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Inconel 718 is the most popular nickel-based superalloy, extensively used in aerospace, automotive and energy industries owing to its extraordinary thermomechanical properties. It is ...also notoriously a difficult-to-cut material, due to its short tool life and low productivity in machining operations. Despite significant progress in cutting tool technologies, the machining of Inconel 718 is still considered a grand challenge.
This paper provides a comprehensive review of recent advances in machining Inconel 718. The progress in cutting tools’ materials, coatings, geometries and surface texturing for machining Inconel 718 is reviewed. The investigation is focused on the most adopted tool materials for machining of Inconel 718, namely Cubic Boron Nitrides (CBNs), ceramics and coated carbides. The thermal conductivity of cutting tool materials has been identified as a major parameter of interest. Process control, based on sensor data for monitoring the machining of Inconel 718 alloy and detecting surface anomalies and tool wear are reviewed and discussed. This has been identified as the major step towards realising real-time control for machining safety critical Inconel 718 components. Recent advances in various processes, e.g. turning, milling and drilling for machining Inconel 718 are investigated and discussed. Recent studies related to machining additively manufactured Inconel 718 are also discussed and compared with the wrought alloy. Finally, the state of current research is established, and future research directions proposed.
Aiming at the problems of low machining accuracy and more serious tool wear in the traditional diamond grinding machining (DGM) microstructure of hard and brittle materials, this paper proposes ...high-speed rotary ultrasonic machining (HRUM) technology and develops a HRUM machine tool. The hardware part of the machine tool mainly includes the spindle module, micro-motion system module, ultrasonic machining tank module, and data acquisition (DAQ) system module. The LabView-based controlled machining control system, including motion selection, initialization, coarse tool setting, constant force tool setting, control machining, and coordinate display module, is developed. Comparative experimental research of the HRUM and DGM of small holes in Al2O3 ceramics is carried out in the developed HRUM machine tool. The results demonstrate that HRUM effectively reduces axial cutting forces, reduces binder adhesion, and suppresses slippage while improving tool-cutting ability and extending tool life compared to DGM under the same machining parameters. This technology has essential research significance for the high-precision and efficient machining of microstructures in hard and brittle materials.
•A hybrid predictive maintenance method for CNC machine tools driven by Digital Twin model and Digital Twin data is proposed.•Digital Twin model is built in multi-domain and reflects the actual ...working conditions; Digital Twin data is gathered by different types of sensors and used for data-driven remaining useful life prediction model; then the system observation value and theoretical derivation value are fused by particle filtering algorithm. The validity and accuracy of the proposed method are verified by cutting tool life prediction of CNC machine tools.•This method enables the Digital Twin model and data to be better integrated and applied, thus can give out a more accurate and intelligent result than before.
As a typical manufacturing equipment, CNC machine tool (CNCMT) is the mother machine of industry. Fault of CNCMT might cause the loss of precision and affect the production if troubleshooting is not timely. Therefore, the reliability of CNCMT has a big significance. Predictive maintenance is an effective method to avoid faults and casualties. Due to less consideration of the status variety and consistency of CNCMT in its life cycle, current methods cannot achieve accurate, timely and intelligent results. To realize reliable predictive maintenance of CNCMT, a hybrid approach driven by Digital Twin (DT) is studied. This approach is DT model-based and DT data-driven hybrid. With the proposed framework, a hybrid predictive maintenance algorithm based on DT model and DT data is researched. At last, a case study on cutting tool life prediction is conducted. The result shows that the proposed method is feasible and more accurate than single approach.
Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human ...resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.
This study aimed to investigate the effect of the clearance angle of the milling tool on wear, cutting forces, machined edge roughness, and delamination during non-contiguous milling of ...carbon-fiber-reinforced plastic (CFRP) composite panels with a twill weave and 90° fiber orientation. To achieve the objective of the study, it was first necessary to design suitable tools (6 mm diameter sintered carbide shank milling cutters) with a variety of clearance angles (8.4°, 12.4°, and 16.4°) and all the machinery and measuring equipment for the research to be carried out. Furthermore, measurement and evaluation methods for cutting tool wear, cutting forces, machined edge roughness, and delamination were developed. Last but not least, the results obtained during the research were summarized and evaluated. From the experiments conducted in this study, it was found that the tool clearance angle has a significant effect on tool wear, roughness of the machined surface, and delamination of the carbon fiber composite board. The tool with a clearance angle of 8.4° wore faster than the tool with a clearance angle of 16.4°. The same trend was observed for cutting force, machined surface roughness, and delamination. In this context, it was also shown that the cutting force increased as the tool wear increased, which in turn increased surface roughness and delamination. These results are of practical significance, not only in terms of the quality of the machined surface but also in terms of time, cost, and energy savings when machining CFRP composite materials.
In this study, the effect of four different machining methods consisting of “Trochoidal,” “Follow Part,” “Zig,” and “Zig-Zag” which are common in CAM package programs and used often in the industry ...has been investigated. Firstly, the 3D model of samples is produced in the CAD program. Models are machined in CNC milling workbench. In order to examine the effect of tool path strategies on tool life, the amount of wear loss as a criterion and the SEM images of tool wear as a supporting criterion are taken into account. According to the results, the “Zig-Zag” tool path strategy is the tool path that causes the highest weight loss in the cutting tool, while the “Trochoidal” tool path strategy causes in the least weight loss in the cutting tool. In addition, the surface roughness of the samples taken from different regions of the model and the operation time of the different tool paths are determined. In this context, the operation time of the test sample is maximum in “Zig” team path strategy, while it is at least in “Follow part” team path strategy. By examining the surface roughness, the best surface roughness values are obtained with the strategy of “Follow Part” and “Trochoidal” tool path, while the worst values are obtained in the “Zig” tool path strategy. As a result of the examination, the optimum tool path strategy for cutting tool life was found to be “Trochoidal” tool path. This work differs from the counterparts as handling the AISI X210Cr12 steel which make the paper first in determining the effect of tool path strategies on machinability. Lastly, obtained findings are useful for the organization of this type of steel in manufacturing chain of industrial companies.
•No single tool satisfies all criteria required in bankruptcy prediction.•A framework for tool selection based on priority criteria is proposed.•Support vector machine performs well on small sample ...sizes in bankruptcy prediction.
The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.
Cutting tool geometries play an important role in tool performance, material flow, cutting force, and cutting temperature distribution. The most common edge geometries of commercial tools are hone, ...chamfer, and chamfer with a hone. A novel method for designing tool edge geometry that combines experimental and simulation results is investigated in this paper. An uncoated carbide tool was used to orthogonally cut AISI 1045 and AISI 4140 steel. By observing how the tool geometry changed during the machining process with white light interferometry, a new tool wear geometry model could be proposed. Furthermore, temperature and stress distribution modeling were carried out with Finite Element Analysis (FEA) based on the newly designed tool geometry under various cutting parameters. A comparison of the machining results of the new and conventional tool geometries has confirmed that the newly designed tool is capable of achieving a lower wear rate, thereby extending the tool life.
•A novel “experiment-simulation procedure” method was used to measure, simulate, regulate, and validate the tool edge geometries.•A non-destructive method of measuring wear was used to monitor 3D tool wear geometry changes of each pass.•A novel turning tool edge geometry was established to reduce wear rate and to improve tool life.•Temperature and stress distribution modeling were carried out by FEA based on the newly designed tool geometry.
Inconel 718, valued for its remarkable mechanical, corrosion, and high-temperature resistance up to 700 °C, is a predominant material constituting almost half of all global superalloy usage. ...Nonetheless, its exceptional properties make it a challenging material to machine, generating elevated heat at the tool-chip interface that strains cutting tools. Although ceramic tools offer one avenue, cemented carbide tools, particularly in “S” grades, find utilization despite limitations. To render cemented carbide tools viable, a lubricating-cooling medium is essential. Traditionally, abundant cutting fluids or conventional flood application (CFA) are employed. Nevertheless, CFA’s broad use raises sustainability concerns across economic, social, and environmental spheres. To address this, extensive global research aims to explore alternatives or substitutes for CFA. This study introduces an innovative approach using internally cooled tools (ICTs), which eliminates fluid release into the atmosphere, curbing improper disposal. ICT operates within a closed-loop cycle, cooling the tool. Moreover, ICT offers low toxicity for operators, minimizing direct contact risks and workplace contamination. Employing cemented carbide inserts with internal coolant galleries, the ICT method underwent tool life tests during Inconel 718 turning, followed by wear mechanism analyses. The study involved three cutting atmospheres (ICT, CFA, and dry machining (DM)) and two tool coating variations (TiNAl and a double coating AlCrN + TiNAl, referred as AlCrN+). With consistent finishing conditions, cutting speed (
v
c
= 45 m/min), feed rate (
f
= 0.103 mm/rev), and depth of cut (
a
p
= 0.5 mm) remained unchanged. Replicated twice for statistical validity, 18 experiments were conducted. After testing, scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS) unveiled wear mechanisms. Results indicated AlCrN+ surpassed TiAlN coatings, 35% better on average. In contrast, ICT delivered optimal TiAlN-coated tool life, even better than CFA, 27% better. Conversely, AlCrN + coatings achieved the best outcomes with CFA. Irrespective of tool coating or atmosphere, observed wear mechanisms encompassed abrasion, adhesion, and diffusion, with AlCrN+ exhibiting smoother, more uniform wear, predominantly flank, and crater wear. Finally, ICT has shown to be a promising eco-friendly technique with high industrial potential to be explored and improved.
•Presents a sensor fusion approach with the investigation of sensor signal features.•5 sensor signals were investigated to understand their capability on flank wear.•A tool condition monitoring ...system was set up to collect and evaluate data.•Acoustic emission, current, temperature, force and vibration signals were measured.•Acoustic emission and temperature signals were found as effective on flank wear.
Monitoring of the cutting area with different type of sensors requires confirmation for composing sensor fusion to obtain longer tool life and high-quality product. The complex structure of machining and interaction between variables affect the influence of parameters on quality indicators. Using multiple sensors provide comparison of information acquired from different resources and make easier to decide about tool and workpiece condition. In this experimental research for the first time, five different sensors were adopted to a lathe for collecting data to measure the capability of each sensor in reflecting the tool wear. Cutting forces, vibration, acoustic emission, temperature and current measurements were carried out during turning of AISI 5140 with coated carbide tools. Considering the graphical investigation, the successes of sensors on detection of progressive flank wear and tool breakage were investigated. Besides, the effects of cutting parameters on measured variables were interpreted considering graphs. According to results, temperature and acoustic emission signals seem to be effective about 74% for flank wear. In addition, fuzzy logic based prediction of flank wear was performed with the assistance of temperature and acoustic emission sensors with high accuracy which demonstrates their availability for sensor fusion. Tool breakage occurs instantly which can prevent with the assistance of sensor signals and tangential and feed cutting forces, acoustic emission and vibration signals seem as reliable indicators for approaching major breakage. Sensor fusion based turning provides confirmed information which enables more reliable, robust and consistent machining.
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