## 1 Introduction  
During milling operations, the relative vibrations of the tool and the workpiece are the main issues limiting milling efficiency, accuracy, and quality [1, 2]. The level of vibrations depends on various factors such as, for example, workpiece material, tool parameters (e.g., length, diameter, cutting insert geometry, and tool wear), milling process parameters (e.g., cutting speed, feed speed, and depth of cutting), dynamic properties of the tool, the milling center and the workpiece, workpiece clamping conditions (which also affect workpiece dynamic properties), and many more [3, 4, 5]. In the literature, in the context of vibrations occurring during milling, usually self-excited chatter vibrations are recognized as the most significant issue, so various authors concentrate on the development of chatter detection, reduction, and avoidance methods. Some recent, interesting, in-depth reviews on this topic [6, 7, 8, 9] show a large variety of techniques applied to solve the challenges raised by chatter vibration occurrence. However, focusing merely on the self-excited vibrations may lead to the depreciation of other dynamic phenomena such as, for example, forced vibrations [10, 11]. Thus, the chatter phenomenon should not be treated as the sole challenge for the development of vibration suppression methods [10]. Of course, chatter must be eliminated, but even in the case of a stable cutting process, vibration reduction is still an important goal [12]. It is also worth noting that most of the methods proposed for milling vibration reduction are developed and tested on rather small details machined in standard CNC centers. Meanwhile, machine tools used for milling large-sized objects and the related process characteristics differ significantly from the conventional ones [10]. This additionally justifies that the dedicated solutions should take into account not only the dynamic properties of the system (represented by its identified poles) but also the harmonic frequencies of time-varying non-stationary excited vibrations of the whole tool-workpiece system [10; 13]. The convolution of modal vibration (associated with poles), usually having relatively low frequencies with vibration excited by rotating tools, also plays a significant role [14]. Additionally, due to high costs and low availability of large machining centers for scientific experiments, there is a gap in the development of vibration reduction techniques for milling large workpieces. This applies especially to simple and cheap methods that could be easily implemented in industrial practice. In this context, an interesting group of possible solutions for vibration reduction are methods dealing with workpiece clamping to the machine table. Some of them concentrate on the design of clamping elements such as, for example, a system of bilateral supports for machining large aircraft engine components [15] or a modular system for flexible clamping of large workpieces [16]. Others, however, try to minimize geometric errors or milling vibrations by optimizing the layout of passive fixtures using, for example, a table with clamping elements acting from the sides on the workpiece and a matrix of supports from the bottom with the actual selection of active (attached to the workpiece) supports optimized using the Genetic Algorithm [17], selection of the supports' positions for a geometrically complex object (engine block) for surface quality improvement [18], selection of the supports' positions to increase the static fixture stiffness using various Machine Learning algorithms [19], and optimization of the supports' positions to minimize deformations of large ship elements using a modified direct stiffness method and improved Particle Swarm Optimization [20]. An interesting set of examples of active and passive methods and devices, mostly for milling thin-walled elements for the aeronautic industry, is included in [21]. Also, in [22], a review of various approaches for fastening thin-walled objects, including large-size ones, is presented.