Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Predicting the microstructure of compacted graphite iron using a fuzzy knowledge‑based system

Gumienny Grzegorz, Macioł Andrzej, Macioł Piotr

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 2

art. no. e87, 2023

One of the important engineering materials is compacted graphite iron (CGI). Obtaining an expected microstructure leading to desired material properties is relatively difficult. In this paper, we present an approach to predicting the microstructure with a fuzzy knowledge-based system. On the basis of the results of statistical analysis and expert knowledge, an original taxonomy of CGI casts was formulated. The procedure of data acquisition, specimen preparation, analysis procedure and microstructures obtained are presented. Methods for expert experience-supported knowledge extraction from experimental data, as well as methods for formalizing knowledge as fuzzy rules, are introduced. The proposed rulesets, the reasoning process, and exemplary results are provided. The verification results showed that, using our approach, it is possible to effectively predict the microstructure and properties of CGI casts even in the absence of sufficient data to use data-driven knowledge acquisition. On the basis of the results obtained, examples of possible applications of the developed approach are presented.

Quick Response Manufacturing for High Mix, Low Volume, High Complexity Manufacturers

Duda Jerzy, Macioł Andrzej, Jędrusik Stanisław, Rębiasz Bogdan, Stawowy Adam, Sopińska-Lenart Monika

Management and Production Engineering Review

2021

Vol. 12, No. 1

72--84

The market of consumer goods requires nowadays quick response to customer needs. As a consequence, this is transferred to the time restrictions that the semi-finished product manufacturer must meet. Therefore the cost of manufacturing cannot determine how production processes are designed, and the main evaluation function of manufacturing processes is the response time to customers’ orders. One of the ideas for implementing this idea is the QRM (Quick Response Manufacturing) production organization system. The purpose of the research undertaken by the authors was to develop an innovative solution in the field of production structure, allowing for the implementation of the QRM concept in a Contract Manufacturer, which realizes its tasks according to engineering-to-order (ETO) system in conditions defined as High Mix, Low Volume, High Complexity. The object of the research was to select appropriate methods for grouping products assuming that certain operations will be carried out in traditional but well-organized technological and/or linear cells. The research was carried out in one of the largest producers of sheet metal components in Europe. Pre-completed groupings for data obtained from the company had indicated that – among the classical methods – the best results had been given by the following methods: King’s Algorithm (otherwise called: Binary Ordering, Rank Order Clustering), k-means, and Kohonen’s neural networks. The results of the tests and preliminary simulations based on the data from the company proved that the implementation of the QRM concept does not have to be associated with the absolute formation of multi-purpose cells. It turned out that the effect of reducing the response time to customer needs can be obtained by using hybrid structures that combine solutions characteristic of cellular systems with traditional systems such as a technological, linear, or mixed structure. However, this requires the application of technological solutions with the highest level of organization.