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The cutting temperature in hard turning is extremely high, which reduces tool life, lowers machined-surface quality, and affects dimensional control. However, hard turning differs greatly from conventional turning in that the cutting process mainly happens at the tool-nose radius due to the extremely shallow depth of the cut. This paper provides a comprehensive and systematic analysis of this issue based on an evaluation of tool geometry in hard turning via finite element analysis (FEA) simulations and experiments. The effect of tool angles on cutting temperature in hard turning is analyzed. The impacts of cutting-edge angle, rake angle, inclination angle, and average local rake angle on the cutting temperature are investigated via central composite design (CCD). The simulated results and the empirically measured cutting temperature exhibit comparable patterns, with a minor 2% difference. Increasing the cutting-edge angle, negative rake angle and negative inclination angle enhances the local negative rake angles of the cutting-edge elements at the tool-nose radius involved in the cutting process. Notably, the most important component influencing cutting temperature in hard turning is the inclination angle, as opposed to normal turning, where the rake angle dominates the heat generation. Following this is the cutting-edge angle and the rake angle, which each contribute 40.75%, 32.39%, and 7.03%. These findings could enhance the application of the hard-turning technique by improving tool life and surface quality by focusing on optimizing the inclination angle.

In this paper, a cascade control structure is suggested to control servo systems that normally include a servo motor in coupling with two kinds of mechanism elements, a translational or rotational movement. These kinds of systems have high demands for performance in terms of fastest response and no overshoot/oscillation to a ramp function input. The fractional-order proportional integral (FOPI) and proportional derivative (FOPD) controllers are addressed to deal with those control problems due to their flexibility in tuning rules and robustness. The tuning rules are designed in the frequency domain based on the concept of the direct synthesis method and also ensure the robust stability of controlled systems by using the maximum sensitivity function. The M-Δ structure, using multiplicative output uncertainties for both control loops simultaneously, is addressed to justify the robustness of the controlled systems. Simulation studies are considered for two kinds of plants that prove the effectiveness of the proposed method, with good tracking of the ramp function input under the effects of the disturbances. In addition, the robustness of the controlled system is illustrated by a structured singular value (µ) plot in which its value is less than 1 over the frequency range.

The main purpose of this study is to investigate the influence of tool geometry (cutting edge angle, rake angle, and inclination angle) and to optimize tool wear and surface roughness in hard turning of AISI 1055 (52HRC) hardened steel by using TiN coated mixed ceramic inserts. The results show that the inclination angle is the major factor affecting the tool wear and the surface roughness in hard turning. With the increase in negative rake and inclination angles, the tool wear decreases, and the surface roughness increases. However, the surface roughness will decrease when the inclination angle increases to overpass a certain limit. This is a new and significant point in the research of the hard turning process. From this result, the large negative inclination angle (λ = −10°) should be applied to reduce the surface roughness and the tool wear simultaneously. With the optimal cutting tool angles in the research, the hard machining process is improved remarkably with decreases of surface roughness and tool wear 8.3% and 41.3%, respectively in comparison with the standard tool angles. And the proposed tool-post design approach brings an effective method to change the tool insert angles using standard tool-holders to improve hard or other difficult-to-cut materials turning quality.

Compliant mechanism has some advantages and has been widely applied in many accurate positioning systems. However, modeling the compliant mechanism behavior has suffered from many challenges, such as unstable results, and the limitation of training data set. In the field of compliant mechanism modeling, there has been no research interested in applying meta-heuristics optimization algorithms to optimize the weights and biases of the neural network globally. Additionally, the Physics-Guided Artificial Neural Network, a research direction that has received much attention recently, has not been considered in problems related to compliant mechanisms. In order to surmount those drawbacks, this paper pioneers a new approach to model behaviors of a compliant mechanism using the Hunger Game Search and the Physics-Guided Artificial Neural Network. The Hunger Game Search can directly search the model’s weights and biases so that the target function that takes advantages of both physical and data information can be minimized. The investigations on diverse training set ratios and the ANOVA tests at the significance level reveal that using the Hunger Game Search can result in smaller errors than using the backpropagation method. Furthermore, applying the Hunger Game Search to a Physics-Guided Artificial Neural Network not only can reduce error but also can increase convergence speed compared to applying Hunger Game Search to conventional neural networks. Those results demonstrate the potential of the proposed method in modeling the behavior of compliant mechanisms. [ABSTRACT FROM AUTHOR]

Vibration-assisted machining, known as hybrid processing technology, offers several benefits over conventional machining methods. However, developing mechanical structure designs to generate a non-resonant frequency source remains challenging. The objective of this study is to propose a novel design for an XY flexure positioner by combining the pseudo-rigid-body model with the Lagrange technique, finite element analysis and Crayfish optimization algorithm. Firstly, the mechanism was designed by combining a hybrid amplifier and parallel driving mechanism integrated with right circular hinges to increase the natural frequency and precision for potential application to VAM CNC milling. Then, the analytical model was established by the pseudo-rigid-body and Lagrange method. Next, the theoretical result was verified by finite element analysis. The first natural frequency results of theory and FEM methods were found at 990.74 Hz and 1058.5 Hz, respectively. The error between the two methods was 6.4%, demonstrating a reliable modeling approach. Based on the analytical equations, the Crayfish optimization algorithm was utilized for optimizing the main design variables of the mechanism. Next, the prototype was fabricated. The results showed that the experimental and simulated frequencies were 1127.62 Hz and 1216.6 Hz, with an error between the two methods of 7.31%. Finally, the workpiece was installed on the prototype and a real vibration-assisted CNC milling process was carried out in the frequency range [700 Hz, 1000 Hz]. The best surface roughness of the specimen was achieved at a frequency of 900 Hz with a Ra of 0.287 µm. This demonstrates that the proposed XY mechanism is an effective structure for generating a non-resonant frequency source for vibration-assisted machining. [ABSTRACT FROM AUTHOR]

A novel compact 2-DOF compliant positioner is developed for the purpose of achieving good characteristics such as high natural frequency, high displacement amplification ratio, good linear motion, and compact structure based on its symmetrical structure. To be specific, the developed stage is proposed according to an advanced six-lever displacement amplifier arranged at an inclination angle of the rigid bar utilizing right circular hinges and a parallel guiding mechanism with integrated flexure leaf hinges to attain the above-mentioned characteristics and reduce the decoupling mobility error. First, to quickly assess the initial quality response, an integration method of kinetostatic analysis, the Lagrange method, and finite element analysis was applied to evaluate and verify the quality characteristic of the stage. The experimental result showed that the error between the analytical method and the FEA method was 1.3%, which was relatively small and reliable for quickly assessing the primary quality response of the proposed positioner. Next, to boost the important output characteristics of the developed positioner, the integration approach of the response surface method and NSGA-II algorithm was utilized to find the optimal design variables. Finally, a prototype was manufactured based on the CNC milling method to validate the experimental and FEA analysis results. The attained results show that the optimal results of safety factor and output displacement were 2.4025 and 248.9 µm. Moreover, the FEA verification results were 2.4989 and 242.16 µm, with errors for safety factor and output displacement between the optimal result and the FEA result of 3.86% and 2.78%, respectively. In addition, the simulation and experimental results of the first natural frequency were 371.83 Hz and 329.59 Hz, respectively, and the error between the FEA result and experimental result for the first natural frequency was 11.36%. Furthermore, the achieved results show that the relationship between input displacement and output displacement of the experimental result and the FEA result of the developed structure achieved a good linear connection. These results suggest that the proposed positioner will be a potential structure employed in precise positioning systems and nanoindentation testing positioning systems for checking bio-specimens' behaviors. [ABSTRACT FROM AUTHOR]

This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and constraints of the mechanism are formulated via proposing a combination of artificial neural network (ANN) and particle swarm optimization (PSO) methods. Next, the PSO is hybridized with the grey wolf optimization, namely PSO-GWO, which is applied to three scenarios in handling the single objective function. In order to search the multiple functions for the mechanism, the multiobjective optimization genetic algorithm (MOGA) is applied to the last scenario. The achieved results showed that the fitness functions are well-formulated using the PSO-based ANN method. In the scenario 1, the stroke achieved by the PSO-GWO (1852.9842 μm) is better than that gained from the GWO (1802.8087 μm). In the scenarios 2, the stress gained from the PSO-GWO (243.3183 MPa) is lower than that achieved from the GWO (245.0401 MPa). In the scenario 3, the safety factor retrieved from the PSO-GWO (1.9767) is greater than that achieved from the GWO (1.9278). In the scenario 4, by using MOGA, the optimal results found that the stroke is about (1741.3 μm) and the safety factor is 1.8929. The prediction results are well-fitted with the numerical and experimental verifications. The results of this paper are expected to facilitate the synthesis and analysis of compliant mechanisms and related engineering designs. [ABSTRACT FROM AUTHOR]

Compliant mechanisms with flexure hinges have been widely applied for positioners, bioengineering, and aerospace. In this study, a new optimized design method for the mobile microrobotic platform was developed for the polishing robot system. A metaheuristic-based machine learning technique in combination with finite element analysis (FEA) was developed. The designed platform allows three degrees of freedom with two x-and-y translations and one z-axis rotation. A new hybrid displacement amplification mechanism was also developed using Scott-Russell and two-lever mechanisms to magnify the workspace of the platform. The leaf hinges were employed due to their large rotation, and the right circular hinges were adopted because of their high accuracy. In modeling the behaviors of the developed platform, the artificial neural network is formulated in combination with the teaching-learning-based optimization (TLBO) method. The ANN architecture was optimized through TLBO to a better approximation. And then, three optimized case studies were conducted by the TLBO. The data is collected through FEA simulation. The modeling results from the TLBO-based ANN were well established with excellent metrics of R, R2, and MSE. The optimized results found that the proposed MPM platform achieves a max-y stroke of 1568.1 μm, max-x stroke of 735.55 μm, and max-θ rotation angle of 2.26 degrees. The proposed MPM platform can operate at a high displacement amplification ratio of over 9. [ABSTRACT FROM AUTHOR]

An XYZ compliant micropositioner has been widely mentioned in precision engineering, but the displacements in the X, Y, and Z directions are often not the same. In this study, a design and optimization for a new XYZ micropositioner are developed to obtain three same displacements in three axes. The proposed micropositioner is a planar mechanism whose advantage is a generation of three motions with only two actuators. In the design strategy, the proposed micropositioner is designed by a combination of a symmetrical four-lever displacement amplifier, a symmetrical parallel guiding mechanism, and a symmetrical parallel redirection mechanism. The Z-shaped hinges are used to gain motion in the Z-axis displacement. Four flexure right-circular hinges are combined with two rigid joints and two flexure leaf hinges to permit two large X-and-Y displacements. The symmetrical four-lever displacement amplifier is designed to increase the micropositioner's travel. The displacement sensor is built by embedding the strain gauges on the hinges of the micropositioner, which is developed to measure the travel of the micropositioner. The behaviors and performances of the micropositioner are modeled by using the Taguchi-based response surface methodology. Additionally, the geometrical factors of the XYZ micropositioner are optimized by teaching–learning-based optimization. The optimized design parameters are defined with an A of 0.9 mm, a B of 0.8 mm, a C of 0.57 mm, and a D of 0.7 mm. The safety factor gains 1.85, while the displacement achieves 515.7278 µm. The developed micropositioner is a potential option for biomedical sample testing in a nanoindentation system. [ABSTRACT FROM AUTHOR]

The nanoindentation technique is employed to characterize the behaviors of biomaterials. Nevertheless, there is a lack of development of a miniaturized precise positioner for in situ nanoindentation. Besides, modeling behaviors of the positioner are restricted due to its complex kinematic characteristics. Therefore, this paper proposes a novel compliant two degrees of freedom (dof) stage for positioning a biomaterial sample in in situ nanoindentation. In addition, a new modeling and dimensional optimization synthesis method of the stage is developed. The proposed effective methodology is developed based on a kinetostatic analysis-based calculation method, the Lagrange approach, and a neural network algorithm. With an increased advance in artificial intelligence, a neural network algorithm is proposed to extend the applicability of artificial neural networks in optimizing the parameters of the stage. First, the 2-dof stage is built via a combination of an eight-lever displacement amplifier and a symmetric parallelogram mechanism. Second, a chain of mathematical equations of the 2-dof stage is constructed using a kinetostatic analysis-based method to calculate the ratio of displacement amplification and the input stiffness of the 2-dof stage. Then, the Lagrange method is utilized to formulate the dynamic equation of the 2-dof stage. Finally, a neural network algorithm is adopted to maximize the natural first frequency of the proposed stage. The optimal results determined that the frequency of the stage can achieve a high value of 112.0995 Hz. Besides, the formed mathematical models were relatively precise by comprising the simulation verifications. [ABSTRACT FROM AUTHOR]

In situ nanoindentation is extensively employed for online observing deformation and mechanical behaviors of bio-materials. However, the existing designs of the positioning stages have limited performances for testing soft or hard biomaterials. Consequently, this paper proposes a new structural design of a compliant one degree of freedom (01-DOF) stage with faster response. In addition to a new design, this article applies an analytical method to estimate the kinematic and dynamic behaviors of the stage. Firstly, the 01-DOF stage is designed with two modules, including a displacement amplifier with six levers and a symmetric parallelogram mechanism. Secondly, a kinetostatic diagram of the stage is built by pseudo-rigid-body method. Then, the dynamic equation of the proposed stage is formulated using the Lagrange method. In order to speed up the response of the indentation system, the structural optimization of the stage is conducted via the Firefly algorithm. The results showed that the theoretical first-order resonant frequency is found at about 226.8458 Hz. The theoretical consequences are nearby to the verified simulation. Besides, this achieved frequency of the presented stage is greater than that of previous stages. In an upcoming study, the prototype will be fabricated by additive manufacturing method or a computerized wire cutting method in order to verify the analytical results with experimental results. [ABSTRACT FROM AUTHOR]

Examines the effect of winding angle on impact properties of thin walled tubes made from carbon fiber and epoxy resin. Maximal impact energy of wound specimens; Low matrix shear strength; Elastic strain energy release rate.