The Cooling Rate Effect on Graphene Synthesis Using Low Pressure Chemical Vapor Deposition

Low-pressure chemical vapor deposition (LPCVD) is the most efficient method to synthesize large-scale, high-quality graphene for many potential applications such as flexible electronics, solar cells, and separation membranes. The quality of LPCVD is affected by process variables including methane/hydrogen (CH4/H2) ratio, time, pressure, temperature, and cooling rate. The cooling rate has been recognized as one of the most important process variables affecting the amount of carbon source, nucleation, reaction time, and thus the quality of the LPCVD. In this research, we investigate the effect of cooling rate on the quality of graphene synthesize by changing the cooling rate and the gas feeding time. Graphene coverage is measured by Raman mapping. It is found that fast cooling rate leads to decreased carbon source reaction time, which in turn results in higher coverage by monolayer graphene. The temperature-dependent gas feeding time corresponding to different cooling rates can be used to properly supply the carbon source onto the copper surface, also leading to a higher graphene coverage.

An OSGi-Based Adaptation Access of Machine Tools in the Cloud Manufacturing Environment

Machine tools (MTs) are the core manufacturing resources for discrete manufacturing enterprises. In the cloud manufacturing environment, MTs are massive, heterogeneous, widely dispersed and highly autonomous, which makes it difficult for cloud manufacturing mode to be deeply applied to support the networked collaboration operation among manufacturing enterprises. Realizing universal access and cloud application of various MTs is an essential prerequisite to solve the above problem. In this paper, an OSGi-based adaptation access method of MTs is proposed. First, the MTs information description model in the cloud manufacturing environment is built. Then, an OSGi-based adaptation access framework of MTs is constructed, and key enabling technologies, including machine tool information acquisition and processing, Bundle and Subsystem construction, are studied. Finally, an application case is conducted to verify the effectiveness and feasibility of the proposed method.

Knowledge Recommendation System for Human-Robot Collaborative Disassembly Using Knowledge Graph

In recent years, more and more attention has been paid to Human-Robot Collaborative Disassembly (HRCD) in the field of industrial remanufacturing. Compared with the traditional manufacturing, HRCD helps to improve the manufacturing flexibility with considering the manufacturing efficiency. In HRCD, knowledge could be obtained from the disassembly process and then provides useful information for the operator and robots to execute their disassembly tasks. Afterwards, a crucial point is to establish a knowledge-based system to facilitate the interaction between human operators and industrial robots. In this context, a knowledge recommendation system based on knowledge graph is proposed to effectively support Human-Robot Collaboration (HRC) in disassembly. A disassembly knowledge graph is constructed to organize and manage the knowledge in the process of HRCD. After that, based on this, a knowledge recommendation procedure is proposed to recommend disassembly knowledge for the operator. Finally, the case study demonstrates that the developed system can effectively acquire, manage and visualize the related knowledge of HRCD, and then assist the human operator to complete the disassembly task by knowledge recommendation, thus improving the efficiency of collaborative disassembly. This system could be used in the human-robot collaboration disassembly process for the operators to provide convenient knowledge recommendation service.

Two-Dimensional Image Based Product Connector Recognition for Robotic Disassembly in Remanufacturing

Remanufacturing has received increasing attention for environmental protection and resource conservation considerations. Disassembly is a crucial step in remanufacturing, is always done manually which is inefficient while robotic disassembly can improve the efficiency of the disassembly. Aiming at the problem of product connector recognition during the robotic disassembly process, we analyze the template matching and feature matching principles based on two-dimensional images. To reduce the computational complexity of traditional template matching, a stepwise search strategy combining coarse and fine is proposed. Based on this a product connector recognition algorithm based on fast template matching and a product connector recognition algorithm based on feature matching is designed. Taking bolts and hexagon nuts as examples, the recognition effects of the two algorithms are compared and analyzed.

Precision Evaluation of NeXus, a Custom Multi-Robot System for Microsystem Integration

Industrial robots, as mature and high-efficient equipment, have been applied to various fields, such as vehicle manufacturing, product packaging, painting, welding, and medical surgery. Most industrial robots are only operating in their own workspace, in other words, they are floor-mounted at the fixed locations. Just some industrial robots are wall-mounted on one linear rail based on the applications. Sometimes, industrial robots are ceiling-mounted on an X-Y gantry to perform upside-down manipulation tasks. The main objective of this paper is to describe the NeXus, a custom robotic system that has been designed for precision microsystem integration tasks with such a gantry. The system tasks include assembly, bonding, and 3D printing of sensor arrays, solar cells, and microrobotic prototypes. The NeXus consists of a custom designed frame, providing structural rigidity, a large overhead X-Y gantry carrying a 6 degrees of freedom industrial robot, and several other precision positioners and processes. We focus here on the design and precision evaluation of the overhead ceiling-mounted industrial robot of NeXus and its supporting frame. We first simulated the behavior of the frame using Finite Element Analysis (FEA), then experimentally evaluated the pose repeatability of the robot end-effector using three different types of sensors. Results verify that the performance objectives of the design are achieved.

Evolution of Interfacial Microstructure During Resistance Spot Welding of Cu and Al With Ni-P Coating

Dissimilar materials of copper (Cu) to aluminum (Al) with nickel-phosphorus (Ni-P) coatings were joined using resistance spot welding. The Ni-P coatings were electroless plated on the Al surfaces to eliminate the formation of brittle Cu-Al intermetallic compounds (IMCs) at the faying interface between Cu and Al. Three welding schedules with various heat input were employed to produce different interfacial microstructure. The evolution of interfaces in terms of phase constitution, elemental distribution and defects (gaps and voids) was characterized and the formation mechanisms were elucidated. During the welding process, the bonding between Cu and Ni-P forms through solid-state diffusion, while the faster diffusion rate of Cu relative to Ni and P atoms promotes the generation of sub-micron voids. As the heat input increases, gaps at the Cu/Ni-P interface diminish accompanied by increase of sub-micron voids. A moderate schedule helps to remove the gaps and inhibits the void formation. An Al3Ni layer and nanovoids were found around the interface of Ni-P/Al. The increased heat input decreases the grain size of Al3Ni at the interface by eutectic remelting and increases the nanovoids by enhanced nanoscale Kirkendall effect.

Reaction-Diffusion Modeling of Photopolymerization During Femtosecond Projection Two-Photon Lithography

Two-photon lithography (TPL) is a polymerization-based direct laser writing process that is capable of fabricating arbitrarily complex three-dimensional (3D) structures with submicron features. Traditional TPL techniques have limited scalability due to the slow point-by-point serial writing scheme. The femtosecond projection TPL (FP-TPL) technique increases printing rate by a thousand times by enabling layer-by-layer parallelization. However, parallelization alters the time and the length scales of the underlying polymerization process. It is therefore challenging to apply the models of serial TPL to accurately predict process outcome during FP-TPL. To solve this problem, we have generated a finite element model of the polymerization process on the time and length scales relevant to FP-TPL. The model is based on the reaction-diffusion mechanism that underlies polymerization. We have applied this model to predict the geometry of nanowires printed under a variety of conditions and compared these predictions against empirical data. Our model accurately predicts the nanowire widths. However, accuracy of aspect ratio prediction is hindered by uncertain values of the chemical properties of the photopolymer. Nevertheless, our results demonstrate that the reaction-diffusion model can accurately capture the effect of controllable parameters on FP-TPL process outcome and can therefore be used for process control and optimization.

Molecular Dynamics Modeling the Nano-Identation of Titanium

Titanium alloys’ excellent mechanical and physical properties make it the most popular material widely used in aerospace, medical, nuclear and other significant industries. The study of titanium alloys mainly focused on the macroscopic mechanical mechanism. However, very few researches addressed the nanostructure of titanium alloys and its mechanical response in Nano-machining due to the difficulty to perform and characterize nano-machining experiment. Compared with nano-machining, nano-indentation is easier to characterize the microscopic plasticity of titanium alloys. This research presents a nano-indentation molecular dynamics model in titanium to address its microstructure alteration, plastic deformation and other mechanical response at the atomistic scale. Based on the molecular dynamics model, a complete nano-indentation cycle, including the loading and unloading stages, is performed by applying Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). The plastic deformation mechanism of nano-indentation of titanium with a rigid diamond ball tip was studied under different indentation velocities. At the same time, the influence of different environment temperatures on the nano-plastic deformation of titanium is analyzed under the condition of constant indentation velocity. The simulation results show that the Young’s modulus of pure titanium calculated based on nano-indentation is about 110GPa, which is very close to the experimental results. The results also show that the mechanical behavior of titanium can be divided into three stages: elastic stage, yield stage and plastic stage during the nano-indentation process. In addition, indentation speed has influence on phase transitions and nucleation of dislocations in the range of 0.1–1.0 Å/ps.

Simulation and Characterization of Nanoparticle Thermal Conductivity for a Microscale Selective Laser Sintering System

Additive Manufacturing (AM) technologies are often restricted by the minimum feature size of parts they can repeatably build. The microscale selective laser sintering (μ-SLS) process, which is capable of producing single micron resolution parts, addresses this issue directly. However, the unwanted dissipation of heat within the powder bed of a μ-SLS device during laser sintering is a primary source of error that limits the minimum feature size of the producible parts. A particle scale thermal model is needed to characterize the thermal properties of the nanoparticles undergoing sintering and allow for the prediction of heat affected zones (HAZ) and the improvement of final part quality. Thus, this paper presents a method for the determination of the effective thermal conductivity of metal nanoparticle beds in a microscale selective laser sintering process using finite element simulations in ANSYS. CAD models of nanoparticle groups at various timesteps during sintering are developed from Phase Field Modeling (PFM) output data, and steady state thermal simulations are performed on each group. The complete simulation framework developed in this work is adaptable to particle groups of variable sizes and geometric arrangements. Results from the thermal models are used to estimate the thermal conductivity of the copper nanoparticles as a function of sintering duration.

MSEC2021 Front Matter

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