This study introduces a novel educational mentoring platform that emphasises clean manufacturing techniques and is intended to support engineering education and mentorship. The mentoring platform provides practical, project-based learning opportunities in an educational environment. The platform's foundation lies in a mentoring approach through conducting sessions and defined projects. This paper will outline the primary components of a new educational mentoring platform and highlight some of its most important use cases. The mentoring platform enables the creation of programs like the Sustainable Manufacturing Innovation Program, which is showcased in this paper, equipping students with the knowledge to enhance sustainability in mechanical engineering and foster cleaner production startups. The paper briefly overviews the Novel Educational Methodology (NEM) integrated into an e-learning platform for personalized and adaptive learning materials and courses. The combined use of e-learning and mentoring platforms represents a comprehensive educational system, enhancing developmental outcomes while promoting and achieving clean production practices. Both platforms, e-learning and mentoring, are highly adaptable, facilitating the creation of diverse online course materials and mentoring programs across various scientific disciplines.

The increasing demand for environmentally friendly manufacturing processes has led to the need for optimizing the use of cutting fluids in turning and milling processes. Cutting fluids are commonly used in cutting processes to reduce tool wear and improve cutting performance. However, cutting fluids have a negative impact on environment and human health. This paper proposes a conceptual model of аn information system based on digital twin of the production process. This system will enable monitoring of the manufacturing process and provide a decision support system for helping industrial engineers manage its parameters. The model is represented by using SADT (Structured Analysis and Design Technique), and it is presented by using one of the most common problems of optimizing cutting fluid usage in cutting processes on CNC machines from an ecological perspective. The proposed model considers various cutting process parameters (cutting speed, feed rate, depth of cut) and cutting environment factors (cutting process temperature) to determine the optimal cutting fluid flow rate. To optimize the usage of cutting fluid, the smart information system acquires, processes, and stores data from cutting process temperature and cutting fluid flow sensors to establish the correlation between process parameters and sensor data, which is then used to develop a model. The proposed model can be integrated with existing CNC machines to reduce environmental impact while maintaining high productivity. This paper provides a promising approach for optimizing cutting fluid usage in CNC machining processes while promoting ecological sustainability.

This study addresses the integration of sustainability-oriented analytics and decision support in CNC milling through a hybrid information system combining structured data management, sustainability KPIs, rule-based expert reasoning, and machine learning models. The proposed framework is evaluated using the publicly available NASA Ames Milling Tool Wear Dataset. Sustainability indicators related to operational energy demand, tool degradation, and vibration/acoustic-emission response are computed from machining parameters and sensor-derived features. Random Forest and Support Vector Machine models are used for tool wear classification. The expert system applies deterministic rules to identify operational risks, which are combined with machine learning predictions through a hierarchical decision-fusion strategy. Under case-wise cross-validation, the Random Forest achieved a mean classification accuracy of 73.8%, while the Support Vector Machine achieved 68.8%. The expert system most frequently identified elevated vibration-index and acoustic-emission conditions, while critical clean-production risks occurred rarely. Overall, the results demonstrate the feasibility of integrating expert knowledge, sustainability KPIs, and data-driven models into a hybrid information system for decision support in CNC milling environments. The proposed framework provides a foundation for future research on hybrid information systems supporting sustainable manufacturing and intelligent decision making.

The increasing demand for customized medical devices has driven the need for highly integrated, data-driven, and adaptable production and logistics systems. Traditional approaches no longer suffice for patient-specific implants, which require rapid design cycles, strict regulatory compliance, and precise coordination among clinical teams, engineering departments, manufacturing operations, sterilization units, and hospital logistics. Smart Information Systems (SIS) that incorporate IIoT, digital twins, AI analytics, and enterprise platforms have become essential for managing these complex workflows. When combined with Industrial Metaverse technologies such as immersive XR interfaces, multi-user collaboration environments, and persistent digital twins, a new paradigm emerges that connects physical and virtual operations across the entire medical device lifecycle. This paper presents a conceptual, integrated framework that illustrates how immersive collaboration, structured knowledge exchange, and real-time simulation improve design precision, manufacturing dependability, sterilization safety, and logistics efficiency, while supporting Industry 5.0 in healthcare manufacturing. These technologies enable a high level of accuracy, transparency, and human–machine interaction through virtual simulations.