Vasileska, Ema
Preferred name
Vasileska, Ema
Official Name
Vasileska, Ema
Main Affiliation
5 results
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Item type:Publication, Statistical analysis and machine learning-based modelling of kerf width in CO2 laser cutting of PMMA(Jan Evangelista Purkyne University in Usti nad Labem, 2024-12-21); ; ; Engineering polymers like PMMA have increasingly replaced traditional materials where feasible, with CO2 laser cutting gaining attention for its high quality and speed in processing these materials. Precise cuts are essential for product accuracy, with kerf width being a key quality attribute for ensuring the final product's quality. This study focuses on the impact of three process variables: stand-off distance, laser power, and cutting speed, on the kerf width in CO2 laser cutting of PMMA. A full-factorial experiment systematically varies process parameters to understand their individual and interaction effects on the cutting process. The kerf width is measured as an indicator of precision to evaluate the quality of the laser cuts. In order to address the non-linear relationships between these process parameters and kerf width, several machine learning models were utilized. Performance comparisons indicated that the Artificial Neural Network (ANN) model provided the highest accuracy, with R² values of 0.98 for the validation dataset and 0.95 for the testing dataset. The optimized ANN model is a robust tool for parameter optimization, determining optimal settings to achieve the desired kerf width and ensure productivity. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, BRIDGING THE GAP: QUALITATIVE COMPARATIVE ANALYSIS OF INDUSTRY 4.0 AND INDUSTRY 5.0(University Ss. Cyril and Methodius in Skopje, 2024); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Experimental Analysis of the Influence of Process Parameters on Cylindricity in Turning Process(University Ss. Cyril and Methodius in Skopje, 2025-05-30); Cylindrical parts produced through turning often demand certain form tolerances such as cylindricity to ensure proper function, reliability, and performance. As manufacturing shifts toward higher accuracy and sustainability, understanding how process parameters influence cylindricity becomes increasingly essential. This study presents an experimental analysis of the influence of three fundamental cutting parameters – depth of cut, feed rate, and spindle speed – on the cylindricity of parts produced under dry turning conditions. The experiments were performed on steel E335 using a full-factorial design with parameters varied at two levels. Cylindricity was measured on each machined part and statistically analyzed to evaluate individual effects and interactions of the parameters. Results show that all three parameters significantly affect cylindricity, with spindle speed exhibiting the highest statistical influence. Higher spindle speeds were associated with improved cylindricity, while increased feed rate and depth of cut tended to degrade form accuracy. A regression model was fitted to the experimental data to quantify the influence of each parameter and predict cylindricity actions based on cutting conditions. The findings align with recent literature and offer practical insights for optimizing dry turning operations to achieve higher geometric precision. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of longitudinal and cross feed on flatness and surface roughness in flat grinding(University Library in Kragujevac, 2025-03-31); This research focuses on analyzing the impact of longitudinal and cross feed on the grinding quality, with particular emphasis on the parameters of surface roughness and flatness of the machined surfaces. The main objective is to examine how different combinations of these parameters affect the surface roughness and flatness. The experimental study is conducted using a surface grinding machine, where roughness (Ra) and flatness are measured under various longitudinal and cross feed settings. A full-factorial experimental design is implemented to systematically analyze the interaction between these factors. Statistical methods, including regression analysis, are used to determine the significance of the variables and their effect on surface characteristics. The results indicate that both longitudinal and cross feed affect the roughness and flatness of the machined surfaces. These findings highlight the need to optimize process parameters to balance surface roughness and flatness for improved machining performance. The insights gained from this research provide valuable guidelines for optimizing grinding operations in industrial applications, ultimately enhancing efficiency and product quality. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing manufacturing efficiency - A Lean Industry 4.0 approach to retrofitting(Mechanical Engieneering-Scientific Journal, 2023-12); ; Industry 4.0 technologies are already affecting global supply chains by revolutionizing how companies manufacture and distribute their products and services. Many companies are affected by this transformation, especially small and medium-sized enterprises (SMEs) that are keen to enhance their market competitiveness as quickly and easily as possible. The transformation from traditional approaches to Industry 4.0 can bring many benefits, however, this transition involves adopting technologically advanced machinery with a high level of digitalization and communication. The cost and time to replace old machines could be unsustainable for many SMEs and that is why these enterprises seek alternative solutions for the digitalization of their legacy machines, such as retrofitting. This paper conducts a review of both retrofitting and Lean Industry 4.0 (Lean 4.0) to identify the challenges and benefits of both concepts and explore how they can interact and merge with each other to help SMEs to increase their market competitiveness.