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Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works.

Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network–based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient–based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.

Micro-milling force and specific cutting energy play an important role in revealing the micro-milling mechanism. However, it is quite difficult to compare the micro-milling force values from different experiments due to the lack of a representative cutting force parameter to comprehensively evaluate the micro-milling force. Especially, there is no unified formula to accurately calculate the specific cutting energy in micro-milling due to variable chip cross-sectional area and periodically varying micro-milling force. In this work, the micro-milling force was systematically analyzed with fast Fourier transform spectrum analysis, curve shape, and representative parameter evaluation. The quasi-dynamic cutting force, which is represented by the P–V value of cutting force, was adopted to comprehensively evaluate the micro-milling force. The specific cutting energy was calculated with the ratio of quasi-dynamic cutting force and the average undeformed chip thickness. Moreover, the variable regularity of quasi-dynamic cutting force and specific cutting energy on cutting parameters were obtained with the micro-milling experiment. The results show that the quasi-dynamic cutting force first decreases and then increases with the increase of feed per tooth due to the chip accumulation effect. With the increase of spindle speed and depth of cut, the quasi-dynamic cutting force decreases and increases, respectively. The minimum undeformed chip thickness is between 0.3 and 0.5 μm, which is around 0.19 to 0.32 of tool edge radius in micro-milling Ti6Al4V. With the increase of spindle speed and depth of cut, the specific cutting energy shows a decreasing trend and changes a little, respectively. With the decrease of the feed per tooth, the specific cutting force shows a nonlinear increase. Our findings are of great significance for further scientific understanding the micro-milling mechanism from the perspective of cutting force and specific cutting energy.

The radial cutting force balance of an indexable insert drill directly affects the workpiece’s depth, dimensional accuracy, and surface quality. Accurate evaluation of the radial cutting force of the indexable insert drill is necessary to guide for the improvement of the drilling performance. In this research, a new finite element simulation model of radial cutting force for indexable insert drill was proposed considering the influencing regularities of cutting feed, cutting speed, and cutting width on the unit radial cutting force of the drill. The optimal geometric parameters of the indexable insert drill were obtained based on the simulated radial cutting force balance. The accuracy of the new proposed model was verified according to the diameter deviation between the hole and the tool. The difference between the hole diameter and the tool diameter was reduced by 80% for the new proposed model compared to the former radial cutting force model.

Response measurement of various functionality states of machines is an inevitable part of smooth production. An effectively efficient measurement and control system of the machinery helps the inspection engineers to detect failures. In the recent age, the concept of industry 5.0, which focuses on the interaction between humans and machines, has increased the importance of sensors in the industry. Various sensing devices may aid and support the machining process, making it more efficient. These sensing devices support machine tools and enhance productivity by reducing failures. The application of an online monitoring system that includes vibration measurement and tool wear measurement, and the electrical energy consumption is getting fame in industry and academia. This paper mainly presents a holistic review of various sensors and their application in the manufacturing processes. Advancements in the sensor for quality measurement, cutting force measurement, and tool wear measurement are discussed. Furthermore, the adoption of the Internet of Things (IoT) in machining processes and conversion of conventional manufacturing processes into modern digitalized systems are discussed. Recent trends of research to improve the sensor technology have been improved. This study provides fundamental guidelines for using and adopting the various types of sensors in machining processes.

Abstract The machinability of Ti-6Al-4V alloy has been a constant challenge in the industry, although the material is widely used in the aerospace and medical industries due to its mechanical properties, particularly its strength-to-weight ratio. The current research presents a hybrid laser- and ultrasonic-assisted machining (LUAM) technique to improve the machinability of Ti-6Al-4V alloy in a turning process. This is compared with ultrasonic-assisted machining (UAM), laser-assisted machining (LAM), and convectional turning (CT). The results reveal that UAM and LAM can reduce the cutting forces and surface roughness (Ra) compared to the CT. However, these are achieved mainly at the lowest range of cutting speeds. The hybrid LUAM process demonstrates process improvement with wider range of cutting speeds and depths of cut, which is achieved due to the combined force reduction and thermal softening effect by the hybrid process.

The machinability of materials is highly affected by their hardness, and it affects power consumption, cutting tool life as well as surface quality while machining the component. This work deals with machining of annealed AISI 4340 alloy steel using a coated carbide tool under a dry environment. The microhardness of annealed and non-annealed workpieces was compared and a significant reduction was found in the microhardness of annealed samples. Microstructure examination of the annealed sample revealed the formation of coarse pearlite which indicated a reduction of hardness and improved ductility. A commercially CVD multilayer (TiN/TiCN/Al2O3/ZrCN) coated cemented carbide cutting tool was employed for turning quenched and tempered structural AISI 4340 alloy steel by varying machining speed, rate of feed, and depth of cut to evaluate the surface quality, machining forces, flank wear, and chip morphology. According to the findings of experiments, the feed rate possesses a high impact on surface finish, followed by cutting speed. The prominent shape of the serrated saw tooth chip was noticed at a higher cutting speed. Machined surface finish and cutting forces during turning is a function of the wear profile of the coated carbide insert. This study proves that annealing is a low-cost and economical process to enhance the machinability of alloy steel.

For carbon fiber-reinforced plastic (CFRP) composite components, especially advanced CFRP components with complex three-dimensional features, surface grinding is often needed to generate final dimensions and functional surfaces. Surface damages are frequently induced during surface grinding, reducing the load-bearing capability and service life of the components. Therefore, it is desirable to perform surface grinding of CFRP in a high-quality and high-efficiency way. Rotary ultrasonic machining (RUM) surface grinding has been investigated to machine CFRP for improved surface quality. Cutting force is one of the most important output variables for evaluating RUM surface grinding. The modeling of cutting force is essential to effectively control the occurrence of surface damages during RUM surface grinding of CFRP. In the RUM surface grinding process, the workpiece material is primarily removed by abrasives on the tool peripheral surface, thus it is essential to investigate the feed-direction cutting force model. However, such models are not available in the literature. In this study, for the first time, a mechanistic feed-direction cutting force model in RUM surface grinding of CFRP is established based on the assumption that the material is removed by brittle fracture. The mechanistic model has one parameter, fracture volume factor of the workpiece material, which needs to be determined by an experiment. There is a good consistency between theoretically predicted trends and experimentally observed results on the relationships between feed-direction cutting force and input variables.

In the present study, prediction and optimization of the surface roughness and cutting forces in slot milling of aluminum alloy 7075-T6 were pursued by taking advantage of regression analysis, support vector regression (SVR), artificial neural network (ANN), and multi-objective genetic algorithm. The effects of process parameters, including cutting speed, feed per tooth, depth of cut, and tool type, on the responses were investigated by the analysis of variance (ANOVA). Grid search and cross-validation methods were used for hyperparameter tuning and to find the best ANN and SVR models. The training algorithm of developed NNs was one of the hyperparameters which was chosen from Levenberg-Marquardt and RMSprop algorithms. The performance of regression, SVR, and ANN models were compared with each other corresponding to each machining response studied. The ANN models were integrated with the non-dominated sorting genetic algorithm (NSGA-II) to find the optimum solutions by means of minimizing the surface roughness and cutting forces. In addition, the desirability function approach was utilized to select proper solutions from the statistical tools.

FV520B steel is a challenging metal to machine, often exhibiting adiabatic shear behavior during cutting, which leads to the formation of serrated chips. Serrated chips not only cause fluctuations in cutting forces, resulting in a loss of cutting accuracy, but also contribute to rapid tool wear. Therefore, it is crucial to understand the evolution of serrated chips during the cutting process and establish an accurate cutting force model. In this study, we established the serrated chip deformation coefficient and serration factor to analyze the changes in chip morphology based on cutting speed, uncut chip thickness, and tool rake angle. Additionally, a cutting force prediction model was developed using the Johnson–Cook constitutive model, taking into account adiabatic shear damage, and the cutting temperature model proposed by Huang and Liang. The research results found that the serration degree of FV520B steel chips gradually becomes severe with increasing cutting speed, increasing uncut chip thickness, and decreasing tool rake angle. The average errors in predicting cutting forces in the X, Y, and Z directions were 10.3%, 10.5%, and 10.9%, respectively, with maximum errors of 15.5%, 13.4%, and 14.0%. The effect of cutting parameters on FX and FY can be summarized as follows: axial cutting depth > feed per tooth > spindle speed. The effect on FZ can be described as: spindle speed > axial cutting depth > feed per tooth.