Jokin Munoa

Summary Turning operations, particularly in internal machining, require the use of slender tools whose high dynamic flexibility promotes the onset of self-excited vibrations, thereby limiting the cutting performance. Traditionally, this issue has been addressed using anti-vibratory bars, which incorporate a damper to attenuate tool vibrations. However, their added complexity and cost have led to reluctance among end users, motivating the search for alternative solutions. This study proposes an innovative strategy based on inducing modal coupling between the tool and machine components to damp the tools, eliminating the need for anti-vibratory tools. To achieve this, a tuneable toolholder is designed with an easy integration into the machine. This toolholder enforces modal coupling with standard bars, enhancing process stability. The system operates over a wide frequency range, enabling vibration attenuation across various bar configurations. Its development is supported by theoretical analyses and simulation tools to evaluate tuning capability and optimize the dynamic response, aiming to ensure stable turning conditions.

Hydrostatic rotary tables are widely used in the machine tool industry for high-precision machining applications. The standard design approach relies on two-dimensional (2D) Reynolds finite element models that treat the bearing gap as a rigid, uniform scalar per ring. Nevertheless, at the scale of these machines, elastic deformations become comparable to the oil film thickness. In this study, a monolithic three-dimensional elasto-hydraulic model is compared with the standard 2D benchmark for a 5.5 m diameter cast iron table with two concentric bearing rings, over a load sweep (115–230 t) and six clamping-rectangle configurations. At the nominal load, the 2D model underestimates the inner-ring mean film by 21%, overestimates the minimum outer-ring film by 30%, and underestimates the pumping power by 24%, all percentages growing with load. Independently of load, clamping configuration alone produces an inner-ring film dispersion of up to 88% of the 2D value and a ring load-sharing ratio varying by a factor of 2.8. These effects are invisible to the 2D framework and, in the most compact configuration, sufficient to collapse the oil film. These results indicate that the 2D model, while adequate for preliminary sizing, cannot substitute for the coupled model when verifying guideway safety margins.

This study presents a digital twin of 5-axis milling processes for thin-walled parts to predict and prevent the occurrence of static deflection issues, surface location error due to forced vibration, and regenerative chatter. Driven by real-time machine signals or machining programs, the twin continuously simulates the milling process given an initial workpiece and any 5- axis toolpath. Thanks to the implementation of an automatic mesher and a finite element model generator, the stiffness and dynamics of the part are determined based on its current geometry. This enables the digital twin to predict and visualize in 3D the static deflection, forced vibration, and process stability, allowing optimization of the process and avoidance of these issues. Finally, the twin is validated on a 5-axis machining case of a steel blade, in which a surface location error caused by a resonance is diagnosed and eliminated.

A photogrammetry-based error compensation solution, comprising calibration, positioning compensation and accuracy validation methodologies, is presented to the aerospace sector, able to assist industrial robots in manufacturing new composite materials, offering versatility and reconfigurability at a lower cost than that resulting from the currently used milling machines. Against a ground truth measured by a laser tracker, it has boosted, in real time, the accuracy level from ±0.685 to ±0.203 mm, on average, and from ±1.621 to ±0.498 mm at peak, following the ISO 9283 standard, and from ±0.534 to ±0.080 mm, on average, and from ±1.804 to ±0.456 mm at peak, with a real part in a large volume under industrial operating conditions, taking into account occlusions and showing robustness against the impact of the payload, the waviness, and the backlash.

Ensuring the structural integrity and service reliability of railway wheels has become a key challenge in modern manufacturing and maintenance strategies within the railway sector. In this context, Eddy Current (EC)-based Non-Destructive Testing (NDT) provides an automated and efficient approach for detecting surface and near-surface defects, while reducing inspection time and operator dependency compared to conventional manual methods. This study presents the integration of an EC inspection system into a precision lathe, enabling in-machining evaluation during wheel turning. Experimental validation was conducted on wheels with artificial defects, yielding high signal-to-noise ratios and enabling reliable defect characterization. Furthermore, computationally efficient and easily deployable machine learning algorithms were developed to enable automatic defect detection, localization, and size estimation. The results confirm the feasibility of in-machine EC inspection during machining operations, enabling early defect detection and contributing to safer, more efficient, and higher-quality manufacturing processes in the railway sector.

The generation of the surface topography in machining is a complex process whereby the dynamics of the machine-tool and the kinematics of the cutting process are combined. The purpose of developing a surface topography simulation model in turning is to predict the combined effect of the tool geometry, process parameters, and relative tool-workpiece vibration. The simulations demonstrate that a single vibration frequency can generate multiple wavelengths on the workpiece profile. The correlation between machine-tool vibrations and surface topography is applied to accurately diagnose the machine defect that results in poor surface quality in a cylindrical turning process. Key words: surface topography, tool-workpiece vibration, waviness profile

In large-scale machine tool applications, the presence of low structural natural frequencies limits the cutting capabilities of the machine. The machine tool joints interact with the structural mode shapes, hence, the feed drive system characteristics can significantly influence the resultant dynamics at the cutting point. This paper investigates the effect of guideway non-linear friction and feed drive motion control parameters on chatter stability predictions. Field experimentation on seven machines reveals substantial differences between in-motion and idle dynamics, leading to errors in traditional process stability predictions. By using a one-degree-of-freedom model that incorporates non-linear friction and controller forces together with motion commands, the effect of axis motion on machine tool dynamics is analyzed. Later, the feed and force non-linearities are studied in a large-scale machine tool using traditional and alternative dynamic characterization techniques. The findings demonstrate that both feed and force non-linearities influence the frequency response functions at the cutting points, ultimately affecting the accuracy of process stability predictions. Proper selection of feed drive control parameters reduces the cutting point compliance, improving machine tool productivity by up to 50%.

Chip morphology is an intrinsic characteristic of the machining process that determines the quality of the process. When machining low machinability materials, the chips formed are usually long, continuous, and difficult to break. Due to the negative effect of the accumulation of the chip along the process, chip breakage and the correct extraction out of the machining area have become indispensable requirements. Although numerous chip-breaking methodologies have been proposed, modulation-assisted machining (MAM) is one of the most promising approaches, due to its independence from the workpiece material, tool geometry, and cutting conditions. In this work, a comparison of different modulation-assisted machining strategies, based on the modulation of the feed (F-MAM) or the depth of cut (D-MAM), were experimentally evaluated and compared to conventional turning in terms of chip morphology, surface roughness, and tool wear. Results showed that both MAM strategies enabled chip breakage and improved chip evacuation in comparison to conventional turning; however, D-MAM showed a better performance in terms of tool wear and surface roughness.