual layer of the multidirectional layered material considered in the calculation through predicted specific cutting forces. For the prediction of specific cutting forces for individual layers a neural network is incorpo‐ rated in the model. The comparison with experimental data shows that the model predicts accurately the flow of cutting force in milling of multidirec‐ tional layered metal materials for any combination of cutting parameters, tool engagement angle and directions of depositing three layers of material. The predicted cutting force values agree well with the values obtained, the maxi‐ mum error of predicted cutting forces is 16.1 % for all comparison tests per‐ formed. © 2020 CPE, University of Maribor. All rights reserved.

This paper presents a cyber-physical fixturing system (CPFS) that provides a new way for smart fixture monitoring in milling processes through cloud based simulation and optimization applications. The purpose of the CPFS is to improve the fixture-workpiece stability and thus to prevent deformations of the machined thin-wall workpiece by controlling the simulated clamping and reaction forces at locators. An equilibrium analysis is employed to model and simulate the behaviour of the fixture-workpiece system with respect to the actual cutting tool position. The simulation is incorporated with an optimization routine to minimize the clamping and locating forces. The smart fixture condition monitoring system is developed by connecting the machine tool to the simulation resources in the fixturing platform which performs instant fixture condition monitoring based on signal processing, cutting force signal feature extraction, fixture layout simulations, clamping forces optimization, simulation of clamping/locating forces and process corrective control actions. A prismatic workpiece with slot milling operation is considered to validate the proposed CPFS system.

Machining of functionally graded metal materials is an important operation in their integration into automotive tool making industry. Effective machining of these materials with changing properties requires detail knowledge of cutting forces which may result excessive product damage. Therefore, in this research, an experimental investigation was carried out to realize the machinability behavior of the fourlayered functionally graded metal material in terms of the nature of the cutting force generated while performing the machining operation. A dynamometer was used to measure the actual cutting forces, which were graphically represented by diagrams depending on the angle of rotation of the cutting tool. The machining of 16MnCr5/316L four-layered metal material, manufactured by the laser engineered net shaping (LENS) process, was performed with a solid carbide ball-end mill. The influence of LENS process parameters, machining parameters and hardness and /or thickness of the deposited layers on resultant maximum cutting forces has been investigated in the analyses. The results were graphically represented.

Multi‐layered functionally gradient metal materials are formed by metal ma‐ terial deposing with Laser Engineered Net Shaping (LENS) technology. LENS is an additive manufacturing technique that employs a high‐power laser as the power source to fuse powdered metals into fully dense three‐dimensional structures layer by layer. Layer thickness is an important factor in machining and processing of such advanced materials, as well as in the production, as a feedback to LENS machine operator. Knowing the thickness of the manufac‐ tured layer of multi‐layered metal material is fundamental for understanding the LENS process and optimizing the machining operations. In this paper, software for visual multi‐layered functionally graded material layer thickness measurement is presented. The layer thickness is automatically determined by the software that is programmed in Matlab/Simulink, high‐level program‐ ming language. The software is using cross‐section metallographic images of cladded layers for thickness measuring. Graphic User Interface (GUI) is also created and presented. The results of measurement are presented to demon‐ strate the efficiency of the developed measurement software. © 2016 PEI, University of Maribor. All rights reserved.

This paper presents a surface roughness control of end milling with 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. For simulation purposes an experimentally validated surface roughness control simulator is employed. Its structure combines genetic programming (GP), neural network (NN) and adaptive neuro fuzzy inference system (ANFIS) based models. Surface roughness control simulator simulates the surface roughness of the part by enabling the regulation of cutting force. The focus of this research is to develop a reliable method to predict surface roughness average during 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 using an appropriate cutting force signals, the surface roughness can be predicted within 3% of the actual surface roughness for various end-milling conditions. Simulation results are presented to confirm the efficiency of a control model. (C) 2015 Elsevier Inc. All rights reserved.

A visual cutting chip control system is designed to automatically adjust feed rate in order to maintain constant surface roughness in ball-end milling. The proposed visual control system has a modular structure, consisting of an optical vision system (OVS), an adaptive cutting chip size-control loop for a feed servo and a surface roughness in-process prediction model. The OVS is employed to acquire the cutting chip sizes form the camera. A division controller is used to control the chip size by modifying the feed rate and consequently maintaining surface roughness constant. Surface roughness is predicted based on the detected chip size. The efficiency of the chip control strategy is tested by series of simulation with various step changes in the cutter/workpiece contact area. For simulation purposes an experimentally validated milling plant simulator with an adopted feed servo drive model and a cutting chip size model is employed. An adaptive neural inference system (ANFIS) is established to effectively simulate the cutting chip size in ball end-milling. In simulation, the reference chip size and consequently the reference surface roughness are well maintained when the cutting-depth profile of a workpiece is varying step-wise or continuously.

Universities and colleges worldwide are the most important factor of globalization for filling the deficit of knowledge and improving the dialogue between people and cultures.In the social role they use their autonomy in discussions about outstanding ethical and scientific issues facing tomorrow's society.Targets of modern academic education stress high flexibility of curricula, mobility of students and teachers, introduction of a study and course credits system , integration into European research projects, etc.We must forget the idea of giving an engineer during their study all the knowledge they might need later on, particularly because engineering knowledge becomes obsolete in some technical spheres within a few years.Training of engineers in Slovenia is a common heritage, therefore at the present stage of development it cannot be left to market laws, since education is a basic human right and a universal human value.

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On the basis of the hybrid process modeling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter, etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identifiers of the process dynamics and primary regulator. An overall procedure of hybrid modeling of cutting process used for creating the CNC milling simulator has been prepared. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability, but also the machining efficiency of the milling system with the adaptive controller is 27% higher than for traditional CNC milling system.

Owing to increasing demands and reduce of human impact on milling processes it is necessary that they are regulated and controlled by new regulation methods.In this article neural network method is described and represented on a concrete milling example.Neural network is developed and tested on measured cutting forces which occur in main coordinates.Neural network is formed to predict best cutting parameters and develop new one if necessary.With this method all logical reflection belongs to computer and trained neural network.Those methods reduce human impact and give us better results than standard optimization.During milling process neural network is trained for so long, that relative error is reduced to minimum.Relative error reduction to required values give us better final tolerance results after milling process and help us to increase milling process to higher intelligent level.

This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The research is concerned with integrating adaptive control and a standard computer numerical controller (CNC) for optimizing a metal-cutting process. It is designed to adaptively maximize the feed rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjusting feed rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.

In the past, the manufacturing industry and scientific research focused on efficiency, precision, and high quality. Better quality, greater demand from consumers, tightened specifications and competition on the global scale market increased efforts to produce better products, faster and cheaper regardless of production methods. The effects of these results were put aside; consequently the impacts of these actions are shown daily. The ecological and economical damage done to the human health and environment is immense. To reduce the effects of these actions, new materials and new methods are being used. Also the governments of many countries are issuing directives for lowering the costs and influence on the

This article presents the development of a system for predicting surface roughness, using a feed-forward neural network. The primary goal was to develop a system in order to predict with complex reliability and defined accuracy. However, this system is designed in such a way that it is also possible to use it for various other workpieces. The described system uses a neural network which receives signals at the input level. The signals then travel through all hidden levels to the output level, where the responses to input signals are received. Data are used which affects the selection of surface roughness regarding the input to the neural network. Three different inputs in total are used for the neural network. Data which represents the inputs to the neural network are encoded, so that they occupy values between 0 and 1. Adequate cutting speed, feed, and depth of cut, are selected in order to achieve an adequate surface roughness of the workpiece, using the trained neural network. This contributes to the optimisation and economy of machining, which is very important during the production of an individual product and also for an individual company or organisation when transferring the final product to the contracting authority or final customer.

The choice of manufacturing processes is based on cost, time and precision. A remaining drawback of modern CNC systems is that the machining parameters, such as feed-rate, cutting speed and depth of cut, are still programmed off-line. The machining parameters are usually selected before machining according to programmer’s experience and machining handbooks. To prevent damage and to avoid machining failure the operating conditions are usually set extremely conservative. As a result, many CNC systems are inefficient and run under the operating conditions that are far from optimal . Even if the machining parameters are optimised off-line by an optimisation algorithm they cannot be adjusted during the machining process. In this paper, a neural adaptive controller is developed and some simulations and experiments with the neural control strategy are carried out. The results demonstrate the ability of the proposed system to effectively regulate peak forces for cutting conditions commonly encountered in end milling operations.

Purpose: This research project was aimed at optimising anaerobic digestion of maize and find out which maturity class of corn and which hybrid of a particular maturity class produces the highest rate of biogas and biomethane. Also the chemical composition of gases was studied. Design/methodology/approach: Biogas and biomethane production and composition in mesophilic (35 degrees C) conditions were measured and compared. The corn hybrids of FAO 300 - FAO 600 maturity class were tested. Experiments took place in the lab, for 35 days within four series of experiments with four repetitions according to the method DIN 38 414. Findings: Results show that the highest maturity classes of corn (FAO 500) increases the amount of biogas and biomethane. The greatest gain of biogas, biomethane according to maturity class is found with hybrids of FAO 400 and FAO 500 maturity class. Among the corn hybrids of maturity class FAO 300 - FAO 400, the hybrid PR38F70 gives the greatest production of biogas and biomethane. Among the hybrids of maturity class FAO 400 - FAO 500, the greatest amount of biogas and biomethane was produced by the hybrid PIXXIA (FAO 420). Among the hybrids of maturity class FAO 500 - FAO 600 the hybrid CODISTAR (FAO 500) the highest production of biomethane. Production of biomethane, which has the main role in the production of biogas varied with corn hybrids from 50-60 % of the whole amount of produced gas. Research limitations/implications: Economic efficiency of anaerobic digestion depends on the optimum methane production and optimum anaerobic digestion process. Practical implications: The results reached serve to plan the electricity production in the biogas production plant and to achieve the highest biomethane yield per hectare of maize hybrid. Originality/value: Late ripening varieties (FAO ca. 600) make better use of their potential to produce

Purpose: Selection of machining parameters is an important step in process planning therefore a new evolutionary computation technique is developed to optimize machining process. This study has presented multi-objective optimization of milling process by using neural network modelling and Particle swarm optimization. Particle Swarm Optimization (PSO) is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present. The goal of optimization is to determine the objective function maximum (predicted cutting force surface) by consideration of cutting constraints. Design/methodology/approach: First, an Artificial Neural Network (ANN) predictive model is used to predict cutting forces during machining and then PSO algorithm is used to obtain optimum cutting speed and feed rates. Findings: During optimization the particles ‘fly’ intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm. The simulation results show that compared with genetic algorithms (GA) and simulated annealing (SA), the proposed algorithm can improve the quality of the solution while speeding up the convergence process. Research limitations/implications: The experimental results show that the MRR is improved by 28%. Machining time reductions of up to 20% are observed. Practical implications: While a lot of evolutionary computation techniques have been developed for combinatorial optimization problems, PSO has been basically developed for continuous optimization problem. PSO can be an efficient optimization tool for solving nonlinear continuous optimization problems, combinatorial optimization problems, and mixed-integer nonlinear optimization problem. Originality/value: An algorithm for PSO is developed and used to robustly and efficiently find the optimum machining conditions in end-milling. This paper opens the door for a new class of EC based optimization techniques in the area of machining. This paper also presents fundamentals of PSO optimization techniques.

Purpose: If measurements of the increased total count of somatic cells in the individual udder quarter or in the whole udder could be determined with electric conductivity by means of the microprocessor-controlled device Mastitron LF 3000 enough precisely to predict the presence of subclinical mastitis. Design/methodology/approach: The occurrence of increased count of somatic cells in milk was found out group by group by the method of measuring the electric conductivity of milk. For the milk conductivity the average measurement from all four udder quarters was taken into account. The population of 102 lactating cows (Black and white, Simmental and Brown Swiss breed) on seven farms fore three summer months was observed. Findings: It was established that higher average electric conductivity than 6.5 mS/cm confirmed in 80% also the increased count of somatic cells in milk. When evaluating the differences between the quarters exceeding 1 mS/cm also a higher total count of somatic cells was confirmed in 73.7%. Moreover, statistically significant relation (P<0.01) between the CMT test and the ECM was found out so it can be claimed that the two methods do not exclude themselves mutually. Research limitations/implications: It was found that the reliability of the ECM in our case was 80% depending to a large extent on the milk composition which changed depending on the stage of lactation, type of feed and general health condition of animals. For a more reliable, accurate and faster implementation of the ECM method further researches will be necessary. Practical implications: Though the ECM method of determination of the subclinical mastitis of the milchcows is well established in the world, it is not yet known well enough in the Slovene practice as a method of diagnosing the subclinical mastitis. Originality/value: It was found that the results of our research in production circumstances were comparable with the indications of the maker and foreign researchers.

The 5S method is a sophisticated Japanese tool for optimisation and improvement of the production process. The purpose of applying the 5S method is to increase the efficiency on the micro-level, keeping workplace clean, in order and accessible. The results are reflected in drastic increase of safety, space utilization, productivity, pride and Kaizen thinking; and drastic reduction of waste, defects, errors, and unnecessary action. This method is one of the foundations of introducing the concept of Lean Production. This article provides an example of applying the 5S method in a shipyard.

Purpose: The paper presents a new hybrid multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Design/methodology/approach: Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. Findings: ACO algorithm is completely generalized and problem independent so it can be easily modified to optimize this turning operation under various economic criteria. It can obtain a near-optimal solution in an extremely large solution space within a reasonable computation time. Research limitations/implications: The developed hybrid system can be also extended to other machining problems such as milling operations. The results of the proposed approach are compared with results of three nontraditional techniques (GA, SA and PSO). Among the four algorithms, ACO outperforms GA and SA algorithms. Practical implications: An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers. Originality/value: New evolutionary ACO is explained in detail. Also a comprehensive user-friendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm.