Resource Management and Innovative Optimisation of Machining Modes in the Production of Complex-Shaped Components

• Дата: 2026
• Автори: Petrova, Desislava, Balabanova, Ivelina, Georgiev, Georgi, Lengerov, Angel
• Формат: Стаття
• Мова: Англійська
• Опубліковано: Dr. Viktor Koval 2026
• Теми: Manufacturing Efficiency; Mechanical Engineering; Optimisation; Production; Risk Assessment.
• Онлайн доступ: https://ees-journal.com/index.php/journal/article/view/348
• Назва журналу: Economics Ecology Socium

Background. A pressing challenge in mechanical engineering is increasing labour productivity during the “cutting” operation. This topic is highly relevant because cutting tool capabilities continue to lag behind the technical potential of modern automated turning equipment. The solution is to apply innovative methods and software in production management and mechanical engineering. Purpose. The aim is to analyse and optimise the processing modes of complex parts and manage the implementation of innovations in mechanical engineering to achieve higher efficiency. Findings. A comparable tendency was identified in the second classification category of the probabilistic neural network model, characterised by improved performance indicators. For the model with comparatively lower classification performance, both the first and second output categories achieved the same accuracy of 90.0%. An assessment of the structural and technological characteristics of components with intricate profile geometries indicates that machining efficiency is strongly influenced by the complexity of part geometry and the diversity of manufacturing procedures when automated multifunctional equipment is applied. The final values of the “weight coefficients W” and “bases B” were determined, and the resulting matrix structures support compliance with the minimum Mean-Squared Error (MSE) criterion while increasing the reliability of predictive outcomes in evaluating production risk for mechanically engineered components and systems. Implications. The evaluation of machining-mode selection demonstrates that identifying optimal manufacturing conditions for components with sophisticated profile surfaces processed on automated systems remains a major engineering and economic challenge. Existing approaches for parametric optimisation insufficiently incorporate technological constraints. As the application of materials with specific physical and mechanical characteristics expands, along with the increasing geometric complexity of components and the wider implementation of multifunctional automated systems, technological production planning increasingly depends on the effective determination of cutting parameters and tool geometry, thereby contributing to improved manufacturing performance.