Showing papers in "Advances in Manufacturing in 2021"

Digital twin-based sustainable intelligent manufacturing: a review

Abstract: As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

Abstract: In this paper, a bioinspired path planning approach for mobile robots is proposed. The approach is based on the sparrow search algorithm, which is an intelligent optimization algorithm inspired by the group wisdom, foraging, and anti-predation behaviors of sparrows. To obtain high-quality paths and fast convergence, an improved sparrow search algorithm is proposed with three new strategies. First, a linear path strategy is proposed, which can transform the polyline in the corner of the path into a smooth line, to enable the robot to reach the goal faster. Then, a new neighborhood search strategy is used to improve the fitness value of the global optimal individual, and a new position update function is used to speed up the convergence. Finally, a new multi-index comprehensive evaluation method is designed to evaluate these algorithms. Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-of-the-art studies.

Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Abstract: Machining of shape memory alloys (SMAs) without losing the shape memory effect could immensely extend their applications. Herein, the wire electric discharge machining process was used to machine NiTi—a shape memory alloy. The experimental methodology was designed using a Box-Behnken design approach of the response surface methodology. The effects of input variables including pulse on time, pulse off time, and current were investigated on the material removal rate, surface roughness, and microhardness. ANOVA tests were performed to check the robustness of the generated empirical models. Optimization of the process parameters was performed using a newly formulated, highly efficient heat transfer search algorithm. Validation tests were conducted and extended for analyzing the retention of the shape memory effect of the machined surface by differential scanning calorimetry. In addition, 2D and 3D Pareto curves were generated that indicated the trade-offs between the selected output variables during the simultaneous output variables using the multi-objective heat transfer search algorithm. The optimization route yielded encouraging results. Single objective optimization yielded a maximum material removal rate of 1.49 mm3/s, maximum microhardness 462.52 HVN, and minimum surface roughness 0.11 µm. The Pareto curves showed conflicting effects during the wire electric discharge machining of the shape memory alloy and presented a set of optimal non-dominant solutions. The shape memory alloy machined using the optimized process parameters even indicated a shape memory effect similar to that of the starting base material.

Precision micro-milling process: state of the art

Abstract: Micro-milling is a precision manufacturing process with broad applications across the biomedical, electronics, aerospace, and aeronautical industries owing to its versatility, capability, economy, and efficiency in a wide range of materials. In particular, the micro-milling process is highly suitable for very precise and accurate machining of mold prototypes with high aspect ratios in the microdomain, as well as for rapid micro-texturing and micro-patterning, which will have great importance in the near future in bio-implant manufacturing. This is particularly true for machining of typical difficult-to-machine materials commonly found in both the mold and orthopedic implant industries. However, inherent physical process constraints of machining arise as macro-milling is scaled down to the microdomain. This leads to some physical phenomena during micro-milling such as chip formation, size effect, and process instabilities. These dynamic physical process phenomena are introduced and discussed in detail. It is important to remember that these phenomena have multifactor effects during micro-milling, which must be taken into consideration to maximize the performance of the process. The most recent research on the micro-milling process inputs is discussed in detail from a process output perspective to determine how the process as a whole can be improved. Additionally, newly developed processes that combine conventional micro-milling with other technologies, which have great prospects in reducing the issues related to the physical process phenomena, are also introduced. Finally, the major applications of this versatile precision machining process are discussed with important insights into how the application range may be further broadened.

On the application of additive manufacturing methods for auxetic structures: a review

Abstract: Auxetic structures are a special class of structural components that exhibit a negative Poisson's ratio (NPR) because of their constituent materials, internal microstructure, or structural geometry. To realize such structures, specialized manufacturing processes are required to achieve a dimensional accuracy, reduction of material wastage, and a quicker fabrication. Hence, additive manufacturing (AM) techniques play a pivotal role in this context. AM is a layer-wise manufacturing process and builds the structure as per the designed geometry with appreciable precision and accuracy. Hence, it is extremely beneficial to fabricate auxetic structures using AM, which is otherwise a tedious and expensive task. In this study, a detailed discussion of the various AM techniques used in the fabrication of auxetic structures is presented. The advancements and advantages put forward by the AM domain have offered a plethora of opportunities for the fabrication and development of unconventional structures. Therefore, the authors have attempted to provide a meaningful encapsulation and a detailed discussion of the most recent of such advancements pertaining to auxetic structures. The article opens with a brief history of the growth of auxetic materials and later auxetic structures. Subsequently, discussions centering on the different AM techniques employed for the realization of auxetic structures are conducted. The basic principle, advantages, and disadvantages of these processes are discussed to provide an in-depth understanding of the current level of research. Furthermore, the performance of some of the prominent auxetic structures realized through these methods is discussed to compare their benefits and shortcomings. In addition, the influences of geometric and process parameters on such structures are evaluated through a comprehensive review to assess their feasibility for the later-mentioned applications. Finally, valuable insights into the applications, limitations, and prospects of AM for auxetic structures are provided to enable the readers to gauge the vitality of such manufacturing as a production method.

Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network

Abstract: Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) rely on the quality of manufactured products and the process capability. Currently, the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard, and variations are controlled without significantly affecting the functioning of processes, machines, and product structures. This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts. In this study, a new class of experimental design techniques for integrated second-order definitive screening design (DSD) and an artificial neural network (ANN) are proposed for designing experiments to evaluate and predict the effects of six important operating variables. By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts, the time consumption and number of complex experiments are reduced considerably in this study. The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements. The results indicate that the slice thickness, part print direction, and number of perimeters significantly affect the percentage of length difference, whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap, bead width, number of perimeters, and part print direction. Furthermore, the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

Abstract: The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

A comprehensive review on residual stresses in turning

Abstract: Residual stresses induced during turning processes can affect the quality and performance of machined products, depending on its direction and magnitude. Residual stresses can be highly detrimental as they can lead to creeping, fatigue, and stress corrosion cracking. The final state of residual stresses in a workpiece depends on its material as well as the cutting-tool configuration such as tool geometry/coating, cooling and wear conditions, and process parameters including the cutting speed, depth-of-cut and feed-rate. However, there have been disagreements in some literatures regarding influences of the above-mentioned factors on residual stresses due to different cutting conditions, tool parameters and workpiece materials used in the specific investigations. This review paper categorizes different methods in experimental, numerical and analytical approaches employed for determining induced residual stresses and their relationships with cutting conditions in a turning process. Discussion is presented for the effects of different cutting conditions and parameters on the final residual stresses state.

Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: a review

Abstract: Additive manufacturing (AM) technologies are currently employed for the manufacturing of completely functional parts and have gained the attention of high-technology industries such as the aerospace, automotive, and biomedical fields. This is mainly due to their advantages in terms of low material waste and high productivity, particularly owing to the flexibility in the geometries that can be generated. In the tooling industry, specifically the manufacturing of dies and molds, AM technologies enable the generation of complex shapes, internal cooling channels, the repair of damaged dies and molds, and an improved performance of dies and molds employing multiple AM materials. In the present paper, a review of AM processes and materials applied in the tooling industry for the generation of dies and molds is addressed. AM technologies used for tooling applications and the characteristics of the materials employed in this industry are first presented. In addition, the most relevant state-of-the-art approaches are analyzed with respect to the process parameters and microstructural and mechanical properties in the processing of high-performance tooling materials used in AM processes. Concretely, studies on the AM of ferrous (maraging steels and H13 steel alloy) and non-ferrous (stellite alloys and WC alloys) tooling alloys are also analyzed.

Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing

Abstract: Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

Abstract: During the actual high-speed machining process, it is necessary to reduce the energy consumption and improve the machined surface quality. However, the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments. Herein, a novel intelligent system is proposed for prediction and optimization. A novel adaptive neuro-fuzzy inference system (NANFIS) is proposed to predict the energy consumption and surface quality. In the NANFIS model, the membership functions of the inputs are expanded into: membership superior and membership inferior. The membership functions are varied based on the machining theory. The inputs of the NANFIS model are cutting parameters, and the outputs are the machining performances. For optimization, the optimal cutting parameters are obtained using the improved particle swarm optimization (IPSO) algorithm and NANFIS models. Additionally, the IPSO algorithm as a learning algorithm is used to train the NANFIS models. The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron. The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2% and 93.4%, respectively. The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models. Based on the IPSO algorithm and NANFIS models, the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency. It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes, thereby enabling sustainable and intelligent manufacturing.

Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process

Abstract: High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control (CNC) machining. Quality monitoring and prediction is of great importance to assure high-quality or zero defect production. In this work, we consider roughness parameter Ra, profile deviation Pt and roundness deviation RONt of the machined products by a lathe. Intrinsically, these three parameters are much related to the machine spindle parameters of preload, temperature, and rotations per minute (RPMs), while in this paper, spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters. Power spectral density (PSD) based feature extraction, the method to generate compact and well-correlated features, is proposed in details in this paper. Using the efficient features, neural network based machine learning technique turns out to be able to result in high prediction accuracy with R2 score of 0.92 for roughness, 0.86 for profile, and 0.95 for roundness.

Analysis and optimization of sustainable machining of AISI O1 tool steel by the wire-EDM process

Abstract: Wire electrical discharge machining (wire-EDM) is an energy-intensive process, and its success relies on a correct selection of cutting parameters. It is vital to optimize energy consumption, along with productivity and quality. This experimental study optimized three parameters in wire-EDM: pulse-on time, servo voltage, and voltage concerning machining time, electric power, total energy consumption, surface roughness, and material removal rate. Two different plate thicknesses (15.88 mm and 25.4 mm) were machined. An orthogonal array, signal-to-noise ratio, and means graphs, and an analysis of variance (ANOVA), determine the effects and contribution of cutting parameters on responses. Pulse-on time is the most significant factor for almost all variables, with a percentage of contribution higher than 50%. Multi-objective optimization is conducted to accomplish a concurrent decrease in all variables. A case study is proposed to compute carbon dioxide (CO2) tons and electricity cost in wire-EDM, using cutting parameters from multi-objective optimization and starting values commonly employed to cut that tool steel. A sustainable manufacturing approach reduced 5.91% of the electricity cost and CO2 tons when machining the thin plate, and these responses were diminished by 14.09% for the thicker plate. Therefore, it is possible to enhance the sustainability of the process without decreasing its productivity and quality.

Performance evaluation of nanofluid-based minimum quantity lubrication grinding of Ni-Cr alloy under the influence of CuO nanoparticles

Abstract: In machining processes, researchers are actively engaged in exploring minimum quantity lubrication (MQL) as a possible alternative to traditional flood cooling owing to economic and ecological concerns. The search for ecologically safe lubricants has attracted the attention of scientists looking to use vegetable oil as a lubricant. The nanofluid MQL technique with biodegradable oils as the base is a relatively new method with the potential to replace mineral oils. In the present study, the grinding of Inconel-718 alloy was investigated using nanofluid MQL (NFMQL) with biodegradable oils as the base. Nanofluids are composed by dispersing 0.5% (mass fraction) and 1% (mass fraction) of CuO nanoparticles in vegetable oil. The surface morphology, G-ratio, forces, and grinding energy were examined under pure MQL, NFMQL, and dry and flood lubrication conditions. The experimental results indicated that the nanofluid MQL significantly improved the machining performance. Owing to the polishing and rolling effect of nanoparticles on the tool work interface, a surface finish under a 0.5% (mass fraction) nanofluid was found to be better than pure MQL-dry and flood lubrication conditions. The NFMQL technique with 1% (mass fraction) CuO nanoparticles with palm oil as the base helped in achieving a better evacuation of chips from the grinding zone, leading to a better surface finish with a high material removal rate along with less energy consumption compared to flood and dry grinding.

Investigation into keyhole-weld pool dynamic behaviors based on HDR vision sensing of real-time K-TIG welding process through a steel/glass sandwich

Abstract: To obtain a deep insight into keyhole tungsten inert gas welding, it is necessary to observe the dynamic behavior of the weld pool and keyhole. In this study, based on the steel/glass sandwich and high dynamic range camera, a vision system is developed and the keyhole-weld pool profiles are captured during the real-time welding process. Then, to analyze the dynamic behavior of the weld pool and keyhole, an image processing algorithm is proposed to extract the compression depth of the weld pool and the geometric parameters of the keyhole from the captured images. After considering the variations of these parameters over time, it was found that the front and rear lengths of the keyhole were dynamically adjusted internally and had opposite trends according to the real-time welding status while the length of the keyhole was in a quasi-steady state. The proposed vision-based observation method lays a solid foundation for studying the weld forming process and improving keyhole tungsten inert gas welding.

Surface integrity evolution of machined NiTi shape memory alloys after turning process

Abstract: Owing to their shape memory effect and pseudoelasticity, NiTi shape memory alloys (SMAs) are widely used as functional materials. Mechanical processes particularly influence the final formation of the product owing to thermal softening and work-hardening effects. Surface integrity is an intermediate bridge between the machining parameter and performance of the product. In this study, experiments were carried out on turning NiTi SMAs at different cutting speeds, where surface integrity characteristics were analyzed. The results show that a higher cutting speed of 125 m/min is required to turn NiTi SMAs based on the evaluation of surface integrity. The degree of work hardening is higher at 15 m/min. Consequently, as a primary effect, work hardening appears on the plastic deformation of the machined samples, leading to dislocations and defects. As the cutting speed increases, the thermal softening effect exceeds work hardening and creates a smoother surface. A stress-induced martensitic transformation is considered during the turning process, but this transformation is reversed to an austenite from the X-ray diffraction (XRD) results. According to the differential scanning calorimetry (DSC) curves, the phase state and phase transformation are less influenced by machining. Subsequently, the functional properties of NiTi-SMAs are less affected by machining.

A novel method for workpiece deformation prediction by amending initial residual stress based on SVR-GA

Abstract: High-precision manufactured thin-walled pure copper components are widely adopted in precision physics experiments, which require workpieces with extremely high machining accuracy. Double-sided lapping is an ultra-precision machining method for obtaining high-precision surfaces. However, during double-sided lapping, the residual stress of the components tends to cause deformation, which affects the machining accuracy of the workpiece. Therefore, a model to predict workpiece deformation derived from residual stress in actual manufacturing should be established. To improve the accuracy of the prediction model, a novel method for predicting workpiece deformation by amending the initial residual stress slightly based on the support vector regression (SVR) and genetic algorithm (GA) is proposed. Firstly, a finite element method model is established for double-sided lapping to understand the deformation process. Subsequently, the SVR model is utilized to construct the relationship between residual stress and deformation. Next, the GA is used to determine the best residual stress adjustment value based on the actual deformation of the workpiece. Finally, the method is validated via double-sided lapping experiments.

Effect of ultrasonic impact treatment on the surface integrity of nickel alloy 718

Abstract: Ultrasonic impact treatment (UIT) is a type of surface strengthening technology that can improve the fatigue properties of materials by improving the surface quality, residual stress, and other aspects. In this study, the influence of ultrasonic impact parameters on the surface integrity of nickel alloy 718 was studied. The micro stress concentration caused by the surface morphology was also explored. The cosine and exponential decay functions were used to fit and characterize the distribution of residual stress and work hardening in the surface material. The results showed that the feed rate had the greatest influence on surface roughness, stress concentration, and surface residual stress. It was not appropriate to evaluate the surface hardening effect only by the number of impacts per unit area, the ultrasonic impact parameters such as feed speed and pre extrusion depth should also be considered. The grain refinement was obvious after UIT. The multi-objective optimization of machining parameters was performed with the objective of surface stress concentration and residual stress. A surface with a smaller surface stress concentration factor and larger compressive residual stress can be obtained simultaneously using medium linear velocity, medium pre extrusion depth, and smaller feed rate.

Experimental and numerical investigation of the abrasive waterjet machining of aluminum-7075-T6 for aerospace applications

Abstract: The machining of hard-to-cut materials with a high degree of precision and high surface quality is one of the most critical considerations when fabricating various state-of-the-art engineered components. In this investigation, a comprehensive three-dimensional model was developed and numerically simulated to predict kerf profiles and material removal rates while drilling the aluminum-7075-T6 aerospace alloy. Kerf profile and material removal prediction involved three stages: jet dynamic flow modeling, abrasive particle tracking, and erosion rate prediction . Experimental investigations were conducted to validate the developed model. The results indicate that the jet dynamic characteristics and flow of abrasive particles alter the kerf profiles, where the top kerf diameter increases with increasing jet pressure and standoff distance. The kerf depth and hole aspect ratio increase with jet pressure, but decrease with standoff distance and machining time. Cross-sectional profiles were characterized by progressive edge rounding and parabolic shapes. Defects can be minimized by utilizing high jet pressure and small standoff distance. The material removal rate increases with increasing jet pressure, abrasive particle size, and exposure time, but decreases with increasing standoff distance.

The influence of ultrasonic vibration on parts properties during incremental sheet forming

Abstract: The integration of ultrasonic vibration into sheet forming process can significantly reduce the forming force and bring benefits including the enhancement of surface quality, the enhancement of formability and the reduction of spring-back. However, the influencing mechanisms of the high-frequency vibration on parts properties during the incremental sheet forming (ISF) process are not well known, preventing a more efficient forming system. This paper comprehensively investigates the effects of different process parameters (vibration amplitude, step-down size, rotation speed and forming angle) on the micro-hardness, minimum thickness, forming limit and residual stress of the formed parts. First, a series of truncated pyramids were formed with an experimental platform designed for the ultrasonic-assisted incremental sheet forming. Then, micro-hardness tests, minimum thickness measurements and residual stress tests were performed for the formed parts. The results showed that the surface micro-hardness of the formed part was reduced since the vibration stress induced by the ultrasonic vibration within the material which eliminated the original internal stress. The superimposed ultrasonic vibration can effectively uniform the residual stress and thickness distribution, and improve the forming limit in the case of the small deformation rate. In addition, through the tensile fracture analysis of the formed part, it is shown that the elongation of material is improved and the elastic modulus and hardening index are decreased. The findings of the present work lay the foundation for a better integration of the ultrasonic vibration system into the incremental sheet forming process.

Machine-vision-based electrode wear analysis for closed loop wire EDM process control

Abstract: The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining (EDM) process control. Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures. Such process interruptions are undesirable because they affect cost efficiency, surface quality, and process sustainability. The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear. Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images. In the proposed method, the images are pre-processed and enhanced to obtain a binary image that is used to compute the wire wear ratio (WWR). The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time, servo voltage, and wire feed rate. The algorithm successfully predicted wire breakage events. In addition, the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits, thereby preventing wire breakage interruptions.

Arduino-based low-cost electrical load tracking system with a long-range mesh network

Abstract: A system that combines the advantage of the long-range (LoRa) communication method and the structural characteristics of a mesh network for an LoRa mesh network-based wireless electrical load tracking system is proposed. The system demonstrates considerable potential in reducing data loss due to environmental factors in far-field wireless energy monitoring. The proposed system can automatically control the function of each node by confirming the data source and eventually adjust the system structure according to real-time monitoring data without manual intervention. To further improve the sustainability of the system in outdoor environments, a standby equipment is designed to automatically ensure the normal operation of the system when the hardware of the base station fails. Our system is based on the Arduino board, which lowers the production cost and provides a simple manufacturing process. After conducting a long-term monitoring of a near-field smart manufacturing process in South Korea and the far-field energy consumption of rural households in Tanzania, we have proven that the system can be implemented in most regions, neither confined to a specific geographic location nor limited by the development of local infrastructure. This system comprises a smart framework that improves the quality of energy monitoring. Finally, the proposed big-data-technology-based power supply policy offers a new approach for prolonging the power supply time of off-grid power plants, thereby providing a guideline for more rural areas with limited power sources to utilize uninterrupted electricity.

Modeling the density gradient of 3D nanofiber scaffolds fabricated by divergence electrospinning

Abstract: Following recent insights on structure-cell-function interactions and the critical role of the extracellular matrix (ECM), the latest biofabrication approaches have increasingly focused on designing materials with biomimetic microarchitecture. Divergence electrospinning is a novel fabrication method for three-dimensional (3D) nanofiber scaffolds. It is introduced to produce 3D nanofiber mats that have numerous applications in regenerative medicine and tissue engineering. One of the most important characteristics of 3D nanofiber mats is the density gradient. This study provides a statistical analysis and response surface modeling framework based on experimental data to evaluate the manner by which the geometric designs of double-bevel collectors influence the fiber density gradient. Specifically, variance of analysis and sensitivity analysis were performed to identify parameters that had significant effects, and a response surface model embedded with seven location indicators was developed to predict the spatial distribution of fiber density for different collector designs. It was concluded that the collector height, bevel angle, and their interactions were significant factors influencing the density gradient. This study revealed the sensitivity of system configuration and provided an optimization tool for process controllability of microstructure gradients.

Microstructural evolution and mechanical properties of 55NiCrMoV7 hot-work die steel during quenching and tempering treatments

Abstract: 55NiCrMoV7 hot-work die steel is mainly used to manufacture heavy forgings in the fields of aerospace and automobile. This study aims to clarify the effects of heat treatment on the microstructural evolution and mechanical properties of the steel, in order to find out an optimal heat treatment scheme to obtain an excellent balance of strength, ductility and toughness. The steel was quenched at temperature from 790 °C to 910 °C followed by tempering treatments of 100–650 °C for 5 h. The mechanical property tests were carried out by tensile, impact toughness and hardness. Optical microscope (OM), scanning electron microscope (SEM) and transmission electron microscope (TEM) were used to observe the austenite grains, lath martensite, carbides and fracture morphology. The results show that the quenching temperature mainly influences the austenite grain size and the volume fraction of undissolved carbides (UCs), while the tempering temperature mainly influences the size and morphology of the martensite with a body centered cubic (BCC) and the carbides with a face centered cubic (FCC). The mechanical properties of the steel, including yield and tensile strength, ductility, impact toughness and hardness, get an excellent balance at a quenching range of 850–870 °C. As the tempering temperature increases, the yield and tensile strength and hardness decrease, while the ductility and impact toughness increase. These variation trends can be further verified by fracture SEM observation and analysis. Combined with a macro-micro coupled finite element (MMFE) modeling technique, the cooling rate, microstructural evolution and yield strength of the steel were predicted and compared with the tested data.

Novel evaluation method for metal workability during cross wedge rolling process

Abstract: This study presents a novel method using a disk-like sample to assess the workability of metal during the cross wedge rolling (CWR) process. Using this method, we can quantitatively evaluate the moment destruction which occurs at the center of the sample during CWR. In this study, 45 steel was selected to demonstrate the proposed method. Firstly, we designed a model for the tools and sample, conducted finite element simulations to analyze the distribution regulations of metal flow, stress, and strain, and evaluated the relationship between the damage and moving distance of the tool during the forming process. Then, we obtained the optimal deformation temperature range, rolling speed, and geometry parameters for the tool. Finally, experiments were conducted from 20 °C to 1 200 °C to verify the accuracy of the developed model. It was demonstrated that the model was significantly accurate in accessing the workability of 45 steel in the CWR process. The proposed method could be generalized to investigate the CWR process for other materials, such as aluminum alloys, superalloys, titanium alloys, etc.

Improving maintenance efficiency and safety through a human-centric approach

Abstract: This paper presents an adaptive human-machine interface (HMI) that can provide appropriate sets of digital maintenance information and guidance to an operator during maintenance. It takes into consideration the expertise level of the operator and the maintenance context and progress. The proposed human-centric methodology considers the heart rate, intention, and expertise level of the operator, which can be captured using sensors during maintenance. A set of rules is formulated based on the sensor data to infer the state of the operator during a maintenance task. Based on the operator state, the adaptive HMI can augment the operator’s senses using a scheme that combines visual, audio, and haptic guidance cues during maintenance to enhance the operator’s ability to perceive information and perform maintenance tasks. Various schemes of visual, audio, and haptic cues are developed based on a comparison of the best practices obtained from experienced operators.

Parameter identification and blanking simulations of DP1000 and Al6082-T6 using Lemaitre damage model

Abstract: This work provides numerical and experimental investigations of blanking process, where the shear-enhanced Lemaitre’s damage model is fully characterized and successfully applied in blanking process to predict the cutting force and cutting edge geometry under different blanking process parameters. Advanced high strength steel DP1000 and an aluminum alloy Al6082-T6 are selected for series of experiments. To obtain the damage parameters in Lemaitre’s damage model the flat rectangular notched specimens tensile test was conducted and the inverse parameter identification procedure was performed. For characterizing the crack closure parameter h in the shear enhanced Lemaitre’s damage model, an in-plane torsion test with novel specimen design was conducted. The finite element model (FEM) of this test was established with the minimum mesh size of 0.01 mm which was consistent with the minimum mesh size in the shear zone of the FEM for blanking process simulation. The longitudinal strain distributions of four kinds of initial notch radius or central-hole specimen were measured and compared with simulation results to validate the FEMs for these four tests. Deformation analysis of blanking of a circular work piece also was performed under three clearances. The effects of blanking conditions on sheared part morphology were detected. Stress triaxiality distribution of the blank sheet was revealed taking advantage of the successfully established FEM. The availability of the testing method and the determination method of the parameters was investigated.

Material removal at atomic and close-to-atomic scale by high-energy photon: a case study using atomistic-continuum method

Abstract: Extreme ultraviolet (EUV) light plays an important role in various fields such as material characterization and semiconductor manufacturing. It is also a potential approach in material fabrication at atomic and close-to-atomic scales. However, the material removal mechanism has not yet been fully understood. This paper studies the interaction of a femtosecond EUV pulse with monocrystalline silicon using molecular dynamics (MD) coupled with a two-temperature model (TTM). The photoionization mechanism, an important process occurring at a short wavelength, is introduced to the simulation and the results are compared with those of the traditional model. Dynamical processes including photoionization, atom desorption, and laser-induced shockwave are discussed under various fluencies, and the possibility of single atomic layer removal is explored. Results show that photoionization and the corresponding bond breakage are the main reasons of atom desorption. The method developed can be further employed to investigate the interaction between high-energy photons and the material at moderate fluence.

Novel design and composition optimization of self-lubricating functionally graded cemented tungsten carbide cutting tool material for dry machining

Abstract: The functionally graded cemented tungsten carbide (FGCC) is a suitable material choice for cutting tool applications due to balanced hardness and fracture toughness. The presence of cobalt and CaF2 composition gradient in FGCC may enhance mechanical as well as antifriction properties. Therefore, structural design of self-lubricating FGCC was proposed using Power law composition gradient model and thermal residual stresses (TRSs) as a key parameter. Wherein, S. Suresh and A. Mortensen model was adopted for estimation of TRS, and optimum composition gradient was identified at Power law exponent n = 2. The designed material displayed compressive and tensile TRS at surface and core respectively; subsequently fabricated by spark plasma sintering and characterized via scanning electron microscope (SEM), indentation method. The agreement between experimental and analytical values of TRS demonstrated the effectiveness of intended design model in the composition optimization of self-lubricating FGCC. This work will be helpful in implementation of dry machining for clean and green manufacturing.

Three-dimensional modeling and reconstructive change of residual stress during machining process of milling, polishing, heat treatment, vibratory finishing, and shot peening of fan blade

Abstract: Residual stress during the machining process has always been a research hotspot, especially for aero-engine blades. The three-dimensional modeling and reconstructive laws of residual stress among various processes in the machining process of the fan blade is studied in this paper. The fan blades of Ti-6Al-4V are targeted for milling, polishing, heat treatment, vibratory finishing, and shot peening. The surface and subsurface residual stress after each process is measured by the X-ray diffraction method. The distribution of the surface and subsurface residual stress is analyzed. The Rational Taylor surface function and cosine decay function are used to fit the characteristic function of the residual stress distribution, and the empirical formula with high fitting accuracy is obtained. The value and distribution of surface and subsurface residual stress vary greatly due to different processing techniques. The reconstructive change of the surface and subsurface residual stress of the blade in each process intuitively shows the change of the residual stress between the processes, which has a high reference significance for the research on the residual stress of the blade processing and the optimization of the entire blade process.