Cooling of motor spindles - a review

Thermally induced loads in motor spindles can cause a number of undesired effects. As a result, the process capability of spindles, and thus, the productivity of a process can decrease. Future motor spindles will be exposed to higher mechanical and especially thermal loads due to trends aiming to increase power densities and maximum speeds. These trends are amplified by increasingly powerful drive concepts and developments in bearing technology. Therefore, researchers assume that it will not be possible to raise the performance potential of spindles due to insufficient cooling of its heat sources. A series of different cooling concepts have been researched and developed in recent decades. These developments have been made for different purposes. They also differ considerably in terms of their cooling principles and cooling performance. In this article, these cooling approaches and the motivations for their development are described. Firstly, the causes of heat development in motor spindles are described in a historical context. Subsequently, the effects of heat development on the manufacturing-relevant properties of motor spindles are revealed. Finally, current deficits in the area of spindle cooling and the need for the development and transfer into industrial practice of more efficient and cost-effective cooling concepts to overcome future challenges are discussed. © 2020, The Author(s)

Multivariate time series data of milling processes with varying tool wear and machine tools

Machining is an essential part of modern manufacturing. During machining, the wear of cutting tools increases, eventually impairing product quality and process stability. Determining when to change a tool to avoid these consequences, while still utilizing most of a tool's lifetime is challenging, as the tool lifetime can vary by more than 100% despite constant process parameters [1]. To account for these variations, all tools are usually changed after a predefined period of time. However, this strategy wastes a significant proportion of the remaining lifetime of most tools. By monitoring the wear of tools, all tools can potentially be used until their individual end of life. Research, development, and assessment of such monitoring methods require large amounts of data. Nevertheless, only very few datasets are publicly available. The presented dataset provides labeled, multivariate time series data of milling processes with varying tool wear and for varying machine tools. The width of the flank wear land VB is used as a degradation metric. A total of nine end milling cutters were worn from an unused state to end of life (VB ≈ 150 µm) in 3-axis shoulder milling of cast iron 600–3/S. The tools were of the same model (solid carbide end milling cutter, 4 edges, coated with TiN-TiAlN) but from different batches. Experiments were conducted on three different 5-axis milling centers of a similar size. Workpieces, experimental setups, and process parameters were identical on all of the machine tools. The process forces were recorded with a dynamometer with a sample rate of 25 kHz. The force or torque of the spindle and feed drives, as well as the position control deviation of feed drives, were recorded from the machine tool controls with a sample rate of 500 Hz. The dataset holds a total of 6,418 files labeled with the wear (VB), machine tool (M), tool (T), run (R), and cumulated tool contact time (C). This data could be used to identify signal features that are sensitive to wear, to investigate methods for tool wear estimation and tool life prediction, or to examine transfer learning strategies. The data thereby facilitates research in tool condition monitoring and predictive maintenance in the domain of production technology

Failure sensitivity and similarity of process signals among multiple machine tools

For a monitoring system to provide considerable performance, it usually requires machine- and process-specific information. This includes information about which process signals are sensitive to failures and which signal behavior indicates these failures. However, this information is mostly unavailable when monitoring the manufacturing of individual parts or small series. The transfer of process-specific information among similar machine tools can provide the required information, thereby improving monitoring performance. Nevertheless, no systematic research exists on what process signals are best suited for such an information transfer. This paper investigates a) whether information about the sensitivity of a signal to failures is transferrable among multiple machine tools and b) whether the behavior of these signals, modelled as probability distributions, is similar among multiple machine tools. Initially, a measure is introduced that quantifies the capability of a signal to separate two process conditions, the signal overlap factor SOF. It is then demonstrated how the SOF can be calculated for transient process conditions. The SOF is then empirically determined for a set of process signals for three different machine tools, individually, to assess failure-sensitivity of the signals for slot milling in steel. Additionally, the SOF is calculated for the union of the data of the machine tools to assess the similarity of signals among machine tools. The set of evaluated process signals includes process forces, the torque of the main spindle, and the torque and position control deviation of the feed axes. All machine tools were operated with identical instructions, tools, and materials. Bores were machined in workpieces to simulate material anomalies. Results suggest that low-pass filtered process forces or position control deviations, if sensitive to failure in a machine tool with linear direct drives, are also sensitive to failure in other machine tools. Also, low-pass filtered process forces were the most similar signals among the investigated machines. Possible causes that impair the similarity of signals among machine tools are discussed

Thermally Induced Clamping Force Deviations in a Sensory Chuck for Thin-Walled Workpieces

Deviations between nominal and actual tolerances are a challenging problem during turning processes of thin-walled workpieces. One main cause of these deviations is the clamping force applied by the turning chuck to hold the workpiece. Due to the low stiffness of thin-walled workpieces, large workpiece deformations can occur even when clamping forces are low. For this reason, the clamping force needs to be precisely adjusted. A possible approach are chucks with integrated actuators. As a result of the more direct power transmission, these chucks have a potentially higher clamping force accuracy compared to conventional external actuation. However, integrated actuators are additional heart sources resulting in thermal loads and thermally induced deformations of the chuck components. Due to the resulting mechanical distortion of the chuck system, the precise adjustment of clamping forces is not possible. Thus, this paper evaluates the thermally induced clamping force deviations on a novel turning chuck with four integrated electric drives. A test bench is used to analyse both a single drive and the combination of all four drives regarding the temperature effect on the clamping force adjustability. A clamping force deviation of up to 26% is observed. Based on the measured chuck temperature, a compensation method is introduced leading to a clamping force accuracy of 96.9%

Sensitivity of process signals to deviations in material distribution and material properties of hybrid workpieces

Hybrid components, made of multiple materials, can meet the increasing demands for lightweight construction and functional integration in the automotive and aircraft industry. Hybrid semi-finished components are produced by applying a high-alloy cladding to a low-alloy base material before hot-forming and machining the workpiece. Throughout this process chain, workpiece deviations in the form of material distribution and material properties can occur that influence the component’s lifetime. This paper investigates whether such workpiece deviations can be detected within the process chain by analyzing process signals obtained from subsequent process steps. For this purpose, artificial workpiece deviations were introduced to hybrid semi-finished workpieces made of C22.8/X45CrSi9-3. Then, process signals during forming and machining were analyzed to determine their sensitivity to the artificial deviations. The results revealed that deviations in cladding size can be effectively monitored using signals from both forming and machining. Cladding position deviations can only be detected during machining, while forming signals are more responsive to detecting the introduced hardness deviations of approx. 100 HV0.1