Iskanje po katalogu digitalne knjižnice

Opis: Servitization is often based on technology, with the producer not selling products but rather offering product-related services. While servitization had been steadily gaining interest until relatively recently, a new trend called deservitization, the outsourcing of service provision, has seen a slow uptake in the scientific literature. This work analyses why servitization is not always beneficial. We analyze the effect of product complexity on servitization and deservitization in three Southern European countries. Due to high competition and knowledge leaking, manufacturers of complex products tend to servitize with their own resources, thus avoiding deservitization or outsourcing of service provision. The analysis is performed using two-step OLS regression. The results confirm that the hypotheses and the model are significant and that manufacturers of simple products tend to deservitize, while manufacturers of complex products tend to servitize. Managerial implications refer to alternatives as to when to enter the servitization arena and when it is more beneficial to deservitize.
Ključne besede: servitization, deservitization, manufacturing technology, EMS
Objavljeno v DKUM: 26.03.2025; Ogledov: 0; Prenosov: 2
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Opis: This study investigates the predictive modelling of weld bead geometry in wire arc additive manufacturing (WAAM) through advanced machine learning methods. While WAAM is valued for its ability to produce large, complex metal parts with high deposition rates, precise control of the weld bead remains a critical challenge due to its influence on mechanical properties and dimensional accuracy. To address this problem, this study utilized machine learning approaches—Ridge regression, Lasso regression and Bayesian ridge regression, Random Forest and XGBoost—to predict the key weld bead characteristics, namely height, width and cross-sectional area. A Design of experiments (DOE) was used to systematically vary the welding current and travelling speed, with 3D weld bead geometries captured by laser scanning. Robust data pre-processing, including outlier detection and feature engineering, improved modelling accuracy. Among the models tested, XGBoost provided the highest prediction accuracy, emphasizing its potential for real-time control of WAAM processes. Overall, this study presents a comprehensive framework for predictive modelling and provides valuable insights for process optimization and the further development of intelligent manufacturing systems.
Ključne besede: wire arc additive manufacturing, WA AM, predictive modelling, machine learning, weld bead geometry, XGBoost
Objavljeno v DKUM: 13.03.2025; Ogledov: 0; Prenosov: 6
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Opis: Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards
Ključne besede: smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 6
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Opis: The main purpose of this study is to examine how the use of management tools supports the readiness of manufacturing organizations for the implementation of Industry 4.0. The originality of the research is reflected in the exploration of the relationship between the use of the selected well-known management tools and their readiness for the implementation of Industry 4.0, which was assessed using a combination of two models—one developed by the National Academy of Science and Engineering (Acatech) and the other by the University of Warwick. The relationship was assessed by applying structural equation modeling techniques to a data set of 323 responses from employees in manufacturing organizations. The results show that the use of six sigma, total quality management, radio frequency identification, a balanced scorecard, rapid prototyping, customer segmentation, mission and vision statements, and digital transformation is positively associated with Industry 4.0 readiness. Inversely, outsourcing and strategic planning are negatively associated with Industry 4.0 readiness, while lean manufacturing, which is often emphasized as the cornerstone of Industry 4.0 implementation, is not associated with Industry 4.0 readiness in our study. These findings can help organizations to understand how to consider and measure readiness for the implementation of Industry 4.0 more comprehensively and present guidelines on how the use of management tools in manufacturing organizations can foster their implementation of Industry 4.0 principles.
Ključne besede: industry 4.0, readiness, implementation, management tools, manufacturing organizations
Objavljeno v DKUM: 16.01.2025; Ogledov: 0; Prenosov: 6
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Opis: Industrial robots are slowly finding their way into manufacturing companies. This paper examines the impact of robots on productivity, exports, quality, sustainability and labor in European manufacturing companies. There is little research on the use of industrial robots and their impact in developed countries. Most research relates to Chinese companies, and often, the data are outdated. The data in this paper come from the European Manufacturing Survey project, which was conducted in 2022 and includes 476 manufacturing companies. The results of the impact of industrial robots on quality, labor productivity, exports and green technologies are determined using a T-test between companies that use industrial robots and those that do not. However, the impact of higher investment in environmental technologies by industrial robot users was examined by a two-stage OLS regression analysis with control variables representing the contextual characteristics of the companies. The results show positive effects on all of the variables. The results show that the greater use of robots occurs in industries with low-to-medium technology intensity, that robots contribute to labor productivity and exports and that companies that use robots also tend to use environmentally friendly technologies.
Ključne besede: industrial robots, productivity, quality, exports, environmental, sustainability, European manufacturing survey
Objavljeno v DKUM: 09.12.2024; Ogledov: 0; Prenosov: 3
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