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The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study. [ABSTRACT FROM AUTHOR]

Tungsten alloy has excellent performance and has been widely used in military, aerospace and nuclear energy, and other cutting-edge industries. However, tungsten alloy has large hardness, high strength, and poor plastic deformation ability, which resulted in high cutting force and serious tool wear during the cutting process, leading to low surface quality of the workpiece after molding. Therefore, it is of great significance to strengthen the research on tungsten alloy cutting technology to promote the development of tungsten alloy application. Firstly, the research progress of tungsten alloy cutting process technology has been systematically reviewed, and the current status of cutting parameters optimization, new cutting methods and devices, and cutting fluid technology have been emphatically reviewed. Secondly, the types of tungsten alloy cutting tools, the relevant tool micro-texture, and tool coatings technology have been briefly described, and the composite cutting technology such as cryogenic cutting, electroplastically assisted cutting, and ultrasonic vibration assisted cutting and the effect of cutting performance prediction technology on tungsten alloy machining performance have been summarized. Finally, the development prospect of tungsten alloy cutting technology has been prospected. [ABSTRACT FROM AUTHOR]

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones. [ABSTRACT FROM AUTHOR]

Machining optimization has been studied for low machining cost, high production rate, and energy saving. Nevertheless, most of the studies did not consider the part geometry. The complex part geometry results in varying contact conditions between the cutting tool and the workpiece. Therefore, the conventional tool life equation may not be applicable, and it is impossible to expect the same performance of machining different parts even with the same cutting parameters. In addition, the amount of material removal is different from part to part. Either the underuse or overuse of the tool is considered production loss in machining process. As a result, it is better for the cutting tool to be utilized until it reaches the end of its life when the operation is completed. Taking the conditions above into considerations, an objective function including the machining cost and the loss of tool usage is proposed in this study. Three turning operations, one straight turning and two profiling, were investigated to show the effect of part geometry on the machining optimization and tool wear. The results showed the different optimization processes and tool wear patterns although the initial cutting parameters and the amount of material removal were identical. The optimization processes were converged in 5 iterations. The cost of three cases reduced 34%, 33%, and 19%, respectively, with different optimized cutting parameters, and the tool approached the end of its life when the operation was done. [ABSTRACT FROM AUTHOR]

Machine learning has been widely used in diagnosing system faults as an integration part in modern intelligent manufacturing. However, sensor selection and index for machine tools are versatile and not standardized at this moment for manufacturing equipment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data-driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. As a result, a thoughtful flow for identifying key sensor index and to evaluate its impact on the performance of subsequent machine learning scheme would be essential. In this work, the status monitoring and prediction of a milling cutter wear problem is investigated as an example to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. In parallel, the well-used multilayer perception (MLP) artificial neural network (ANN) models are also used to elucidate the importance of the problem. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures. A procedure is developed to evaluate the sensitivity of each index and the key dominated indexes are eventually identified. To elucidate the importance of proper sensor index selection, three MLP ANN models are established based on different sensor features to examine the influence of selected sensors indexes on the prediction accuracy. The results show that with appropriate sensors signatures, even with less amount of experimental data, the model can indeed achieve a better prediction (98.7%) in comparison with that without proper sensor index selection (90.3). This implies that a rigorous process for identifying key feature is essential for system diagnosis in future intelligent manufacturing. [ABSTRACT FROM AUTHOR]

In low-frequency vibration-assisted drilling (LFVAD) of CFRP/Ti6Al4V stacks, owing to the different mechanical and thermal performances of the involved carbon fiber–reinforced plastic (CFRP) composite and Ti6Al4V alloy, the short tool life of commonly used carbide (WC) tools and rapidly deteriorated machining quality are the major problems. This paper aims to explore whether the polycrystalline diamond (PCD) tool can achieve a competitive effect on drilling CFRP/Ti6Al4V stacks compared with previous CFRP drilling studies. To avoid the frequent occurrence of chisel edge fracture in LFVAD of Ti6Al4V alloy, a model of plowing behavior is established and parametric experiments based on it are performed with both PCD and WC drills. It is found that the periodically dynamic force is the major factor causing the chisel edge fracture of the PCD drill, which can be alleviated by reducing the feed rate. Besides, by using PCD drills instead of WC drills in Ti6Al4V drilling, the exit burr height, drilling diameter deviation, and surface roughness value are reduced under each given parameter. Finally, PCD and WC drill bits are used to perform a wear test on the CFRP/Ti6Al4Vstacks under optimized parameters. The PCD drill achieves superior performance, including lower wear rate, better hole quality, and higher consistency. The experiments discussed in this paper provide some suggestions for selecting tools and cutting parameters when drilling CFRP/Ti6Al4V stacks. [ABSTRACT FROM AUTHOR]

Machining of CFRP composites is usually described as a challenging process from tool life and surface quality perspectives. To achieve a flawless machined surface at reasonable cutting tool life/cost, an optimum combination of cutting tool material and geometry is the key. The cutting tool angles are of importance as these dictate the cutting mechanism as well as the cutting edge robustness and the introduction of vibration to the machining process opens up new horizons of improvement and stirs further research questions. This article investigates the effect of tools with different geometry possessing different rake angles when used in ultrasonic-assisted edge trimming operation dealing with multidirectional CFRP laminates. A full-factorial experimental design was adopted to analyze the effect of process parameters typically cutting speed, feed rate, rake angle, amplitude, and their interactions. Machining performance indicators were captured which were the cutting forces, tool wear, chip temperature, and surface roughness. The results showed that UAM mode contributed to an increase in the cutting forces, tool wear, and chip temperature compared to the conventional mode. On the other hand, UAM mode improved the quality of the machined surface. Additionally, the ultrasonic mode enhanced the material removal mechanism using a tool with a negative rake angle. [ABSTRACT FROM AUTHOR]