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    Wyświetlanie 1-8 z 8
    Tytuł:
    Methodologies of knowledge discovery from data and data mining methods in mechanical engineering
    Autorzy:
    Rogalewicz, M.
    Sika, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/407431.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    knowledge discovery
    data mining methods
    data mining methodology
    Opis:
    The paper contains a review of methodologies of a process of knowledge discovery from data and methods of data exploration (Data Mining), which are the most frequently used in mechanical engineering. The methodologies contain various scenarios of data exploring, while DM methods are used in their scope. The paper shows premises for use of DM methods in industry, as well as their advantages and disadvantages. Development of methodologies of knowledge discovery from data is also presented, along with a classification of the most widespread Data Mining methods, divided by type of realized tasks. The paper is summarized by presentation of selected Data Mining applications in mechanical engineering.
    Źródło:
    Management and Production Engineering Review; 2016, 7, 4; 97-108
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data mining model for quality control of primary aluminum production process
    Autorzy:
    Horvath, M.
    Vircikova, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/406754.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    quality control analysis
    data mining
    multivariate autocorrelated process
    quality improvement
    Opis:
    Traditional statistical process control approaches are less effective in dealing with multivariate and autocorrelated processes. With the continual increase in process complexity, this inefficiency is becoming more apparent. A special type of multivariate and autocorrelated process is a process occurring within a heterogeneous production environment (a variety of types of machines, pots, etc. used for the same task). This makes the quality control of such processes more difficult. The approach presented in the paper utilizes time series fitting, cluster analysis and association mining in relation to a single data mining model for the analysis of complex multivariate autocorrelated processes. The aim is to divide the production cells (machines, pots, etc.) into groups exhibiting similar behaviors. This can then be used for more effective quality control of the entire process and afterwards to analyze the reasons for this behavior. This paper includes someof the results obtained from applying the model to an actual multivariate high autocorrelated process, the production of primary aluminum using the Hall-Heroult electrolysis process. The Hall-Heroult electrolysis process is a continual process that is ongoing in several pots simultaneously. The average plant operates 300 pots. Therefore, the quality control of such a complex process faces many issues concerning monitoring and problem diagnosis. The paper describes a method for dividing the pots into control groups exhibiting similar behaviors, which can then be used in the planning phase of the quality control analysis and to make improvements within these groups and thereby within the whole process.
    Źródło:
    Management and Production Engineering Review; 2012, 3, 4; 47-53
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Multi-label Transformation Framework for the Rectangular 2D Strip-Packing Problem
    Autorzy:
    Neuenfeldt Júnior, Alvaro
    Francescatto, Matheus
    Stieler, Gabriel
    Disconzi, David
    Powiązania:
    https://bibliotekanauki.pl/articles/2023851.pdf
    Data publikacji:
    2021-12
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    strip packing problem
    data mining
    multi-label transformation
    classification analysis
    heuristics
    Opis:
    The present paper describes a methodological framework developed to select a multi-label dataset transformation method in the context of supervised machine learning techniques. We explore the rectangular 2D strip-packing problem (2D-SPP), widely applied in industrial processes to cut sheet metals and paper rolls, where high-quality solutions can be found for more than one improvement heuristic, generating instances with multi-label behavior. To obtain single-label datasets, a total of five multi-label transformation methods are explored. 1000 instances were generated to represent different 2D-SPP variations found in real-world applications, labels for each instance represented by improvement heuristics were calculated, along with 19 predictors provided by problem characteristics. Finally, classification models were fitted to verify the accuracy of each multi-label transformation method. For the 2D-SPP, the single-label obtained using the exclusion method fit more accurate classification models compared to the other four multi-label transformation methods adopted.
    Źródło:
    Management and Production Engineering Review; 2021, 14, 4; 27-37
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment
    Autorzy:
    Del Gallo, Mateo
    Ciarapica, Filippo Emanuele
    Mazzuto, Giovanni
    Bevilacqua, Maurizio
    Powiązania:
    https://bibliotekanauki.pl/articles/27324213.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    data mining
    association rules
    optimization model
    production scheduling
    job-shop scheduling
    flow shop scheduling
    Opis:
    Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.
    Źródło:
    Management and Production Engineering Review; 2023, 14, 4; 56--70
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Lean principles for organizing items in an automated storage and retrieval system: an association rule mining – based approach
    Autorzy:
    Bevilacqua, Maurizio
    Ciarapica, Filippo Emanuele
    Antomarioni, Sara
    Powiązania:
    https://bibliotekanauki.pl/articles/407183.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    lean management
    AS/RS
    Association Rules
    Data Mining
    Industry 4.0
    5S
    shoe manufacturing
    Opis:
    The application of the 5S methodology to warehouse management represents an important step for all manufacturing companies, especially for managing products that consist of a large number of components. Moreover, from a lean production point of view, inventory management requires a reduction in inventory wastes in terms of costs, quantities and time of non-added value tasks. Moving towards an Industry 4.0 environment, a deeper understanding of data provided by production processes and supply chain operations is needed: the application of Data Mining techniques can provide valuable support in such an objective. In this context, a procedure aiming at reducing the number and the duration of picking processes in an Automated Storage and Retrieval System. Association Rule Mining is applied for reducing time wasted during the storage and retrieval activities of components and finished products, pursuing the space and material management philosophy expressed by the 5S methodology. The first step of the proposed procedure requires the evaluation of the picking frequency for each component. Historical data are analyzed to extract the association rules describing the sets of components frequently belonging to the same order. Then, the allocation of items in the Automated Storage and Retrieval System is performed considering (a) the association degree, i.e., the confidence of the rule, between the components under analysis and (b) the spatial availability. The main contribution of this work is the development of a versatile procedure for eliminating time waste in the picking processes from an AS/RS. A real-life example of a manufacturing company is also presented to explain the proposed procedure, as well as further research development worthy of investigation.
    Źródło:
    Management and Production Engineering Review; 2019, 10, 1; 29-36
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Data Mining Approach for Analysis of a Wire Electrical Discharge Machining Process
    Autorzy:
    Dandge, Shruti Sudhakar
    Chakraborty, Shankar
    Powiązania:
    https://bibliotekanauki.pl/articles/2023974.pdf
    Data publikacji:
    2021-09
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wire electrical discharge machining
    data mining
    classification and regression tree
    chi-squared
    automatic interaction detection
    classification
    Opis:
    Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.
    Źródło:
    Management and Production Engineering Review; 2021, 13, 3; 116-128
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selection of data mining method for multidimensional evaluation of the manufacturing process state
    Autorzy:
    Rogalewicz, M.
    Piłacińska, M.
    Kujawińska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/407333.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    jakość kontroli
    proces produkcji
    eksploaracja danych
    metoda
    klasyfikacja
    quality control
    process state evaluation
    data mining methods
    classification
    Opis:
    The article deals with the issues involved in evaluating the process state on the basis of many measures, including: process parameters, diagnostic signals and events occurring during the process. These measures as well as those measurements traditionally used in the evaluation of process capability, offer a relevant source of information about the manufacturing process and the authors attempted to ascertain the most suitable method, or group of methods, for achieving this. They present the main criteria for the categorization division of the methods of the manufacturing process state evaluation and, from those identified, distinguish the traditional from Data Mining methods. The authors then specify some basic requirements regarding the desired method or group of methods and focus on the classification problem. A division and classification of the methods is made and briefly described. Finally, the authors specify the criteria for their selection of the Data Mining method type as being the most appropriate for the evaluation of the manufacturing process state and, from within this type, offer the most suitable groups of methods. Some directions for further research are discussed at the end of the article.
    Źródło:
    Management and Production Engineering Review; 2012, 3, 2; 27-35
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data
    Autorzy:
    Singh, Juhi
    Mittal, Mandeep
    Powiązania:
    https://bibliotekanauki.pl/articles/1841413.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    categorical data
    clustering algorithm
    frequent pattern mining
    association rules
    customer relationship management
    Opis:
    Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer’s next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.
    Źródło:
    Management and Production Engineering Review; 2021, 12, 2; 57-64
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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