Showing papers on "Machining published in 2020"

Review of ultrasonic vibration-assisted machining in advanced materials

Abstract: Compared to conventional machining (CM), ultrasonic vibration-assisted machining (UVAM) with high-frequency and small-amplitude has exhibited good cutting performances for advanced materials. In recent years, advances in ultrasonic generator, ultrasonic transducer, and horn structures have led to the rapid progress in the development of UVAM. Following this trend, numerous new design requirements and theoretical concepts have been proposed and studied successively, however, very few studies have been conducted from a comprehensive perspective. To address this gap in the literature and understanding the development trend of UVAM, a critical overview of UVAM is presented in this study, covering different vibration-assisted machining styles, device architectures, and theoretical analysis. This overview covers the evolution of typical hardware systems used to achieve vibratory motions from the one-dimensional UVAM to three-dimensional UVAM, the discussion of cutting characteristics with periodic separation between the tools and workpiece and the analysis of processing properties. Challenges for UVAM include ultrasonic vibration systems with high power, large amplitude, and high efficiency, as well as theoretical research on the dynamics and cutting characteristics of UVAM. Consequently, based on the current limitations and challenges, device improvement and theoretical breakthrough play a significant role in future research on UVAM.

Evaluation of tool wear, surface roughness/topography and chip morphology when machining of Ni-based alloy 625 under MQL, cryogenic cooling and CryoMQL

Abstract: Although nickel-based aerospace superalloys such as alloy 625 have superior properties including high-tensile and fatigue strength, corrosion resistance and good weldability, etc., its machinability is a difficult task which can be solved with alternative cooling/lubrication strategies. It is also important that these solution methods are sustainable. In order to facilitate the machinability of alloy 625 with sustainable techniques, we investigated the effect of minimum quantity lubrication (MQL), cryogenic cooling with liquid nitrogen (LN2) and hybrid-CryoMQL methods on tool wear behavior, cutting temperature, surface roughness/topography and chip morphology in a turning operation. The experiments were performed at three cutting speeds (50, 75 and 100 m/min), fixed cutting depth (0.5 mm) and feed rate (0.12 mm/rev). As a result, CryoMQL improved surface roughness (1.42 µm) by 24.82% compared to cryogenic cooling. The medium level of cutting speed (75 m/min) can be preferred for the lowest roughness value and lowest peak-to-valley height when turning of alloy 625. Further, tool wear is decreased by 50.67% and 79.60% by the use of MQL and CryoMQL compared with cryogenic machining. An interesting result that MQL is more effective than cryogenic machining in reducing cutting tool wear.

Robotic grinding of complex components: A step towards efficient and intelligent machining – challenges, solutions, and applications

Abstract: Robotic grinding is considered as an alternative towards the efficient and intelligent machining of complex components by virtue of its flexibility, intelligence and cost efficiency, particularly in comparison with the current mainstream manufacturing modes. The advances in robotic grinding during the past one to two decades present two extremes: one aims to solve the problem of precision machining of small-scale complex surfaces, the other emphasizes on the efficient machining of large-scale complex structures. To achieve efficient and intelligent grinding of these two different types of complex components, researchers have attempted to conquer key technologies and develop relevant machining system. The aim of this paper is to present a systematic, critical, and comprehensively review of all aspects of robotic grinding of complex components, especially focusing on three research objectives. For the first research objective, the problems and challenges arising out of robotic grinding of complex components are identified from three aspects of accuracy control, compliance control and cooperative control, and their impact on the machined workpiece geometrical accuracy, surface integrity and machining efficiency are also identified. For the second aim of this review, the relevant research work in the field of robotic grinding till the date are organized, and the various strategies and alternative solutions to overcome the challenges are provided. The research perspectives are concentrated primarily on the high-precision online measurement, grinding allowance control, constant contact force control, and surface integrity from robotic grinding, thereby potentially constructing the integration of “measurement – manipulation – machining” for the robotic grinding system. For the third objective, typical applications of this research work to implement successful robotic grinding of turbine blades and large-scale complex structures are discussed. Some research interests for future work to promote robotic grinding of complex components towards more intelligent and efficient in practical applications are also suggested.

Review of tool condition monitoring in machining and opportunities for deep learning

Abstract: Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.

The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2

Abstract: Today, developments in technology have gained momentum more than ever, and the need for efficiency in production as well as in the ecological domain has increased significantly. Studies examining dry machining and coolant removal have been superseded by those presenting new cooling and lubrication techniques. The effects on surface roughness directly related to final product quality are being investigated in terms of tool life and employee health. This has resulted in more frequent use of the eco-friendly minimum quantity lubrication (MQL) technique, which has now become a major competitor to dry and coolant machining. In this study, AISI D2 cold work tool steel, a material widely used in the mold industry, was used as the workpiece. Tests were carried out under dry and MQL conditions and the temperature, cutting tool vibration amplitude, tool wear, surface roughness and tool life were evaluated. The experiments were carried out using two different cutting tool coating types (CVD-chemical vapor deposition and PVD-physical vapor deposition) and three different cutting speeds (60, 90 and 120 m/min) at a constant cutting depth (1 mm) and feed rate (0.09 mm/rev). Results revealed that tool wear, cutting temperature and cutting tool vibration amplitude were lower by 23, 25, and 45%, respectively, compared to dry cutting. Because of these improvements, the surface roughness of the workpiece was improved by 89% and tool life was increased by up to 267%.

Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

Abstract: Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.

Abstract: The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

Real-time machining data application and service based on IMT digital twin

Abstract: With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed.

Delamination formation and suppression during rotary ultrasonic elliptical machining of CFRP

Abstract: As a superior hole manufacturing process, rotary ultrasonic elliptical machining (RUEM) has been successfully employed for drilling carbon fiber reinforced plastics (CFRPs) recently. The delamination of CFRP during drilling is generally considered as the most undesirable form of damage and the most challenging failure mode. In order to reduce the delamination in this novel process, it is important to understand the formation and suppression mechanisms during RUEM of CFRP. In this study, the delamination formation in both core drilling (CD) and RUEM was observed and analyzed. The variation trend of delamination factor with feed speed as well as cutting speed was obtained. The experimental results show that, compared with CD, RUEM method can effectively reduce hole exit delamination by 5.4%–19.3% between 1/2 plies and 0.7%–8.4% between 2/3 plies at the feed rates from 50 to 100 μm/rev. Moreover, the delamination suppression mechanism in RUEM was fully analyzed and verified. In a word, in the industry practice, RUEM can be considered as a competitive and promising technique to drill CFRP compared to other delamination suppression techniques.

CO2 cryogenic milling of Inconel 718: cutting forces and tool wear

Abstract: Machining Inconel 718 alloy is a challenge due to its low machinability. This thermal resistant alloy combines high strength even at high temperatures with strain hardening tendency that causes high forces and extreme cutting temperatures during the machining. These issues force industries to achieve suitable machining processes to deal with this kind of alloys and the high worldwide competitiveness. Nevertheless, environmental considerations must to be taken into account due to growing environmental concerns. In the work here presented, cryogenic cooling with external MQL lubrication (CryoMQL) working along with CO2 as internal coolant is proposed for milling Inconel 718 with the aim of not only improving from a technical point of view but also environmental. This technique was compared with other lubricooling techniques. The results show that internal CryoMQL improves tool life by 57% in comparison with emulsion coolant, achieving 120% if it is compared with MQL in stand-alone mode.

Damage behaviors of unidirectional CFRP in orthogonal cutting: A comparison between single- and multiple-pass strategies

Abstract: Carbon Fibre Reinforced Plastics/Polymer (CFRP) composites has experienced a rapid revolution, requiring the accurately controllable machining technology. Although efforts have been paid on CFRP machining, most of them focused on the single-pass orthogonal cutting where the perfect unprocessed surfaces were employed as the initial state. The reality is however the multiple-pass cutting with the progressive cut depths has been widely used in the industries, where the influence of the defects generated in the previous passes on the following cuts can not be ignored. To fill this gap, this paper investigated the damage behaviors of unidirectional CFRP in orthogonal cutting with the special emphasis on the difference between the single- and the multiple-pass strategies. The good agreement were found between the experimental and simulation results, where the maximal relative errors were separately 10.1%, 9.2%, and 8% for fibre pull-out depth, fibre-matrix debonding depth and cutting forces. Further discussion based on the model can draw the conclusion that, the employment of the multiple-pass cutting strategy can improve the fibre breakage length by 40%, the fibre pull-out depth by 63%, and the fibre-matrix interface debonding by 25%. This work is anticipated to not only open a new avenue to provoke more in-depth thoughts of CFPR behaviors in cutting but also to provide the practical guidance for industrial CFRP high-quality machining.

Modelling the dynamics of industrial robots for milling operations

Abstract: Using industrial robots as machine tools is targeted by many industries for their lower cost and larger workspace. Nevertheless, performance of industrial robots is limited due to their mechanical structure involving rotational joints with a lower stiffness. As a consequence, vibration instabilities, known as chatter, are more likely to appear in industrial robots than in conventional machine tools. Commonly, chatter is avoided by using stability lobe diagrams to determine the stable combinations of axial depth of cut and spindle speed. Although the computation of stability lobes in conventional machine tools is a well-studied subject, developing them in robotic milling is challenging because of the lack of accurate multi-body dynamics models involving joint compliance able of predicting the posture-dependent dynamics of the robot. In this paper, two multi-body dynamics models of articulated industrial robots suitable for machining applications are presented. The link and rotor inertias along with the joint stiffness and damping parameters of the developed models are identified using a combination of multiple-input multiple-output identification approach, computer-aided design model of the robot, and experimental modal analysis. The performance of the developed models in predicting posture-dependent dynamics of a KUKA KR90 R3100 robotic arm is studied experimentally.

In-process tool condition forecasting based on a deep learning method

Abstract: It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.

Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites

Abstract: This article presents an experimental investigation to assess the influence of input process parameters of machinability of wire electrical discharge machining (WEDM) process for machining of tripl...

Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

Abstract: AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (Ra) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as Vc = 190 m/min, f = 0.06 mm/rev, κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.