Process planning is one of the most difficult tasks in product development caused by the large number of technical, technological, economic, environmental and other criteria. Accordingly, the selection of manufacturing processes is a complex multi-criteria decision making problem since it considers a number of possible alternative manufacturing processes in addition to a large number of specified criteria. This paper represents the computer-aided methodology for the multi-criteria evaluation and selection of manufacturing processes at the stage of conceptual process planning. The developed methodology is primarily focused on the mapping of product design and manufacturing requirements. Manufacturing processes that fail to meet the given conditions on the basis of 10 criteria such as materials, production volume, productivity, dimensional accuracy, surface finish, etc., are eliminated according to the developed rules. Then, the multi-criteria evaluation and ranking of manufacturing processes is performed based on 5 criteria: manufacturing cycle time, process flexibility, material utilization, quality and operating costs. Based on this methodology, a system is developed for the multi-criteria selection of manufacturing processes, whose implementation is presented in the case of the hip joint endoprosthesis.

Thin-walled parts made of aluminum alloy are mostly used as structural elements in the aerospace, automobile, and military industries due to good homogeneity, corrosion resistance, and the excellent ratio between mechanical properties and mass. Manufacturing of these parts is mainly performed by removing a large volume of material, so it is necessary to choose quality machining parameters that will achieve high productivity and satisfactory quality and accuracy of machining. Using the Taguchi methodology, an experimental plan is created and realized. Based on its results and comparative analysis of multi-criteria decision making (MCDM) methods, optimal levels of machining parameters in high-speed milling of thin-walled parts made of aluminum alloy Al7075 are selected. The varying input parameters are wall thickness, cutting parameters, and tool path strategies. The output parameters are productivity, surface quality, dimensional accuracy, the accuracy of forms and surface position, representing the optimization criteria. Selection of the optimal machining parameter levels and their ranking is realized using 14 MCDM methods. Afterward, the obtained results are compared using correlation analysis. At the output, integrative decisions were made on selecting the optimal level and rank of alternative levels of machining parameters.

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Original scientific paper Due to the complexity of grinding process of multilayer ceramics, and the need for a specific product quality, the choice of optimal technological parameters is a challenging task for the manufacturers. The main aim of investigation is to secure the demanded final product quality (plane parallelism) in the function of input parameters (machine, machine operator, foil and production line). "Soft computing techniques" are becoming more interesting to the researchers for the modelling of processing parameters of complex technological processes. In this paper, a soft computing technique, known as the Artificial Neural Networks (ANN), is used for the modelling and prediction of parameters of technological process of CNC grinding of multilayer ceramics. The results show that the ANN with the back-propagation algorithm justifies the application also to this problem. By designing different architectures of ANN (learning rules, transfer functions, number and structure of hidden layers and other) on the set of data from the productiontechnological process, the best result of RMS error (10,76 %) in the process of learning and 12,07 % in the process of validation was achieved. The achieved results confirm the acceptability and the application of this investigation in the technological and operational preparation of production. Keywords: grinding, neural networks, prediction, soft computing Uporaba tehnike mekog računalstva za modeliranje i predviđanje postupka CNC brušenjaIzvorni znanstveni rad Zbog složenosti procesa brušenja višeslojne keramike te osiguranja zahtijevane kvalitete proizvoda, odabir optimalnih tehnoloških parametara je izazovan zadatak za proizvođače. Osigurati traženu izlaznu kvalitetu proizvoda (paralelnost površina) u funkciji ulaznih parametara (stroj, operater stroja, folija i proizvodna linija) predstavlja glavni cilj istraživanja. "Tehnike mekog računalstva" dobivaju pozornost istraživača za modeliranje procesnih parametara složenih tehnoloških procesa. U ovom radu koristi se tehnika mekog računalstva poznata kao umjetne neuronske mreže (ANN) za modeliranje i predviđanje parametara tehnološkog procesa CNC brušenja višeslojne keramike. Rezultati su pokazali da ANN s algoritmom širenja unazad potvrđuje primjenu i na ovaj problem. Oblikovanjem različitih arhitektura ANN (pravila učenja, prijenosne funkcije, broj i strukture skrivenih slojeva i drugi) na setu podataka iz proizvodno -tehnološkog procesa ostvaren je najbolji rezultat RMS greške od 10,76 % u procesu učenja i 12,07 % u procesu validacije. Ostvareni rezultati potvrđuju prihvatljivost i primjenu ovog istraživanja u tehnološkoj i operativnoj pripremi proizvodnje.

In the paper, the influence of tool shoulder geometries on the mechanical properties and weld surface roughness of an Al-Mg alloy was studied. Different types of tools were used: with straight and concave profiles. Three concave-shoulder types were applied, with volume ratios of 0.5 and 0.9 of the square pin to the shoulder reservoir and one with three concentric semi-toroidal reservoirs with a volume ratio of 0.5 of the pin to the shoulder reservoir. The tensile and bend properties, hardness profiles and macro-features of welds were examined. It was found that the optimum tensile and bending properties were obtained when applying the tool with concentric reservoirs and the lowest welding speed. In this way, the widest nugget zone at the plate axis is obtained, as well as the thickest nugget-zone layer under the specimen surface, covering the thermomechanical and heat-affected zones. The overlapping of the nugget zone with the thermomechanical and heat-affected zones enables higher proof and ultimate tensile strengths compared to the base material. The surface-roughness parameters of the weld face are lower for the specimens welded with the tools with reservoirs and considerably lower than the base-material edge-surface roughness. Keywords: friction-stir welding, 5052 aluminum alloy, FSW parameters, joint properties, surface roughness V~lanku je prikazana raziskava vpliva geometrije boka orodja na mehanske lastnosti in hrapavost povr{ine zvara Al-Mg zlitine. Uporabljeni sta bili orodji z ravnim in konkavnim profilom. Uporabljene so bile tri vrste konkavnih bokov, z razmerjem volumna 0,5 in 0,9 bo~nega rezervoarja ter kvadratne konice in eden s tremi poltoroidnimi koncentri~nimi rezervoarji, z razmerjem volumnov 0,5 konica-bok. Preiskovane so bile natezne in upogibne lastnosti, profili trdote in makro izgled zvarov. Ugotovljeno je, da so bile optimalne natezne in upogibne lastnosti dobljene pri uporabi orodja s koncentri~nimi rezervoarji pri najmanj{i hitrosti varjenja. Na ta na~in se dose`e naj{ir{e podro~je me{anja pod povr{ino vzorca, ki pokriva termomehansko in toplotno vplivano podro~je. Prekrivanje podro~ja me{anja s termomehanskim in toplotno vplivanim podro~jem, omogo~a vi{jo mejo plasti~nosti in vi{jo natezno trdnost, v primerjavi z osnovnim materialom. Parametri povr{inske hrapavosti~ela zvara so manj{i pri vzorcih zvarjenih z orodji z rezervoarji in so ob~utno ni`ji, kot je osnovna hrapavost roba materiala. Klju~ne besede: torno vrtilno varjenje, aluminijeva zlitina 5052, parametri FSW, lastnosti spoja, hrapavost povr{ine

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Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning.

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