Additively manufactured stainless steels are gaining considerable attention in the production of complex components, especially in the aerospace, food production, energy, and biomedical industries. Machining and achieving the desired surface properties of such materials remains a challenge. Abrasive waterjet machining technology appears to be one of the options due to the advantages it brings. Removing support structures and separating individual parts is also one of the possible applications of this technology. This study investigates the effects of process parameters for individual cut qualities (Q1–Q5) of abrasive waterjet on the surface properties of additively manufactured stainless steel (SS 316L) specimens, considering the different mechanical properties of the material due to the direction of layering of the material during its production. Experimental specimens were prepared by selective laser melting technology with parameters ensuring the best possible quality of the resulting part. The results of the study showed changes in the topography of the machined surface, especially in the roughness parameters. Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy analysis proved the presence of fragmented abrasive particles in the cut areas.

The growing demand for engineers with practical competencies in industrial automation requires educational approaches that effectively integrate theoretical knowledge with hands-on experience. This study proposes and evaluates a hybrid virtual–physical learning framework for programmable logic controller (PLC) education by combining Factory I/O simulation with deployment on a physical PLC laboratory system. The proposed framework was evaluated using a quasi-experimental pre-test/post-test design with control and experimental groups involving undergraduate engineering students assigned to control and experimental groups. Educational effectiveness was assessed using five complementary performance indicators: knowledge gain, programming errors, task completion time, deployment success rate, and student confidence. The results indicated that students using the proposed framework achieved higher knowledge gains, committed fewer programming errors, completed programming tasks more efficiently, and reported greater confidence than students following a conventional laboratory approach. The findings indicate that integrating virtual industrial simulation with physical PLC implementation may support competency development and students’ preparation for real industrial automation tasks. The proposed framework provides a practical methodology for competency-based PLC education and may contribute to the modernization of engineering curricula by strengthening the connection between virtual simulation and real-world industrial practice.