This paper presents the design and initial results of a project involving the robotic assembly of a large segmented structure. This project aims to develop an operator-guided semi-autonomous assembly process using industrial robots integrated with multiple sensors. The goal is to demonstrate the potential of robotic technology to reduce cycle time, enhance assembly quality, and improve worker ergonomics, as compared to the current manual or fixture-based approaches. The focus is primarily on the software framework which is composed of a collection of commercial and customized components for robot positioning, motion planning, low latency teleoperation, visualization and simulation. A foundation step of the implementation is safe teleoperation which allows the user to operate the robot without concern of collision or joint limits. The concept has been demonstrated in RobotStudio, the simulation environment for ABB robots, and a physical ABB robot. While some of the software is specific to the ABB industrial robot used in the project, the framework is readily adapted to other industrial robots that allow externally commanded motion.

The morphology of fibers (e.g., spatial uniformity and orientation) plays a decisive role in determining the material properties or fabrication quality of fiber-reinforced nanocomposites. The existing literature lacks a reliable and automatic fiber extraction method for morphology analysis based on the scanning electron microscope (SEM) images. This paper proposes four different methods, namely, the simple Hough Transform, opening method, partitioning Hough Transform and gradient based Hough Transform, to automatically identify the fibers from SEM images to expedite the morphology analysis. The performance of these methods are thoroughly evaluated and compared through simulation studies and real case studies.

It is well known that the chip morphology predictions in machining simulations depend on the separation criteria used for modeling chip formation. In this paper, we propose to use two different criteria for chip separation and serration along with the Johnson-Cook damage model. The threshold value for chip separation is determined from machining experiments using the methodologies discussed in Patel et al. [1]. In addition, two separate damage evolution laws for chip separation and serration are used. Our results indicate that the choice of the evolution law and the threshold values of G c used for chip separation and serration have a significant effect on chip shape and other field variables such as the equivalent plastic strain, cutting force, temperature, etc.

Computer-aided manufacturing (CAM) software allows for the generation of toolpaths for computer numerical control (CNC) machine tools and enables the creation of sophisticated parts that would not otherwise be possible with conventional manual machining methods. Voxel-based CAM is a recent approach to toolpath planning that enables creation of paths for parts that would be difficult to create with traditional CAM software. However, the use of voxel-based CAM necessitates the presence of powerful hardware (specifically, graphics processing units) in order to perform the necessary computations for creating toolpaths. The concepts of virtualization and desktop-as-a-service offer a promising solution to this challenge, as they allow for many users to access computer hardware that is hosted on a single server. This work investigates the performance impact caused by multiple simultaneous users on voxel-based CAM deployed in a virtualized environment. The implementation of a Python application for multi-user simulation on the virtualized platform is described and timing results gathered from a sequence of simulations are presented and analyzed as the number of users is varied. The results from these simulations demonstrate consistent operational times for a low number of simultaneous users before a period of high performance variation due to resource sharing.

This paper aims at providing a state-of-the-art review of an increasingly important class of joining technologies called solid-state welding. Among many other advantages such as low heat input, solid-state processes are particularly suitable for dissimilar materials joining. In this paper, major solid-state joining technologies such as the linear and rotary friction welding, friction stir welding, ultrasonic welding, impact welding, are reviewed, as well as diffusion and roll bonding. For each technology, the joining process is first depicted, followed by the process characterization, modeling and simulation, monitoring/diagnostics/NDE, and ended with concluding remarks. A discussion section is provided after reviewing all the technologies on the common critical factors that affect the solid-state processes such as the joining mechanisms, chemical and materials compatibility, surface properties, and process conditions. Finally, the future outlook is presented.

Ultrasonic welding is a solid-state joining process which uses ultrasonic vibration to join materials at relatively low temperatures. Ultrasonic powder consolidation is a derivative of the ultrasonic additive process which consolidates powder material into a dense solid block without melting. During ultrasonic powder consolidation process, metal powder under a compressive load is subjected to transverse ultrasonic vibrations resulting in a fully-dense consolidated product. While ultrasonic powder consolidation is employed in a wide variety of applications, the effect of critical process parameters on the bonding process of powder particles during consolidation is not clearly understood. This study uses a coupled thermo-mechanical finite element analysis technique to investigate the effect of critical process parameters including vibrational amplitude and base temperature on the stress, strain, and particle temperature distribution during the ultrasonic powder consolidation process. The study finds that during this process, the ultrasonically vibrating tool imparts cyclic vibratory shear stress on the particles. The simulation also revealed that the particle temperature just reaches the recrystallization point. Higher vibration amplitude imparted higher frictional heat on the particles, thereby aiding the consolidation process. The simulation study also showed indications of thermal softening and restricted grain boundary sliding during the ultrasonic powder consolidation process. The outcomes of this study can be used to further the industrial applications of ultrasonic powder consolidation process as well as other ultrasonic welding based processes.

Carbon fiber reinforced plastic (CFRP) are advanced engineering materials which are recognized as the most sought-after composite for several industrial applications including aerospace and automotive sectors. CFRP have superior physical and mechanical properties such as lightweight, high resilience, high-durability and high strength-to-weight ratio. CFRP composites stacked up with titanium to form multi-layered material stacks to enhance its load bearing capability. Traditional methods of stacking up CFRP and titanium involves using either high strength adhesives or rivets and bolts. The laminate structures joined by these methods often tend to fail during high load-bearing applications. Conventional metal welding technologies use high heat causing high thermal stresses and microstructural damages. Ultrasonic welding is a solid-state joining process, which has the capability of welding dissimilar materials at relatively low temperatures using ultrasonic vibration. Ultrasonic additive manufacturing (UAM) process is an ideal method to weld CFRP and Titanium. During the ultrasonic welding process, two dissimilar materials under a continuous static load are subjected to transverse ultrasonic vibrations, which results in high stress and friction between the two surfaces. This research focuses on the study of ultrasonically welding CFRP and Titanium stacks using UAM process. The study involves experimentation performed on an in-house built UAM setup. Finite element analysis is performed to understand the distribution stresses and strains during the UAM process. In this study, CFRP and Titanium layers are successfully welded using UAM process without causing any melting or significant heating. The finite element analysis study revealed that during UAM process, CFRP/Titanium stacks are subject to repeated cyclic shear stress reversals resulting in a strong weld joint. The stress-strain diagram during the process showed a considerable increase in plastic strain during the UAM process. The outcomes of this study can be used to further the industrial applications of the ultrasonic additive process as well as other ultrasonic welding based processes involving dissimilar materials.

In micro machining processes, achieving the required accuracy and surface quality in the first attempt is of utmost importance. Because, it is challenging to perform a secondary operation in a microscaled structure due to re-positioning inaccuracies. In the micro grinding process, geometrically deviated structures are common due to the low depth of cuts compared to the size of abrasive grits. Moreover, abrasive grits random distribution on the tool surface is also a factor. In most of the cases, the number of grits participating in the grinding process is countable, unlike macro grinding process. Hence, the position of every grit on the micro tool surface is important. In the micro grinding process, geometrically deviated structures are common due to the low depth of cuts compared to the size of abrasive grits. In this case, the depth of cut values will be less than the size of the abrasive grits. In that case, the abrasive grits located at the tool bottom surface highly influence the quality of the component produced. In the present work, a simulation study has been performed for micro grinding process to understand the influence of tool topography on the micro channel geometrical deviations, especially when the depth of cut is less than the size of the grits. Simulation results showed the importance of the number of grits, their protrusion heights and radial positions on the tool bottom surface at small depths of cuts to minimize the geometrical deviations.

In this paper, a Coupled Eulerian Lagrangian (CEL) finite element model (FEM) was developed to simulate the friction stir spot welding (FSSW) of commercial pure copper. Through simulations results, the paper presents and discusses the effect of FSSW process parameters; namely rotational speed, plunging rate and dwell time, on the developed temperatures and their distribution within the workpiece as well as material flow and deformation. Model validation showed a good agreement between predicted temperature history and the experiment one, with a maximum error of 6%. Furthermore, the predicted formation of flash was also found in good agreement with the experiment with an error of only 7%. Simulation results predicted peak temperature and plastic strain among all studied welding conditions were 920 K and 3.5 respectively at 1200 rpm rotational speed, 20 mm/min plunging rate and 4 seconds dwell time, which is approximately 70% of the melting point of pure copper.

Laser shock peening (LSP) is an advanced surface treatment technique that can extend fatigue life in metallic components by inducing near-surface compressive residual stresses. In this study, LSP was implemented to induce compressive residual stresses and modify material properties of selective laser melted (SLM) aluminum A357 specimens. An initial hypothesis on the effect of LSP during tension testing was formulated and tested using finite element simulation. The hypothesis was that, due to the LSP-induced tensile residual stress field in the middle of the specimen cross sections, yielding was expected to initiate in this region. True stress-strain curves of two as-built (AB) and two laser shock peened samples were obtained through transverse tensile tests. The single explicit analysis using time dependent damping (SEATD) technique was used to simulate LSP process utilizing Johnson-Cook (J-C) constitutive parameters. J-C parameters for the cast A357 alloy were used for preliminary study. This was followed by the simulation of the transverse tensile test. J-C parameters for SLM A357 alloy were then empirically estimated, and simulations were repeated accordingly. It was found that the specific LSP pattern induced tensile residual stresses along the edges as well as the middle of the test specimen’s cross-section. Axial residual stress and yield strength profiles along three different paths on specimen’s cross-section were compared and yield regions were investigated. This supported the initial hypothesis, but also provided for a more detailed understanding of actual tensile test failure in the specific SLM A357 specimens for the given LSP treatment. In addition, the same LSP treatment on SLM A357 alloy resulted in lower magnitude of compressive residual stress than for cast A357 aluminum alloy.

Liquid Assisted Laser Beam Micromachining (LA-LBMM) process is advanced machining process which can overcome the limitations of traditional laser beam machining processes. LA-LBMM process uses a layer of a liquid medium such as water above the substrate surface during the application of laser beam. During LA-LBMM process, the liquid medium is used both in static mode in which the water is still or in a dynamic mode in which the water flows over the substrate with a specific velocity. Experimental studies on LA-LBMM process have shown that the cavity machined has a better surface finish due to a reduction in the amount of re-deposition and recast material. While LA-LBMM process promises significant improvement in laser-based micromachining applications, the process mechanisms involved in LA-LBMM process is not well understood. In the past, finite element simulation studies on LA-LBMM process is studied which could only find the temperature distribution on the substrate during machining. A clear understanding of the role of water medium during the LA-LBMM process is lacking. This research involves the use of Molecular Dynamics (MD) simulation technique to investigate the complex and dynamic mechanisms involved in the LA-LBMM process both in static and dynamic mode. The results of the MD simulation are compared with those of Laser Beam Micromachining (LBMM). The study revealed that machining during LA-LBMM process showed higher removal compared with LBMM process. The LA-LBMM process in dynamic mode showed lesser material removal compared with static mode as the flowing water carrying the heat away from the machining zone. Formation of nanoscale bubbles along with shockwave propagation is observed during the simulation of LA-LBMM process. The findings of this study provide further insights to strengthen the knowledge base of LA-LBMM process.

An Ultrasonic Powder Consolidation is an additive manufacturing technique that utilizes high-frequency vibrations to consolidate micro/nano powder materials to fully dense and near to net-shaped parts. Unlike traditional powder consolidation techniques such as sintering, shock wave-based and pressure-based processes, the consolidation during Ultrasonic Powder Consolidation process happens at relatively low temperatures and pressures within few seconds or less. Ultrasonic Powder Consolidation process presents several inherent advantages including low power consumption, low cost and zero thermal stresses on the consolidated parts. Experimental studies have shown that Ultrasonic Powder Consolidation process is capable of successfully consolidating powders of metals and metal-matrix composites. While Ultrasonic Powder Consolidation process promises several potential applications, the mechanism of bond formation between the consolidated metal powders is not completely understood. This research uses Molecular Dynamics simulation technique to investigate the underlying bond formation and consolidation mechanisms involved in Ultrasonic Powder Consolidation process. The research also explores the effects of critical process parameters including vibration frequency, amplitude and initial temperature on the quality of the consolidated part. The study found that high-frequency vibrations cause high interfacial stresses resulting in acoustic softening and high plastic deformation of the nanoparticles. The study revealed that the overall atomistic temperature does not exceed the melting point of the material. The study also found that the vibration amplitude and frequency played a significant role in the consolidation process. Finally, the simulation study showed that the high-frequency vibration leads to large plastic deformations at ultra-high shear strain rates causing the interfacial atoms to interlock with each other resulting in high densification and consolidation. The results of this study would augment the ongoing experimental studies on Ultrasonic Powder Consolidation process which would help realize the promised potentials of this low temperature – low-pressure consolidation technique.

Roll diameter surface deviations can generate significant strip flatness defects in the cold rolling of thin gauge metal sheet. The resolution of roll profile deviations can vary between 0.001 and 0.01 mm. In such a high-fidelity contact problem, lack of availability of the measured data, and high computational cost in using the data in simulations, make it very difficult to predict the effects of such deviations. Nonetheless, an understanding of how high-fidelity roll profile deviations can lead to rolled sheet flatness defects is very important since such defects may be unrecoverable. This paper evaluates the ability of a simplified mixed finite element roll-stack model to predict contact behavior effects on the rolled sheet created by high-fidelity roll profile deviations. The simplified finite element method combines 3D Timoshenko beam elements with Winkler elastic foundations. The beam elements are used to capture bending and shear deformation, while the Winkler foundations are tested for their ability to accurately and efficiently capture the high-fidelity flattening-type contact behavior. Results for a simple roll and plate contact case study indicate favorable comparison with the results obtained from a large-scale commercial finite element simulation, and yet the methods requires a small fraction of the associated computing time and memory. The work also offers significant insights into the sheet flatness defects that can arise in cold rolling because of low magnitude but high fidelity roll diameter machining errors.

Minimum Quantity Lubrication or MQL is an increasingly used technique for metal cutting operations and it has become an attractive alternative for machining parts at big scale production. However, fully lubricated conditions are still in use for machining special materials so that surface finish, tool wear, and temperature distribution levels remain at optimum levels. On the other hand, dry condition machining is in use as well although with some restraint due to issues with material burr, surface roughness, and tool wear. The main purpose of this work is to analyze the effects of cutting fluid flow rate, its application mechanisms, and cutting speed on surface roughness and establish the lowest possible cutting fluid flow rate that yields to minimum surface roughness (Ra). To achieve the objective, a set of experiments was performed using a Computer Numerical Control (CNC) lathe instrumented with a Kistler 9121 dynamometer and a customized cutting fluid application system to obtain coefficients of friction and cutting forces. Finally, a previously 2D finite element analysis (FEA) simulation from Akbar et al. [1] is applied and compared to experimental results to find out if the cutting force can be predicted. A first regression model that correlates cutting force and surface roughness is posed, so that FEA simulation can be implemented to predict the final surface roughness. AISI 4140 machinery steel in annealed condition is used to carry out the simulated and experimental work.

A new method, which accurately predicts cutting force in ball end milling considering cutting edge around center web, has been proposed. The new method accurately calculates the uncut chip thickness, which is required to estimate the cutting force by the instantaneous rigid force model. In the instantaneous rigid force model, the uncut chip thickness is generally calculated on the cutting edge in each minute disk element piled up along the tool axis. However, the orientation of tool cutting edge of ball end mill is different from that of square end mill. Therefore, for the ball end mill, the uncut chip thickness cannot be calculated accurately in the minute disk element, especially around the center web. Then, this study proposes a method to calculate the uncut chip thickness along the vector connecting the center of the ball and the cutting edge. The proposed method can reduce the estimation error of the uncut chip thickness especially around the center web compared with the previous method. Our study also realizes to calculate the uncut chip thickness discretely by using voxel model and detecting the removal voxels in each minute tool rotation angle, in which the relative relationship between a cutting edge and a workpiece, which changes dynamically during tool rotation. A cutting experiment with the ball end mill was conducted in order to validate the proposed method. The results showed that the error between the measured and predicted cutting forces can be reduced by the proposed method compared with the previous method.

Selective Laser Melting (SLM) has been a major subject of study in the field of powder bed additive manufacturing (AM) process. It is desired to know the melt pool size and the associated thermal gradient during the powder melting process. However, there are challenges associated with accurately measuring the melt pool size as a whole by experiment alone. Therefore, the combination of experimental and numerical study may help analyze the melt pool shape in a better way. In this study, a 3D powder scale model using volume of fluid (VOF) approach has been developed using ANSYS FLUENT. A temperature dependent material property is defined and then volumetric heat source is applied to melt the powder particles. The single track results obtained from the simulation are compared with the experiment and the results show that single track width predicted by the simulation is in good agreement with the experimental counterpart. The predicted track width is within 10% error.

One of the limitations of commercially available metal Additive Manufacturing (AM) processes is the minimum feature size most processes can achieve. A proposed solution to bridge this gap is microscale selective laser sintering (μ-SLS). The advent of this process creates a need for models which are able to predict the structural properties of sintered parts. While there are currently a number of good SLS models, the majority of these models predict sintering as a melting process, which is accurate for microparticles. However, when particles tend to the nanoscale, sintering becomes a diffusion process dominated by grain boundary and surface diffusion between particles. As such, this paper presents an approach to model sintering by tracking the diffusion between nanoparticles on a bed scale. Phase Field Modeling (PFM) is used in this study to track the evolution of particles undergoing sintering. Part properties such as relative density, porosity, and shrinkage are then calculated from the results of the PFM simulations. These results are compared to experimental data gotten from a Thermogravimetric Analysis done on dried copper nanoparticle inks, and the simulation constants are calibrated to match physical properties.

3D additive manufacturing, namely 3D printing, has been increasingly needed in the fabrication of biological materials and devices. Compared to traditional fabrication, direct 3D digital transformation simplifies the manufacturing process and enhances capability in geometric fabrication. In this paper, we demonstrated a rapid and low-cost 3D printing approach for “lego” assembly of micro-structured parts as an electro-transfection device. Electro-transfection is an essential equipment for engineering and regulating cell biological functions. Nevertheless, existing platforms are mainly employed to monolayer cell suspensions in vitro , which showed more failures for translating into tissues and in vivo systems constituted by 3D cells. The knowledge regarding the three-dimensional electric transport and distribution in a tissue microenvironment is lacking. In order to bridge the gap, we assembled PDMS parts molded from 3D-printed molds as the 3D-cell culture chamber, which connects arrays of perfusion channels and electrodes. Such design allows spatial and temporal control of electric field uniformly across a large volume of 3D cells (10 5 ∼10 6 cells). Most importantly, multi-dimensional electric frequency scanning creates local oscillation, which can enhance mass transport and electroporation for improving transfection efficiency. The COMSOL electrostatic simulation was employed for proof of concept of 3D electric field distribution and transport in this “lego” assembled electro-transfection device, which builds the foundation for engineering 3D-cultured cells and tissues.

Condition-based maintenance (CBM) is important to improve production system performance because it is capable to effectively prevent costly equipment failures. However, CBM usually has to stop machines for maintenance during operation and this may severely impede the normal production. This paper establishes a real-time CBM decision making method to minimize the negative impact of CBM stoppage events in a multistage manufacturing system. The method utilizes an event-based analysis method to estimate the permanent production loss resulted from a CBM event. An online control algorithm is introduced to effectively explore the optimal CBM control options. Simulation case studies are performed to validate the event-based CBM decision making method.

Industrial Cloud Robotics (ICR), with the characteristics of resource sharing, lower cost and convenient access, etc., can realize the knowledge interaction and coordination among cloud Robotics (CR) through the knowledge sharing mechanism. However, the current researches mainly focus on the knowledge sharing of service-oriented robots and the knowledge updating of a single robot. The interaction and collaboration among robots in a cloud environment still have challenges, such as the improper updating of knowledge, the inconvenience of online data processing and the inflexibility of sharing mechanism. In addition, the industrial robot (IR) also lacks a well-developed knowledge management framework in order to facilitate the knowledge evolution of industrial robots. In this paper, a knowledge evolution mechanism of ICR based on the approach of knowledge acquisition - interactive sharing - iterative updating is established, and a novel architecture of ICR knowledge sharing is also developed. Moreover, the semantic knowledge in the robot system can encapsulate knowledge of manufacturing tasks, robot model and scheme decision into the cloud manufacturing process. As new manufacturing tasks arrived, the robot platform downloads task-oriented knowledge models from the cloud service platform, and then selects the optimal service composition and updates the cloud knowledge by simulation iterations. Finally, the feasibility and effectiveness of the proposed architecture and approaches are demonstrated through the case studies.