Showing papers on "Machining published in 2018"

Processing Characteristics of Vegetable Oil-based Nanofluid MQL for Grinding Different Workpiece Materials

Abstract: Minimum quantity lubrication (MQL) is an efficient, green, and eco-friendly method of applying cutting fluids in machining processes. This study presents the processing characteristics of different vegetable oil-based nanofluid MQL for grinding various workpiece materials. The performance of three lubricant types (i.e., pure palm oil, MoS2 nanofluid, and Al2O3 nanofluid) of good lubrication performance and three types of materials (i.e., Inconel 718, ductile cast iron, and AISI 1045 steel) was evaluated in terms of force ratio, specific grinding energy, and G ratio. The optimal processing combination of lubricants and workpiece materials under the same experimental conditions was obtained using orthogonal experiment. Optimization results were verified by evaluating the morphology of the workpiece surface and grinding debris. Experimental results show the different processing characteristics of materials when various workpieces are processed using dissimilar MQL lubricants. MoS2 nanofluid MQL is suitable for machining soft medium carbon steels, such as 45 steel, while Al2O3 nanofluid is suitable for machining materials of high strength and hardness, such as nickel-based alloys.

Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

Abstract: The Fourth Industrial Revolution incorporates the digital revolution into the physical world, creating a new direction in a number of fields, including artificial intelligence, quantum computing, nanotechnology, biotechnology, robotics, 3D printing, autonomous vehicles, and the Internet of Things. The artificial intelligence field has encountered a turning point mainly due to advancements in machine learning, which allows machines to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and productivity rates, to monitor the health of systems, and to optimize design and process parameters. This is known as smart machining, referring to a new machining paradigm in which machine tools are fully connected through a cyber-physical system. This paper reviews and summarizes machining processes using machine learning algorithms and suggests a perspective on the machining industry.

Effects of nano-cutting fluids on tool performance and chip morphology during machining Inconel 718

Abstract: Flood cooling is a typical cooling strategy used in industry to dissipate the high temperature generated during machining of Inconel 718. The use of flood coolant has risen environmental and health concerns which call for different alternatives. Minimum quaintly lubricant (MQL) has been successfully introduced as an acceptable coolant strategy; however, its potential to dissipate heat is much lower than the one achieved using flood coolant. MQL-nano-cutting fluid is one of the suggested techniques to further improve the performance of MQL particularly when machining difficult-to-cut materials. The main objective of this study is to investigate the effects of two types of nano-cutting fluids on tool performance and chip morphology during turning of Inconel 718. Multi-walled carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3) gamma nanoparticles have been utilized as nano-additives. The novelty here lies on enhancing the MQL heat capacity using different nano-additives-based fluids in order to improve Inconel 718 machinability. In this investigation, both nano-fluids showed better results compared to the tests performed without any nano-additives. Significant changes in modes of tool wear and improvement in the intensity of wear progression have been observed when using nano-fluids. Also, the collected chips have been analyzed to understand the effects of adding nano-additives on the chip morphology. Finally, it has been found that MWCNT nano-fluid has shown better performance than Al2O3 nano-fluid.

Pulsed Nd:YAG laser beam drilling: A review

Abstract: Laser beam drilling (LBD) is one of non contact type unconventional machining process that are employed in machining of stiff and high-strength materials, high strength temperature resistance materials such as; metal alloys, ceramics, composites and superalloys. Most of these materials are difficult-to-machine by using conventional machining methods. Also, the complex and precise holes may not be obtained by using the conventional machining processes which may be obtained by using unconventional machining processes. The laser beam drilling in one of the most important unconventional machining process that may be used for the machining of these materials with satisfactorily. In this paper, the attention is focused on the experimental and theoretical investigations on the pulsed Nd:YAG laser drilling of different categories of materials such as ferrous materials, non-ferrous materials, superalloys, composites and Ceramics. Moreover, the review has been emphasized by the use of pulsed Nd:YAG laser drilling of different materials in order to enhance productivity of this process without adverse effects on the drilled holes quality characteristics. Finally, the review is concluded with the possible scope in the area of pulsed Nd:YAG laser drilling. This review work may be very useful to the subsequent researchers in order to give an insight in the area of pulsed Nd:YAG laser drilling of different materials and research gaps available in this area.

FeatureNet: Machining feature recognition based on 3D Convolution Neural Network

Abstract: Automated machining feature recognition, a sub-discipline of solid modeling, has been an active research area for last three decades and is a critical component in digital manufacturing thread for detecting manufacturing information from computer aided design (CAD) models. In this paper, a novel framework using Deep 3D Convolutional Neural Networks (3D-CNNs) termed FeatureNet to learn machining features from CAD models of mechanical parts is presented. FeatureNet learns the distribution of complex manufacturing feature shapes across a large 3D model dataset and discovers distinguishing features that help in recognition process automatically. To train FeatureNet, a large-scale mechanical part datasets of 3D CAD models with labeled machining features is automatically constructed. The proposed framework can recognize manufacturing features from the low-level geometric data such as voxels with a very high accuracy. The developed framework can also recognize planar intersecting features in the 3D CAD models. Extensive numerical experiments show that FeatureNet enables significant improvements over the state-of-the-arts manufacturing feature detection techniques. The developed data-driven framework can easily be extended to identify a large variety of machining features leading to a sound foundation for real-time computer aided process planning (CAPP) systems.

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Abstract: Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, $$V_{B}$$ into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.

Cryogenic machining of carbon fiber reinforced plastic (CFRP) composites and the effects of cryogenic treatment on tensile properties: A comparative study

Abstract: Carbon fiber reinforced plastics (CFRPs) are prone to damage locally during machining due to the applied cutting forced and generated heat. Cryogenic machining can reduce the heat generated damages of CFRPs by utilizing cryogenic liquids instead of conventional cutting fluids. The goal of this study is to investigate milling performance of CFRPs in cryogenic medium. For this, a new cryogenic machining approach was adopted to slot milling of CFRPs by submerging the workpiece within a cryogenic liquid. The CFRPs were fabricated via vacuum assisted resin transfer method by using woven carbon fiber fabric as a reinforcement and epoxy as a matrix. Machining performance was evaluated based on the resulting cutting force, delamination factor, surface roughness, and surface damage. Moreover, the influences of cryogenic coolant on the tensile properties, fracture surface microstructure, and machined surface of the CFRP laminates were analyzed with scanning electron microscopy (SEM). SEM analysis revealed that combination of different damage modes such as debonding, micro matrix crack, fiber pull out, and bundle pull out, delamination, and fiber breakage were observed. The results showed that cryogenic machining approach provided less damage formation on the machined surface, reduced delamination factor and surface roughness but increased resulting cutting force during machining of the CFRPs. On the other hand, there was a slight improvement (about 3%) of the tensile properties for the CFRPs exposed to cryogenic coolant due to matrix hardening and increasing in the fiber strength and shear strength.

Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process

Abstract: Process monitoring is necessary in machining operation to increase productivity, improve surface quality, and reduce unscheduled downtime. Tool wear and breakage are important and common source of machining problems due to high temperatures and forces of the machining process. Therefore, it is highly beneficial to develop an online tool condition monitoring (TCM) system. This paper investigates a robust tool wear monitoring system for milling operation. Recent developments in machine learning, in particular deep learning methods, result in significant improvement in automation of different industries. Therefore, in this research, we employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation. Wavelet packet-based features are extracted for tool wear monitoring as a powerful time-frequency fault indicator. Moreover, a hybrid feature extraction method is proposed using wavelet time-frequency transformation and spectral subtraction algorithms to intensify the effect of tool wear in the signal and reduce the effect of other cutting parameters. CNN-based monitoring systems are compared with three other machine learning methods (support vector machine, Bayesian rigid network, and K nearest neighbor method) as the baseline. The research is validated using different datasets. The algorithms are implemented and compared using experimental force and vibration signals from LIPPS lab of ETS university as well as using current signals as the fault indicator from Nasa_Ames dataset.

Performance evaluation of Ti–6Al–4V machining using nano-cutting fluids under minimum quantity lubrication

Abstract: Owing to their superior mechanical, physical, and chemical characteristics, titanium and its alloys are broadly used in different industrial applications such as military, aerospace, power generation, and automotive. However, titanium alloys are inherently difficult to cut materials due to the high generated temperature during machining. In addition to flood cooling, several other techniques were employed to reduce the harmful effect and the generated temperature and generally improve titanium alloys machinability. In this paper, an attempt is made to utilize nano-additives to improve the cooling efficiency of minimum quantity lubrication (MQL) during machining titanium alloys. The main objective of the current research is to investigate the influence of dispersed multi-walled carbon nanotubes (MWCNTs) into vegetable oil by implementing the MQL technique during turning of Ti–6Al–4V. The novelty here lies on enhancing the MQL heat capacity using different concentrations of nano-fluid in order to improve Ti–6Al–4V machinability. Different cutting tests were performed and relevant data were collected. The studied design variables were cutting speed, feed rate, and percentage of added nano-additives (wt%). It was found that 2 wt% MWCNT nano-fluid reduced the power consumption by 11.5% in comparison with tests performed without any nano-additives, while the same concentration reduced the flank wear by 45%.

Fatigue Response of As-Built DMLS Maraging Steel and Effects of Aging, Machining, and Peening Treatments

Abstract: The main motivations for this study arise from the need for an assessment of the fatigue performance of DMLS-produced Maraging Steel MS1, when it is used in the “as fabricated” state. The literature indicates a lack of knowledge from this point of view; moreover, the great potentials of the additive process may be more and more incremented, if an easier and cheaper procedure could be used after the building stage. The topic has been tackled experimentally, investigating the impact of heat treatment, machining, and micro-shot-peening on the fatigue strength with respect to the “as built state”. The results indicate that heat treatment may improve the fatigue response, as an effect of the relaxation of the process-induced tensile residual stresses. Machining can also be effective, but it must be followed (not preceded) by shot-peening, to benefit from the compressive residual stress state generated by the latter. Moreover, heat treatment and machining are related by a strong positive interaction, meaning their effects are synergistically magnified when they are applied together. The experimental study has been completed by fractographic as well as micrographic analyses, investigating the impact of the heat treatment on the actual microstructure induced by the stacking process.

Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques

Abstract: The Inconel 718 alloy, a difficult-to-cut superalloy with an extensive demand on aircraft and nuclear industries, being a low thermally conductive material exhibits a poor machinability. Consequently, the cutting tool is severely affected, and the tool cost is increased. In this context, an intelligent solution is presented in this paper—investigation of minimum quantity lubrication (MQL) and the selection of best machining conditions using evolutionary optimization techniques. A series of milling experiments on Inconel 718 alloy was conducted under dry, conventional flood, and MQL cooling modes. Afterward, the particle swarm optimization (PSO) and bacteria foraging optimization (BFO) were employed to optimize the cutting speed, feed rate, and depth-of-cut to minimize the flank wear (VBmax) parameter of a cutting tool. Though both the PSO and BFO models performed well, the validated results showed the superiority of PSO. Furthermore, it was found that the MQL performed better than the dry and flood cooling condition with respect to the reduction of the tool flank wear.

Electric discharge machining of titanium and its alloys: review

Abstract: Electric discharge machining (EDM) is one of the leading edge machining processes successfully used to machine hard-to-cut materials in wide range of industrial applications. It is a non-conventional material removal process that can machine a complex shapes and geometries with high accuracy. The principle of the EDM technique is to use thermoelectric energy to erode conductive components by rapidly recurring sparks between the non-contacted electrode and workpiece. To improve EDM performance, the machine’s operating parameters need to be optimized. Studies related to the EDM have shown that the appropriate selection of the process, material, and operating parameters had considerably improved the process performance. This paper made a comprehensive review about the research studies on the EDM of different grades of titanium and its alloys. This review presents the experimental and theoretical studies on EDM that aimed to improve the process performance, including material removal rate, surface quality, and tool wear rate, among others. This paper also examines evaluation models and techniques used to determine the EDM process conditions. Moreover, the paper discusses the recent developments in EDM and outlines the progression for future research.

Highly accurate 5-axis flank CNC machining with conical tools

Abstract: A new method for 5-axis flank computer numerically controlled (CNC) machining is proposed. A set of tappered ball-end-mill tools (aka conical milling tools) is given as the input and the flank milling paths within user-defined tolerance are returned. Thespace of lines that admit tangential motion of an associated truncated cone along a general, doubly curved, free-form surface is explored. These lines serve as discrete positions of conical axes in 3D space. Spline surface fitting is used to generate a ruled surface that represents a single continuous sweep of a rigid conical milling tool. An optimization-based approach is then applied to globally minimize the error between the design surface and the conical envelope. The milling simulations are validated with physical experiments on two benchmark industrial datasets, reducing significantly the execution times while preserving or even reducing the milling error when compared to the state-of-the-art industrial software.

Influence of defects and as-built surface roughness on fatigue properties of additively manufactured Alloy 718

Abstract: Electron beam melting (EBM) and Selective Laser Melting (SLM) are powder bed based additive manufacturing (AM) processes. These, relatively new, processes offer advantages such as near net shaping, manufacturing complex geometries with a design space that was previously not accessible with conventional manufacturing processes, part consolidation to reduce number of assemblies, shorter time to market etc. The aerospace and gas turbine industries have shown interest in the EBM and the SLM processes to enable topology-optimized designs, parts with lattice structures and part consolidation. However, to realize such advantages, factors affecting the mechanical properties must be well understood – especially the fatigue properties. In the context of fatigue performance, apart from the effect of different phases in the material, the effect of defects in terms of both the amount and distribution and the effect of “rough” as-built surface must be studied in detail. Fatigue properties of Alloy 718, a Ni-Fe based superalloy widely used in the aerospace engines is investigated in this study. Four point bending fatigue tests have been performed at 20 Hz in room temperature at different stress ranges to compare the performance of the EBM and the SLM material to the wrought material. The experiment aims to assess the differences in fatigue properties between the two powder bed AM processes as well as assess the effect of two post-treatment methods namely – machining and hot isostatic pressing (HIP). Fractography and metallography have been performed to explain the observed properties. Both HIPing and machining improve the fatigue performance; however, a large scatter is observed for machined specimens. Fatigue properties of SLM material approach that of wrought material while in EBM material defects severely affect the fatigue life.