Review of Superfinishing by the Magnetic Abrasive Finishing Process

Recent developments in the engineering industry have created a demand for advanced materials with superior mechanical properties and high-quality surface finishes. Some of the conventional finishing methods such as lapping, grinding, honing, and polishing are now being replaced by non-conventional finishing processes. Magnetic Abrasive Finishing (MAF) is a non-conventional superfinishing process in which magnetic abrasive particles interact with a magnetic field in the finishing zone to remove materials to achieve very high surface finishing and deburring simultaneously. In this review paper, the working principles, processing parameters, and current limitations for the MAF process are examined via reviewing important work in the literature. Additionally, future developments of the MAF process are discussed.

Effect of tool wear on quality in drilling of titanium alloy Ti6Al4V, Part II: Microstructure and Microhardness

Drilling of Ti6Al4V with worn tools can introduce superficial and easily measured features such as increase of cutting forces, entry and exit burrs and surface quality issues and defects. Such issues were presented in the part I of this paper. In part II, subsurface quality alterations, such as changes of the microstructure and microhardness variation is considered by preparing metallographic sections and measurement, mapping of the depth of grain deformation, and microhardness in these sections. Drastic changes in the microstructure and microhardness were found in sections drilled with drills with large wear lands, particularly in the dry cutting tests. These measurements emphasize the importance of detection of tool wear and ensuring coolant flow in drilling of holes in titanium components.

Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier

Modern machine tools with high speed machining capabilities could place rotating shafts, gears, and bearings under extreme thermal, static, and impact stresses, potentially increasing their failure rates. In this research, a gearbox damage detection strategy based on discrete wavelet transform (DWT), wavelet packet transform (WPT), support vector machine (SVM), and artificial neural networks (ANN) is presented. Three case studies are conducted to compare the classification performance of SVM kernel functions and ANN. First, a fault detection analysis based on DWT and WPT is carried out to extract the damage information from the gearbox’s raw vibration signal. In this step, wavelet coefficients obtained from DWT are characterized using statistical calculations. Energy characteristics of the gearbox signal are acquired using WPT and their statistical characteristics are also computed. These three sets of information extracted from wavelet transforms are utilized as the input to SVM and ANN classifiers. Secondly, the improved distance evaluation technique (IDE) is implemented to select the sensitive input features for SVM and ANN. The penalty parameter C and kernel parameter γ in SVM are also optimized using the grid-search method. Finally, the optimized features and parameters are input into SVM and ANN algorithms to detect gearbox damage. The result shows that gearbox damage detection using energy characteristics extracted from WPT (Case 2) or their statistical values as input features (Case 3) to the learning algorithms produces higher classification accuracies than using statistical values of the DWT coefficients as inputs (Case 1). In addition, RBF-SVM has the best classification performance in Case 2 and 3 while Linear-SVM has the best classification accuracy rate in Case 1 in damage detection average.

Effect of tool wear on quality in drilling of titanium alloy Ti6Al4V, Part I: Cutting Forces, Burr Formation, Surface Quality and Defects

Titanium’s Ti6Al4V, alloy is an important material with a wide range of applications in the aerospace industry. Due to its high strength, machining this material for desired quality at high material removal rate is challenging and may lead to high tool wear rate. As a result, this material may be machined with worn tools and the effects of tool wear on machining quality need to be investigated. In this experimental paper, it is shown how drills of various wear levels affect the cutting forces, surface quality and burr formation. Furthermore, it is shown that high cutting forces and high plastic deformation, along with high temperatures that arise in cutting with worn tools may lead to initiation of microscopic cracks in the workpiece material in proximity of the drilling zone.

Development and Optimization of Vibration-Damped Tool Holders for High Length-to-Diameter Boring Operations

When the length-to-diameter ratio of a boring bar becomes too high, the vibration problem becomes excessive. This paper discusses the design and optimization of tuned mass dampers (TMDs) for vibration-damped boring bars. A typical TMD for a boring bar consists of a high-density mass, rubber elements, and viscous damping fluid. The TMD is mounted as close to the free end of the boring bar as possible for maximizing the damping effect. The different tuning strategies are related to the different types of vibrations that are observed in machining operations. It is shown that the classical tuning with minimized magnitude of the frequency response function will give the shortest settling time for free vibrations, while tuning for maximized negative real part of the frequency response function will give the highest resistance to regenerative chatter. For suppressing regenerative chatters, the internal damping in the material in the boring bar body is of minor importance for boring bars with TMDs, but of high importance for conventional undamped boring bars. The paper presents design solutions for TMDs in boring bars.

Modeling of Position-, Tool- and Workpiece-Dependent Milling Machine Dynamics

Depending on the machine design, milling machines can show a significant variation of their dynamic properties with respect to the axes configurations, in particular at high speed spindle rotations and high feedrates. Moreover, the workpiece and the milling tool are critical parts of the machine tool and can have a major effect on the dynamic properties. Certain combinations of milling tool,workpiece, tool engagement, process parameters and axes configurations can come along with undesired forced or self-excited vibrations. So far, planning of milling processes usually does not account for these unwanted vibrations. The focus of this paper is to present a modeling framework, which accounts for the abovementioned influences via simulation. The dynamic properties of various workpieces and tools as well as the dynamic properties for many different axes configurations are stored in databases. Based on these databases, the dynamics of any given machine tool configuration can be simulated efficiently based on a substructure coupling approach and an interpolation strategy.

In-Process Tool Wear Detection Using Internal Encoder Signals for Unmanned Robust Machining

Automated Tool Condition Monitoring (TCM) often relies on additional sensors sensitive to tool wear in order to achieve robust machining processes. The need of additional sensors could impede the implementation of tool monitoring systems in industry due to the cost and retrofitting difficulties. This paper has investigated the use of existing position encoder signals to monitor a special face turning process with constant feed per revolution and machining speed. A signal processing method by converting encoder signals into a complex-valued form and a new vibration signature extraction method based on phase function were developed to analyze the encoder signals in the frequency domain. The cumulative spectrum indicated that the spectral energy would shift from the lower to the higher frequency band with increasing cutting load. The embedded vibration signatures extracted from the encoder signals provided real-time detectability of the machining condition with distinguishable spectral modes. The embedded vibration signatures extracted from the encoder signals provided additional detectability of the machining condition with distinguishable spectral modes. In particular, tool chipping manifested itself as significant amplitude changes at a specific frequency band 20-30 Hz in the extracted vibration signatures. A new monitoring metric based on the XY-plane modulations combined with statistical process control charts was proposed. This metric was shown to be highly correlated to tool wear and tool wear rate. The results show that when tool chipping occurred, it could be detected when this tool wear rate value jumped in combination with breach of the control limits. This confirms that internal encoder signals, together with the proposed metric, could be an in-process toolwear monitor to help achieving unmanned robust machining.

Enabling Advanced CNC Programming with openNC Controllers for HSM Machines Tools

The manufacturing area has benefited from various progresses over the last decades, such as High Speed Machining (HSM), but CNC programming is still based on dated practices and habits with a segmented unidirectional CAD/CAM/CNC data chain. Decision power is limited at the NC controller stage and online process optimization is difficult. In contrast, advanced programming approaches, such as STEP-NC [9], aim to unify the product/ process data within a consistent environment from design to machining. Hence, openNC controller solutions which allow access to their internal algorithms, have emerged. It is consequently possible to implement new tool path control algorithms that respond directly to the actual machining condition. This paper focuses on the interest of openNC controllers to develop advanced programming approaches forHSMprocesses. Themajor drawbacks of legacy controllers in the implementation of advanced tool path generation methods are discussed and the most significant openNC projects are reviewed. An integrated test platform has been developed. The advanced HSM programming methods enabled by this openNC controller are discussed.

Rapid and High Aspect Ratio Micro-hole Drilling With Multiple Micro-Second Pulses Using a CW Single-Mode Fiber Laser

Laser drilling is an important industrial process for the production of various sizes of holes. In this paper, we investigate rapid, high aspect ratio microhole drilling using multiple microsecond pulses based on the single pulse drilling technique reported in [17, 18]. It was established that there would be a synergistic effect if a subsequent pulse is irradiated at the target within 100 μs of the previous pulse before the melt solidifies. However, the peak power values of subsequent pulses decrease with higher repetition rates. The results show that the synergistic effect could outweigh the reduction in laser power. Another contributing factor of the synergistic effect is related to the melt ejection efficiency. As the hole deepens, the melt ejection becomes less effective to eject the melt completely out of the hole, resulting in a partially blocked hole. A subsequent laser pulse needs to reopen the hole before the hole can be deepened further. To overcome this hole blocking problem, shooting a subsequent pulse at a higher repetition rate also ensures that the energy absorption is more efficient when a subsequent laser pulse is irradiating at the hole blocking melt which is not yet solidified. This multiple-pulse drilling techniquewas applied for through-hole drilling. It was found that the total drilling times through an 800 μm plate were found to be 634 ms and 21.9 ms at 13 kHz and 20 kHz, respectively. The drilling efficiency at the 20 kHz repetition rate is drastically higher, needing only 428 shots, compared with 8240 shots at the 13 kHz, an improvement of nearly 200 times. It is confirmed that this multiple-pulse drilling technique with microsecond pulses using a 300Wsingle mode fiber laser is a viable technique to produce high aspect ratio through holes with a simple and robust setup for the production environment.

Contactless dynamic tests for analyzing effects of speed and temperature on the natural frequency of a machine tool spindle

This paper presents a contactless dynamic test for characterizing several effects on the dynamic stiffness, in particular the first mode frequency, in the radial direction of a rigidly preloaded spindle. The effects of the static load, spindle speed, and spindle outer race temperature on the frequency response function (FRF) are investigated independently in different measurement steps. The first mode frequency was estimated from the measured FRF to evaluate the effects. In the test measurement for a spindle with rigid preload, both thermal and speed effects on the first mode frequency were quantitatively evaluated and a simple prediction model based on the measured data is presented.