Wyniki wyszukiwania - BazTech

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PLM optimization with cooperation of PMS in production stage

Paavel M., Snatkin A., Karjust K.

Archives of Materials Science and Engineering

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2013

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Vol. 60, nr 1

38--45

EN

Purpose: To present a short overview of PLM and its directions including evolution, different phases of PLM and Closed-loop PLM. To describe a new approach in order to improve the closed-loop lifecycle by using production monitoring systems at an early stage of the lifecycle. Monitoring of products in their early life stages in production processes and to use this information for the improvement of the product development and implementation process. Design/methodology/approach: Implementation of a production monitoring system plays an important role in our approach. Sensors that are part of a production monitoring system are sending valuable data from the production to the PLM database where it can be used for production development and analysis. Findings: It is demonstrated that installation of the production monitoring system improved product lifecycle at the beginning of their lifecycle. Feedback from production monitoring systems helps to make better conclusions and quicker decisions in the development phase and gives real-time input to the analysis process. Research limitations/implications: Production monitoring systems improve product lifecycle management in the early stages but do not support the product directly in the other life stages. Decisions can be made only based on this information. Practical implications: Information gathered by production monitoring systems could help to modify the production development and implementation process . Also afterwards the information can be used to identify deviations and mistakes. All this is necessary to fill the gaps in product lifecycle. Originality/value: The proposed installation of production monitoring systems improves new data income to the system that provides better decision making opportunities. Based on this real-time information, it is easier to make changes in the product development phases.

2

Artificial neural networks and evolutionary algorithms in engineering design

Velsker T, Eerme M, Majak J., Pohlak M., Karjust K.

Journal of Achievements in Materials and Manufacturing Engineering

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2011

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Vol. 44, nr 1

88--95

EN

Purpose: Purpose of this paper is investigation of optimization strategies eligible for solving complex engineering design problems. An aim is to develop numerical algorithms for solving optimal design problems which may contain real and integer variables, a number of local extremes, linear- and non-linear constraints and multiple optimality criteria. Design/methodology/approach: The methodology proposed for solving optimal design problems is based on integrated use of meta-modeling techniques and global optimization algorithms. Design of the complex and safety critical products is validated experimentally. Findings: Hierarchically decomposed multistage optimization strategy for solving complex engineering design problems is developed. A number of different non-gradient methods and meta-modeling techniques has been evaluated and compared for certain class of engineering design problems. The developed optimization algorithms allows to predict the performance of the product (structure) for different design and configurations parameters as well as loading conditions. Research limitations/implications: The results obtained can be applied for solving certain class of engineering design problems. The nano- and microstructure design of materials is not considered in current approach. Practical implications: The methodology proposed is employed successfully for solving a number of practical problems arising from Estonian industry: design of car frontal protection system, double-curved surface forming process modeling, fixings for frameless glazed structures, optimal design of composite bathtub (large composite plastics), etc. Originality/value: Developed numerical algorithms can be utilised for solving a wide class of complex optimization problems.

3

Technology design of composite parts

Karjust K., Küttner R., Pohlak M.

Journal of Achievements in Materials and Manufacturing Engineering

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2007

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Vol. 22, nr 2

23-26

EN

Purpose: Purpose of this paper is to optimize the design of the manufacturing technology process of large composite plastic products. One of the key problems is how to integrate computer-based product design and planning of the technology process. Design/methodology/approach: In the current study the Neural Network meta-modelling technique has been used. The optimization of the plastic sheet and its strengthening layer thickness has been performed using the surrogate design model. For modeling and structural analysis of derivative products CAE (ANSYS) and CAD (Unigraphics) systems are used. The Finite Element Analysis simulation was performed with optimal thickness values to verify the prediction accuracy of a surrogate model. Findings: The optimization model is proposed to control and analyze the calculated technology planning route, the optimal vacuum forming processes, the technology of post-forming operations, strengthening and assembling operations. The design of the new products is tightly integrated with manufacturing aspects. The product family of the large composite plastic products together with the derivate products and their production technologies is designed using proposed methodology. The optimization of the plastic sheet and its strengthening layer thickness has been performed. Practical implications: The most of the methods described in this study are now under development and industrial testing. Development of manufacturing (operation) plans for a product family is of great practical importance with many significant cost implications. In design of derivative products for the product family, the nonlinear optimization is used and the detailed description of the product is established. The proposed approach is exemplified by the development of a family of products in Wellspa Inc. Originality/value: Value of this paper is that developed optimization model controls and analyzes the calculated technology planning route.