Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.
Practice experimentation that integrates the manufacturing processes and cutting-edge technologies of smart manufacturing (SM) is essential for future academic and applied engineering personnel. The broadening efficacy of hands-on experience in SM engineering education has been manifested. In this regard, a reference practical system is proposed in this study for hands-on training in SM crucial advancements. The system constructs a mobile robot-based production line (MRPL) to increase participants’ interest in theoretical learning and professional skills. The MRPL-based reference system includes the comprehensive principles and processes involved in modern SM factories from warehousing to logistics, processing, and testing. With key features of modularity, integrability, customizability, and open architecture, this system has a threefold objective. First, it is an interdisciplinary subject that enables students to translate classroom learning into authentic practices, thus facilitating knowledge synthesis and training involvements. Second, it offers effective support to cultivate the attributions and behavioral competencies of SM talents, such as perseverance, adaptability, and cooperation. Third, it promotes students’ capacities for critical thinking and problem solving so that they can deal with the difficulties that physical systems have and motivates them to pursue careers with new syllabi, functions, and process techno-logies. The received positive evaluations and assessments confirm that this MRPL-based reference system is beneficial for modern SM talent training in higher engineering education.
Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.
Ultrasonic cutting with a disc cutter is an advanced machining method for the high-quality processing of Nomex honeycomb core. The machining quality is influenced by ultrasonic cutting parameters, as well as tool orientations, which are determined by the multi-axis machining requirements and the angle control of the cutting system. However, in existing research, the effect of the disc cutter orientation on the machining quality has not been studied in depth, and practical guidance for the use of disc cutters is lacking. In this work, the inclined ultrasonic cutting process with a disc cutter was analyzed, and cutting experiments with different inclination angles were conducted. The theoretical residual height models of the honeycomb core, as a result of the lead and tilt angles, were established and verified with the results obtained by a linear laser displacement sensor. Research shows that the residual height of the honeycomb core, as a result of the tilt angle, is much larger than that as a result of the lead angle. Furthermore, the tearing of the cell wall on the machined surface was observed, and the effects of the ultrasonic vibration, lead angle, and tilt angle on the tear rate and tear length of the cell wall were studied. Experimental results revealed that ultrasonic vibration can effectively decrease the tearing of the cell wall and improve the machining quality. Changes in the tilt angle have less effect than changes in the lead angle on the tearing of the cell wall. The determination of inclination angles should consider the actual processing requirements for the residual height and the machining quality of the cell wall. This study investigates the influence of the inclination angles of a disc cutter on the machining quality of Nomex honeycomb core in ultrasonic cutting and provides guidelines for machining.
The machining industry must maximize the machine tool utilization for its efficient and effective usage. Determining a feasible workpiece location is one of the significant tasks performed in an iterative way via machining simulations. The maximum utilization of five-axis machine tools depends upon the cutting system’s geometry, the configuration of the machine tool, and the workpiece’s location. In this research, a mathematical model has been developed to determine the workpiece’s feasible location in the five-axis machine tool for avoiding the number of iterations, which are usually performed to eliminate the global collision and axis limit errors. In this research, a generic arrangement of the five-axis machine tool has been selected. The mathematical model of post-processor has been developed by using kinematic modeling methods. The machine tool envelopes have been determined using the post-processor and axial limit. The tooltip reachable workspace is determined by incorporating the post-processor, optimal cutting system length, and machining envelope, thereby further developing an algorithm to determine the feasible workpiece setup parameters accurately. The algorithm’s application has been demonstrated using an example. Finally, the algorithm is validated for feasible workpiece setup parameters in a virtual environment. This research is highly applicable in the industry to eliminate the number of iterations performed for the suitable workpiece setup parameters.
The drastically changed thermal, mechanical, and chemical energies within the machined surface layer during hard machining tend to initiate microstructural alteration. In this paper, attention is paid to the introduction of thermodynamic potential to unravel the mechanism of microstructure evolution. First, the thermodynamic potential-based model expressed by the Helmholtz free energy was proposed for predicting the microstructure changes of serrated chip and the machined surface layer. Second, the proposed model was implemented into a validated finite element simulation model for cutting operation as a user-defined subroutine. In addition, the predicted irreversible thermodynamic state change in the deformation zones associated with grain size, which was reduced to less than 1 mm from the initial size of 1.5 mm on the machined surface, was provided for an in-depth explanation. The good consistency between the simulated results and experimental data validated the efficacy of the developed model. This research helps to provide further insight into the microstructure alteration during metal cutting.
Typically, the achievable positioning bandwidth for piezo-actuated nanopositioners is severely limited by the first, lightly-damped resonance. To overcome this issue, a variety of open- and closed-loop control techniques that commonly combine damping and tracking actions, have been reported in literature. However, in almost all these cases, the achievable closed-loop bandwidth is still limited by the original open-loop resonant frequency of the respective positioning axis. Shifting this resonance to a higher frequency would undoubtedly result in a wider bandwidth. However, such a shift typically entails a major mechanical redesign of the nanopositioner. The integral resonant control (IRC) has been reported earlier to demonstrate the significant performance enhancement, robustness to parameter uncertainty, gua-ranteed stability and design flexibility it affords. To further exploit the IRC scheme’s capabilities, this paper presents a method of actively shifting the resonant frequency of a nanopositioner’s axis, thereby delivering a wider closed-loop positioning bandwidth when controlled with the IRC scheme. The IRC damping control is augmented with a standard integral tracking controller to improve positioning accuracy. And both damping and tracking control parameters are analytically optimized to result in a Butterworth Filter mimicking pole-placement—maximally flat passband response. Experiments are conducted on a nanopositioner’s axis with an open-loop resonance at 508 Hz. It is shown that by employing the active resonance shifting, the closed-loop positioning bandwidth is increased from 73 to 576 Hz. Consequently, the root-mean-square tracking errors for a 100 Hz triangular trajectory are reduced by 93%.
Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.
This study proposes a gained switching-based discrete-time sliding mode control method to address the chattering issue in disturbed discrete-time systems, which suffer from various unknown uncertainties. Through the new structure of the designed reaching law, the proposed method can effectively increase the convergence speed while guaranteeing chattering-free control. The performance of controlling underactuated robotic systems can be further improved by the adoption of fuzzy logic to perform adaptive online hyper-parameter tuning. In addition, an underactuated robotic system with uncertainties is studied to validate the effectiveness of the proposed reaching law. Results reveal the dynamic performance and robustness of the proposed reaching law in the studied system and prove the proposed method’s superiority over other state-of-the-art methods.
With recent relevant publications on stochastic motion robots in Nature, Science, and other journals, research on such robots has gained increasing attention. However, theoretical and applied research on stochastic motion in the field of robotics and mechanisms face many challenges due to the uncertainty of stochastic motion. Currently, a large gap remains in the research of stochastic motion mechanism. In this study, a novel mechanism that can conduct probabilistic rolling is proposed to reach a designated position and achieve overlying movement over a particular area. The mechanism consists of a regular tetrahedron frame, a central node, and four connecting linear actuators. According to mobility and kinematic analyses, the mechanism can implement probabilistic rolling. Each rolling gait has three probable rolling directions, and the mechanism rolls in one of the three directions in probability. A kinematic simulation is conducted, and a control method is proposed on the basis of the moving path analysis. Furthermore, the mathematical principle of probabilistic rolling is revealed in terms of probability theory and statistics. Lastly, a prototype is fabricated. To achieve the rolling function, the design of the linear actuators is improved, and the extension ratio is increased from 0.58 to 1.13. Then, tests are conducted. In a 4 m2 test site, the mechanism makes 11 moves to reach the target position and covers 29.25% of the site.
Functional performance variations of products and systems are often used to measure the qualities of products and systems considering the changes in the design parameter values caused by uncertainties. A robust design approach has been developed in this research to minimize the functional performance variations considering the design parameter uncertainties by identifying the boundaries of the functional performance variations through optimization. In this work, a mathematical model is developed to describe the relationships among functional performance, design configurations and parameters, and design parameter uncertainties. A multi-level optimization model is established to identify: (1) The optimal design configuration, (2) the optimal values of design parameters, and (3) the boundaries of functional performance variations. Sensitivity analysis considering the impact of parameter uncertainties on functional performance variation boundaries has also been conducted. A case study on the design of a truss system has been conducted. Case study results show that the sensitivities of functional performance variation boundaries to the design parameter uncertainties can be reduced significantly using the new robust design approach.
As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.
The flat clinching process is attracting a growing attention in the joining field of lightweight materials because it avoids the geometric protrusion that appears in the conventional clinching process. In this paper, the effects of sheet thickness and material on the mechanical properties of the clinched joint were studied. Al1060 and Al2024 sheets with 2 mm thickness were employed to develop the clinched joint by using different material configurations, and Al1060 sheets with 2.5- and 1.5-mm thicknesses were used to produce the clinched joint by using different thickness configurations. The clinched joints using various sheet configurations were sectioned, and dimensional analysis was conducted. Cross-tensile and shearing tests were carried out to analyze the mechanical properties of the clinched joint, including tensile strength, shearing strength, and absorbed energy. In addition, the failure modes of the clinched joints were discussed. Results indicated that the clinched joint with a stiff top sheet had increased static strength regardless of the test type. The clinched joint with a thick top sheet demonstrated lower static strength than the joint with a thick bottom sheet in the cross-tensile test. However, this result was reversed in the shearing tests. The flat clinching process has a great potential in joining dissimilar and various thickness materials.
Pilot two-stage proportional valves are widely used in high-power hydraulic systems. For the purpose of improving the dynamic performance, reliability, and digitization of the traditional proportional valve, a novel two-stage proportional valve with a pilot digital flow distribution is proposed from the viewpoint of the dual nozzle-flapper valve’s working principle. In particular, the dual nozzle-flapper is decoupled by two high-speed on/off valves (HSVs). First, the working principle and mathematical model of the proposed valve are determined. Then, the influences of the control parameters (duty cycle and switching frequency) and structural parameters (fixed orifice’s diameter and main valve’s spring) on the main valve’s motion are analyzed on the basis of theory, simulation, and experiment. In addition, in optimizing the value of the fixed orifice’s diameter, a new design criterion that considers the maximum pressure sensitivity, flow controllability, and flow linearization is proposed to improve the balance between the effective displacement and the displacement fluctuation of the main valve. The new scheme is verified by simulations and experiments. Experimental results of the closed-loop displacement tracking have demonstrated that the delay time of the main valve is always within 3.5 ms under different working conditions, and the tracking error can be significantly reduced using the higher switching frequency. The amplitude–frequency experiments indicate that a −3 dB-frequency of the proposed valve can reach 9.5 Hz in the case of ±50% full scale and 15 Hz in the case of 0%–50% full scale. The values can be further improved by increasing the flow rate of the pilot HSV.