Heuristic techniques for modelling machine spinning processes
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Heuristic techniques for modelling machine spinning processes Roman Stryczek1
· Kamil Wyrobek1
Received: 7 November 2019 / Accepted: 25 September 2020 © The Author(s) 2020
Abstract In spite of many efforts made a complete model of machine spinning processes, due to its complexity, multidimensionality of the decision space and the present state of knowledge, is unachievable. The paper addresses the issues of constructing a local process model to enable the search for a locally optimal course of the process, within a short time and with the cost as low as possible. Comparison was made between the theoretically well-grounded response surface designs method with a few approaches to the model construction based on intuitively understood heuristic bases justified by their successful practical applications. In order to determine a set of Pareto-optimal solutions for a discrete decision space, the durations of process execution were generated through a virtual simulation. In order to outline and justify the adopted solutions a comprehensive example of the practical construction of the machine spinning process model was presented, including its various versions. The results obtained were validated and evaluated. The main utilitarian conclusion is the indication whereby basing on a partial experiment plan it is possible, thanks to simple heuristic methods, to obtain Pareto-optimal solutions which are close to those obtained when the full experiment plan is carried out. Keywords Machine spinning · Response surface designs · Case based reasoning · Potential function method · Madaline
Introduction The constantly developing field of science, known as knowledge engineering, facilitates the acquisition, structuring, storage and processing of the manufacturing knowledge for engineers. One of the techniques readily deployed in knowledge engineering is the construction of models describing knowledge in a given field, their validation and searching for optimal solutions based thereon. The demand for intelligent planning of manufacturing processes rises as a reflection of the highly competitive market environment that requires, lower production costs shortening production cycle and providing more stable process planning ability (Ma et al. 2020; Ye et al. 2020). A virtual model, although simplified, may provide a range of valuable cognitive information, contributing to the understanding and, consequently, development of a given manufacturing technique. Modelling allows for check-
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Kamil Wyrobek [email protected] Roman Stryczek [email protected]
1
Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, Bielsko-Biala, Poland
ing design assumptions and possibly their quick verification at a relatively low cost. The machining processes carried out on tool machines involve the interaction of the tool and the workpiece. Including all the interactions between the tool and the workpiece in the design process, as well as the dynamic behaviour of the machine tool on which the process will be car
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