Tool wear monitoring using vibrations is a complex task, due to various simultaneously occurring vibration sources and due to distortion of the signals acquired. This work investigates the mechanism by which tool wear information is concealed within acquired process-intrinsic vibration signals. Excluding other sources of vibration, such as machine-related, is attempted utilizing process simulations. As a case study, face milling is performed for three different cutting speeds. At first, the resulted simulated wear curves have been compared with experimental ones resulted under the same cutting conditions. Then, a quantification of the effect of tool wear on the acquired signals is presented.

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Additive manufacturing (AM) is a significant development of the manufacturing sector that has emerged during the last decades and tends to change the way products are designed, manufactured, and repaired, enabling unprecedented flexibility levels. The unique process mechanism of AM enables the realization of complex designs after considering design limitations, which are unique to each process mechanism and machine. These limitations, together with the lack of established AM-related knowledge, lead to the design of parts that are not fully conforming with AM buildability restrictions, resulting in failed builds. To this end, this work presents a methodology that enables to embed the AM-related knowledge and use it for an automated manufacturability assessment. The 3D model of a part is used as an input in an.stp format, and the features that are relevant for AM manufacturability are recognized from the global CAD. Then, an analysis of the manufacturability of these features according to the limitations of the process and/or machine is performed, and design changes are suggested to the user aiming to improve the process outputs. The whole methodology is implemented in a software tool with an intuitive user-interface that supports the users in the design of parts that will be made with additive manufacturing.