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.
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.
Markets are nowadays oriented towards high quality products that are produced in small series production with fast delivery dates. Therefore, manufacturing companies are forced to adapt their production processes and make them more flexible and efficient. In that sense, this paper presents digital transformation of manufacturing as a concept of product innovations in which conventional methods and models are replaced by digital ones through all stages of product lifecycle. Digital transformation or digitalization of manufacturing refers to the interaction between physical and digital models within the production process. That is the crucial step towards the implementation of the Industry 4.0 concept which is fully focused on the integration of manufacturing with information technologies and digital services.
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