# Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts
## 1 Introduction  
Lightweight constructions are becoming more and more important for many industries, such as power generation, aerospace, automotive and medical technology. The functional requirements of these parts often lead to the inclusion of thin-walled features in the design of such components. Due to the strict strength and fatigue requirements, such structures are often machined from the bulk as monolithic components, removing a large amount of material to create the final product. Usually this is achieved through intensive milling operations, removing up to 95% of the stock volume, to create the final geometry [1]. An arising trend in thin-walled parts manufacturing is the introduction of additive manufacturing (AM) in the process chain. Indeed, AM allows one to deposit only the material needed for the thin-walled structures, considerably reducing the material waste. This is a critical issue, especially when high cost materials are used, such as titanium or nickel-based alloys. Nowadays, many different AM processes are available on the market [2]: powder bed fusion processes such as electron beammelting or selective laser melting enable the manufacturing of extremely complex geometries, but have strict limitations on the part size [3]; on the other hand, directed energy deposition processes such as laser deposition [4] or wire-arc-additive-manufacturing (WAAM) [5] enable the manufacturing of large parts with a limited features resolution. Despite the great advancements undergone by the AM processes in terms of material quality [6] and repeatability, these technologies often do not meet the dimensional and surface finishing requirements prescribed by the applications [7]. For this reason, milling operations are usually carried out after AM, to correct inaccuracies and improve the surface quality [8]. Therefore, including AM in the process chain requires carrying out machining of thin-walled components. This is a critical operation due to the low stiffness of such workpieces, making them prone to forced vibrations, chatter [9], and deflection issues [10], being responsible for surface location errors [11] and poor surface finish [12; 13]. The general approach used to prevent these issues is to limit the material removal rate (MRR) by means of conservative machining parameters. This generates a reduction in the cutting forces, hence limiting workpieces' vibrations at the expense of reduced productivity. Counteracting process vibrations without affecting the productivity requires the knowledge of workpiece dynamic behavior, i.e., the frequency response functions (FRFs), at each driving point triggered during the milling cycle [14; 15]. This is hardly practicable through experimental techniques, such as impact testing, since the workpiece FRFs change during the milling operation, due to the material removal process [16]. Moreover, in a thin-walled workpiece, the driving points' FRFs are strongly dependent on the excitation point, resulting in a different dynamic behavior over the component. Finite element (FE) models are a convenient way of overcoming these issues, enabling a virtual identification of workpiece dynamic behavior at different driving points [17]. This paper proposes an innovative approach to identify the dynamic behavior of thin-walled workpieces during milling operations. The basis of the proposed technique is the identification of the workpiece dynamics through FE modelling using 2D shell elements [18]. This approach enables an efficient and accurate description of thin-walled structures' dynamics. Moreover, the generation of a shell model could be easily automated exploiting AM deposition path, since it provides the information concerning the workpiece skeleton surface. The proposed technique is divided into three main stages: (i) AM stock modelling, (ii) stock thickness updating and (iii) FRF identification. In the first step, a shell FE model of the deposed material is created.