Complicated Machining Research Articles

Digital twin-based anomaly detection for real-time tool condition monitoring in machining

Real-time tool condition monitoring (TCM) has been emerging as a key technology for smart manufacturing. TCM can improve the dimensional accuracy of products, minimize machine tool downtime, and eliminate scraps and re-work costs. Digital twins offer new opportunities for real-time monitoring of machining processes, which can, in principle, take into account changes in machining processes and operating environments, help understand mechanisms of cutting tool wear, and improve the anomaly detection accuracy and fault diagnosis results. The present study exploits these potential advantages of digital twins and proposes a new digital twin-based anomaly detection framework for real-time TCM in machining. The framework of the digital twin consists of three parts: the physical product, the virtual product and data flow connections. Within this framework of the digital twin, the “physical product” represents the machining processes. The “virtual product” includes a real-time data-driven model representing the dynamic relationship between vibration data measured from machining processes as well as the model frequency features (MFFs)-based diagnostics for cutting tool anomaly detection. The “data flow connections” involve real-time measured vibration data and machine tool numerical controller (NC) signals providing real-time information on machine tool dynamics and various machining processes. The novelty is associated with an innovative integration of real-time data-driven modeling, MFFs extraction, and MFFs and machine tool NC signal-based tool wear diagnostics. This, for the first time, enables the concept of digital twins to be potentially applied to the TCM for complicated dynamic machining processes which, as far as we are aware of, has never been achieved before. Comprehensive field studies have demonstrated the effectiveness of the proposed digital twin-based TCM framework and its potential industrial applications.

Undeformed chip thickness with composite ultrasonic vibration-assisted face grinding of silicon carbide: Modeling, computation and analysis

In order to achieve high efficiency and low damage processing of hard and brittle materials, composite ultrasonic vibration-assisted face grinding (CUVAFG) has been proposed by comprehensively combining the high efficiency of axial ultrasonic vibration grinding (UVAG) and the low damage of elliptic ultrasonic vibration grinding (EVAG). However, the three-dimensional ultrasonic vibration brings about a more complicated machining mechanism than the previous ultrasonic vibration-assisted grinding methodology. Hence, in order to explore the grinding mechanism in deep, this paper first developed a new model of undeformed chip thickness for CUVAFG from the perspectives of the kinematic mechanism and elastic-plastic deformation mechanism. Then the influences of different processing parameters on interference rate and undeformed chip thickness were further confirmed. Finally, CUVAFG experiments of silicon carbide (SiC) ceramics were conducted to study the relationship between the undeformed chip thickness and the resultant grinding forces, specific grinding energy, ground surface morphology and roughness, and subsurface damage. It is found from the experimental results that the critical chip thickness of brittle-ductile transition in CUVAFG is between 0.31 and 0.35 μm. As the undeformed chip thickness is less than the critical value, the grinding forces and the specific grinding energy increases with the undeformed chip thickness, but the ground surface roughness decreases with improved surface morphology and shallow subsurface damage. After exceeding the critical value, the grinding force remains unchanged, the specific grinding energy decreases, and ground surface roughness increases with deteriorated surface morphology and deepened subsurface damage. For the purpose of good machining surface integrity, it is essential to guarantee that the undeformed chip thickness is less than the critical value. The computational and experimental results have demonstrated the contribution of the developed undeformed chip thickness model to understanding the grinding performance of hard and brittle material by CUVAFG.

Combination of Dynamical Movement Primitives With Trajectory Segmentation and Node Mapping for Robot Machining Motion Learning

Movement primitive (MP) is proposed to model the robot skills with the idea of reusing basic behavior units. By learning the demonstrations, the MP can be modeled and utilized to generate similar motions to adapt to slightly changed scenarios. However, many kinds of MP models do not consider the geometric shape constraints and the trajectory execution accuracy, which will bring the shape distortion and contour error when task generalization. Traditional MP models are mainly studied for imitating human-like operating skills and have not been applied in industrial scenarios such as robot machining. In this article, a framework based on the combination of dynamical movement primitives (CDMP) with trajectory segmentation and node mapping methods is proposed to realize the imitation of robot machining motion. The demonstration machining trajectory is segmented first according to the kinematic features, and the shape features are extracted and stored by the node mapping policy. Then, the CDMP model is built for describing a complicated machining motion with many component DMPs. The parameters preferences associated with the model performance of CDMP are analyzed in the simulation. At last, a robot grinding experiment is conducted to show that the proposed framework can successfully imitate the machining motion with low geometric distortion, contour error, and computation cost, which also obtain a good surface quality and can significantly improve the intelligence level of robot machining.

Reduction of the mechanical anisotropy in hot-deformed Nd-Fe-B magnets by the construction of heterogeneous structure

Conventional hot-deformed Nd-Fe-B magnets exhibit significant mechanical anisotropy in the two directions perpendicular and parallel to c-axis, resulting in a complicated machining process. In this work, hydrogenation-disproportionation-desorption-recombination (HDDR) powders were added to reduce the mechanical anisotropy of hot-deformed magnets. The effects of the addition of HDDR powders on the compressive strength of hot-deformed magnets and their corresponding microstructures have been investigated. The mechanical anisotropy was maximally decreased by 68 % at 20 wt. % addition. Microstructure observation indicated that a heterogeneous structure containing two kinds of grains with different morphology was formed after the addition of HDDR powders. Further analyses revealed that the reduction of mechanical anisotropy was mainly attributed to the change of the shape anisotropy of grains and the disruption of the regular stacking structure of the melt-spun ribbons. This work provides a new insight for the preparation of high performance hot-deformed Nd-Fe-B magnet with outstanding comprehensive service capability.

Reliability Characteristics of Machining Systems and Some Queueing Theory Regarding the Machine Repair Problem

Abstract: The article delves into the elements of machine repairable systems that contribute to their dependability, as well as certain parts of queueing theory. The use of reliability theory, which is both a mathematical and physical discipline, has aided in our understanding of the laws that control the incidence of failures in machining systems. Life testing, structural reliability, machine maintenance concerns, and the replacement problem are all subfields of reliability research. When dealing with bottlenecks and delays in complicated machining systems, the idea of queueing is often a useful tool. People wait in lines or line ups in many real-world situations, including at traffic lights, phone assistance lines, petrol stations, airports, hospitals, via communication channels, in computer systems, etc. One important subset of queues is made up of queues with finite source populations. Some of the numerous domains that might profit from this class include production, power generation, manufacturing techniques, information and communication technology, and distribution hubs.

Spiral Groove Machining Through Wire Electrical Discharge Machining with Two Rotary Axes

In wire electrical discharge machining (WEDM) that can perform 2-D or 2.5-D machining, 3-D complex shape machining is also possible via the addition of a rotary axis on the NC table. Several examples of pin-shaped machining using a rotating shaft like spindle have been previously reported. Alternatively, machining using a rotary axis as an indexing device has also been reported. In these machining processes, the rotary axis is not servo controlled. Conversely, a spiral groove is formed on the outer circumference of the round bar by gripping the round bar workpiece on the rotary axis and performing machining in synchronization with the x- and rotary axes. In this machining, the gap control in electrical discharge machining is performed along the x- and rotary axes. Furthermore, complicated shape machining becomes possible by adding a 2-axis rotary axis of rotation and tilt. When the x-axis is synchronized with the rotation and tilt axes, a spiral groove with a variable groove width is formed. In this case, servo control is synchronized with the three axes, and machining proceeds. In this study, we performed spiral groove shape machining through WEDM with the addition of 1-axis or 2-axis rotary axes, consequently verifying the machining accuracy. Moreover, two types of NC program were used for machining, direct input and CAM output, and the accuracy was compared. The results revealed that the groove width was wider in the direct input program. Therefore, there was a possibility that the wire could bend during machining and tilt along the direction of the apparent widening of the groove width. Thus, it is necessary to consider the deflection of the wire in WEDM with a rotary axis, which is different from the conventional one, to realize precision machining.

Measurement method of aero-engine rotor concentricity and perpendicularity based on deep belief neural network.

When implementing the traditional assembly method, the rotor is affected by machining errors. The morphology of the rotor is complex, and the machining error of the rotors at all levels are transmitted step by step through the stop mating surface, which affects the performance and service life of the aero-engine. The evaluation of machining error of single-stage rotor is the basis of assembly quality of multi-stage rotor. In order to improve the current situation of complicated and time-consuming rotor machining error evaluation, this paper proposes to establish a deep belief neural network (DBNN) to replace the traditional procedure of depolarization. The network takes the relative evaluation error of the rotor profile data without depolarization as the input and takes the machining error of the rotors obtained after depolarization as the output. First, the evaluation mechanism of the rotor's machining error is analyzed, and the corresponding machining error influence source is selected as the input source of the deep belief neural network. Second, as DBNN is trained, and the appropriate weight initialization method and the optimization algorithm of the prediction network are selected to ensure the optimization of the whole network for feature mapping extraction of the training set. Finally, the assembly of multi-stage rotors is simulated and analyzed. It is shown in the experiments that after the iteration, the prediction network, with good training effects, has converged, and its prediction results tend to be consistent with the real values. The mean prediction error of the concentricity is 0.09 µm while the mean difference of angle of concentricity error value is 0.77°, and the mean difference of perpendicularity error value is 0.21 µm while the mean difference of angle of perpendicularity error value is 1.4°, the corresponding R2 determination coefficients were 0.99, 0.98, 0.91, and 0.94, respectively. It meets the requirements of field assembly and fully embodies the effectiveness of the procedure of depolarization based on deep confidence neural network.

オープンソース3Dモデラーを用いたトポロジー最適化を組み込んだCAE/CAM教材の開発

In recent years, 3D printers have become popular in universities and technical colleges. The advantage of 3D printers is the performance to manufacture the complex shapes. However, students often use it as a substitute for complicated manual machining because they do not have their machining skills. It is important to make young students aware of the importance and value of additive manufacturing, which is a new machining method such as 3D printers. Incidentally, the topology optimization attracts attention in study of the structure shape optimization. Moreover the topology optimization have a high affinity with 3D printers. In this study, I developed teaching materials for implementing CAE/CAM training including topology optimization in machine practice subject at department of mechanical engineering in Kobe City College of Technology, using open source 3D modeler.

Effect of a Single Particle in Abrasive Waterjet Machining on 42CrMo4 Modifications

Waterjet machining is a powerful tool for processing of a variety of materials from ductile to brittle ones. However, it is a complicated machining process to control and predict its results on the workpiece due to the interaction of high dynamic abrasive particles with the workpiece surface and with each other simultaneously. A proper approach to decrease this complication is to investigate the effect of a single particle on the workpiece surface instead of multiple particles with their complex interactions. Therefore, the aim of this study is to understand the mechanism of abrasive waterjet machining by considering the effect of single particles of different abrasives with different attacking angles on two different microstructural conditions of a steel alloy. Here, 42CrMo4 is selected as the workpiece material for its ability to have different modifications from ductile to brittle characteristics. First, the effect of process parameters on the impact characteristics of the waterjet is studied. In the next step, the effect of particle velocity on the workpiece material modification is investigated. Furthermore, the effect of impact angle on the material modification and shape of the generated footprints is explored. Moreover, the effect of kinetic and kinematic properties of abrasive particle on burr formation in the ductile modification of 42CrMo4 is investigated.

Design and Optimization of a 6-DOF Singularity-Free Parallel Manipulator

A novel six degrees-of-freedom (DOF) singularity-free parallel manipulator (PM) is presented. The PM consists of five peripheral limbs and a centre limb, which makes the mechanism have high stiffness and large tilting capability . Due to the special architecture, the doubly actuated centre limb of the manipulator could have infinite inverse solutions. In every configuration of the end-effector, the manipulator can adapt its centre limb to the position and orientation with best dexterities. Targeting for further analysis of the manipulator, its detailed kinematic analysis is developed, including the solutions of the inverse position problems, the singularity, the dexterity and the workspace. The analysis results show that the proposed manipulator has good performance and thus may be suitable candidate for complicated surface machining.

Theoretical analysis and performance prediction on modified surface layer caused by ultrasonic surface rolling

Ultrasonic surface rolling process (USRP) is a prospective approach that can improve fatigue property of a workpiece by improving compressive residual stress and microhardness in the modified surface layer, as well as improving surface roughness. However, because USRP is a complicated machining process, many studies on surface properties focus on experiment test and simulation method. Thus, analytical models of the modified surface layer should be established to facilitate the design and utility of the USRP. In the present work, analytical models of residual stress, plastic strain of modified surface layer, and surface roughness are proposed on the basis of Hertz contact theory and elastic–plastic mechanics, and the effects of processing parameters on the modified surface layer are predicted using the developed models. Results of the analytical models are consistent with experiment results. Therefore, the models can be used to guide the design of the modified surface layer and identify optimal processing parameters in USRP.

Ultrawideband Characterization of Complex Dielectric Constant of Planar Materials for 5G Applications

A broadband (1–35 GHz) time-/frequency-domain reflection technique for measuring the complex dielectric constant of low-loss dielectric slabs is presented. The technique uses a coplanar waveguide (CPW) transmission line sample holder for measurements. In addition to the standard substrate materials, the technique is suitable for the characterization of materials, such as glass, as it does not require complicated sample machining and preparation. The procedure is based on comparing the measured effective dielectric constant and the attenuation constants for both the loaded and unloaded CPW line sample holder. The effective dielectric constant and the attenuation constant are first modeled as a function of the real and imaginary parts of the superstrate’s dielectric constant using a 2-D numerical line calculator. Samples under test are assumed to be nonmagnetic and have their material resonances at frequencies substantially above 35 GHz. The technique is validated through measurements of known Rogers RT/duroid samples. Measured complex dielectric constants of different materials are in very good agreement with data reported in materials’ datasheets.

An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

Electrical discharge machining (EDM) can effectively solve the shortcomings of traditional machining processes that cannot process special materials, so it is widely used on workpieces with strong hardness materials, such as titanium alloys and tool steels to produce various molds and dies. However, the operating procedures of EDM are quite complicated and low machining productivity. To improve machining efficiency, this study develops an intelligent system that adaptively controls debris removal operations instead of using preset debris removal parameters. A feature extraction method is proposed in this study to effectively identify the machining states from streaming images of the machining curve for evaluating the appropriate time of the debris removal operation. Then, the extracted features feed into the artificial neural network model to establish a debris removal predicted model. The preliminary experimental result shows that the established predicted model can achieve an accuracy of 96.93% for a testing dataset containing 750 machining curve images. To further verify the effectiveness of the proposed intelligent system in improving EDM efficiency, we integrate the debris removal predicted model into the EDM machine and test it on the manufacturing site. Compared with the preset debris removal parameter, the proposed intelligent system can save nearly 38.60% of machining time for the machining depth of 6.45mm under specific EDM conditions.

Grinding Wheel Loading Evaluation by Using Acoustic Emission Signals and Digital Image Processing.

In the manufacturing industry, grinding is used as a major process for machining difficult-to-cut materials. Grinding is the most complicated and precise machining process. For grinding machines, continuous generating gear grinding machines are widely used to machine gears which are essential machine elements. However, due to its complicated process, it is very difficult to design a reliable measurement method to identify the grinding wheel loading phenomena during the grinding process. Therefore, this paper proposes a measurement method to identify the grinding wheel loading phenomenon in the grinding process for continuous generating gear grinding machines. In the proposed approach, an acoustic emission (AE) sensor was embedded to monitor the grinding wheel conditions; an offline digital image processing technique was used to determine the loading areas over the surface of Al2O3 grinding wheels; and surface roughness of the ground workpiece was measured to quantify its machining quality. Then these three data were analyzed to find their correlation. The experimental results have shown that there are two stages of grinding in the grinding process and the proposed measurement method can provide a quantitative grinding wheel loading evaluation from the AE signals online.

Design of a spiral bevel gear acc. to ISO 23509:2006 standards

When the transmitting of power and torque between parts of a machine requires high efficiency, the most appropriate type of gears are the bevel gears. The two main forms of bevel gears are straight and spiral bevel gears. Manufacturing of bevel gears has been implemented using dedicated bevel gear cutting machines. These machines make the production of this type of gears quite costly. This is the main reason that forced many manufacturers start using CNC milling machines for cutting bevel gears. A combination of a 5-axis milling center machine and a modern computer aided manufacturing (CAM) system that supports a vast number of complicated machining procedures, offers the opportunity of machining high quality bevel gears. This paper concerns the presentation of a program written in MATLAB that calculates the basic designing features of a bevel gear pair, as they described by ISO standard. It is also presented the calculated tooth profiling that is used in order to be designed a 3-D model of the gears in a CAD software.

Development of a CAPP system for multi-tasking machine tools to deal with complicated machining operations

Development of a CAPP system for multi-tasking machine tools to deal with complicated machining operations

Chip formation and hole quality in dry drilling additive manufactured Ti6Al4V

ABSTRACTDry drilling known as a sustainable and complicated machining operation is usually involved in many applications of titanium alloys. Previous studies have demonstrated that different surface qualities of the drilled holes influence the service performance and the final fatigue limit of the parts. However, a comprehensive understanding of their origins in terms of the generated chips and thereby microstructures of hole surfaces is not widely reported but is of great importance. To this end, this work addresses the typical characteristics of produced chips and drilled holes induced by the drilling operation with a macro-micro combination view. A particular focus was put on the chip morphology and chip evolution and surface integrity of drilled holes with the commonly used standard twist drill during dry drilling process. The results show that the chips show different morphologies with the advancement of drill bit and continuous chips were obtained regardless of the cutting variables. As the drill progressed, chip morphology was transferred from spiral cone to folded ribbon. Some scratch and smearing traces were formed on the hole surfaces due to the tangled chips on the drill bit. Furthermore, the grains in the hole wall vicinity present different morphologies when observing the cross-section of drilled holes and a nanocrystalline structure appears close to the drilled hole surface.

Intelligent pulse analysis of high-speed electrical discharge machining using different RNNs

High-speed electrical discharge machining (EDM) is a nontraditional machining method using high electric energy to efficiently remove materials. In this paper, a novel pulse classification method was proposed based on the recurrent neural network (RNN) for high-speed EDM pulse analysis. This study is the first time that an RNN has been applied in high-speed EDM pulse analysis. Different from traditional EDM, discharge pulses of high-speed EDM were classified into five types during the machining process: open, spark, arc, partially short and short. Models based on three different RNNs including the traditional RNN, LSTM (long short-term memory) and IndRNN (independently recurrent neural network) with different activation functions were built to analyze the discharge pulses in the research. A new input data structure based on the minimum signal change period was proposed in the classification method to simplify the model structure and improve accuracy at the same time. Without setting thresholds, the highest classification accuracy of the proposed model is up to 97.85%, which can simultaneously classify discharge pulses based on 10,000 orders of magnitude including various current values. The proposed method was effectively adapted to the complicated machining conditions and the compound power source of the high-speed EDM. The optimal model was used to analyze the distribution of discharge pulses during the machining process under different currents, fluxes and feeding speeds. The proportion of the discharge pulses was clearly predicted. Through analyzing the discharge pulses of long machining time, the regulation of discharge under different machining parameters was revealed more reliably, providing valuable information for the improvement of high-speed EDM servo systems.

In-process complex machining condition monitoring based on deep forest and process information fusion

Abnormal machining condition causes losses of quality for finished part. A machining condition monitoring system is considerably vital in the intelligent manufacturing process. Existing machining condition monitoring methods usually detect only one single abnormal condition under the same machining process, which is unrealistic and impractical for real complicated machining process. In this paper, a novel hybrid condition monitoring approach for multiple abnormal conditions’ detection of complicated machining process by using deep forest and multi-process information fusion is proposed. First, various process data are obtained from a triaxial accelerometer and a sound sensor mounted on the spindle of CNC. Then, the time domain, frequency domain, and time-frequency domain features extracted from the multiple sensory signals are simultaneously optimized to select a subset with key features by the lasso technique. Furthermore, deep forest is utilized as a condition classifier by using the selected features. Finally, cutting experiments are designed and conducted, and the results show that the proposed method can effectively detect the multiple abnormal conditions under the different machining parameters.

Development of superhydrophilic Al foil with micropore arrays via mask electrochemical machining and chemical immersion for efficient oil/water separation

Extreme wettability materials, which can enable high-efficiency oil/water separation, has become a widely discussed topic due to increasingly severe oil pollutions including frequent offshore oil-spills accidents and industrial oily wastewater emission. However, most of existing methods to prepare these materials involved complicated machining processes or dangerous operations, especially the use of corrosive or toxic chemicals. Herein, a novel and environment friendly method including mark electrochemical machining (MECM) and subsequent chemical immersion, was proposed to acquire micropore arrays on Al foil for efficient oil collection from oil/water mixtures. Micro-dimple arrays were generally fabricated by MECM process followed by CuCl2 immersion to make it through. Both simulation and experimental verification were conducted to elucidate how dimple structures varied with machining parameters. The micro-scale rectangular-shaped step-like structures on micropore arrays endowed as-prepared samples with excellent superhydrophibic and underwater superoleophobic properties. Moreover, oils with kinematic viscosities ranging from 0.41 to 74.4 cSt could be separated from water with separation efficiencies above 95%. The superhydrophilic Al foil still showed good durability through repeated oil/water separation tests, and retained its superhydrophilicity even after the storage in water for 120 hours. Therefore, this novel and green method gives researchers new insights into functional materials preparation for practical oil/water separation.