Carbon Nanotube Reinforced Fused Deposition Modeling Using Microwave Irradiation

Additive manufacturing (AM) is a class of manufacturing processes where material is deposited in a layer-by-layer fashion to fabricate a three-dimensional part directly from a computer-aided design model. With a current market share of 44%, thermoplastic-based additive manufacturing such as fused deposition modeling (FDM) is a prevailing technology. A key challenge for AM parts (especially for parts made by FDM) in engineering applications is the weak inter-layer adhesion. The lack of bonding between filaments usually results in delamination and mechanical failure. To address this challenge, this study embedded carbon nanotubes into acrylonitrile butadiene styrene (ABS) thermoplastics via a filament extrusion process. The vigorous response of carbon nanotubes to microwave irradiation, leading to the release of a large amount of heat, is used to melt the ABS thermoplastic matrix adjacent to carbon nanotubes within a very short time period. This treatment is found to enhance the inter-layer adhesion without bulk heating to deform the 3D printed parts. Tensile and flexural tests were performed to evaluation the effects of microwave irradiation on mechanical properties of the specimens made by FDM. Scanning electron microscopic (SEM) images were taken to characterize the fracture surfaces of tensile test specimens. The actual carbon nanotube contents in the filaments were measured by conducting thermogravimetric analysis (TGA). The effects of microwave irradiation on the electrical resistivity of the filament were also reported.

One Touch Workpiece Verification System for CNC Machining Using a Low-Cost Computer Vision Approach

While CNC programmers have powerful tools to develop optimized toolpaths and machining plans, these efforts can be wholly undermined by something as simple as human operator error during fixturing. This project addresses that potential operator error with a computer vision approach to provide coarse, closed-loop control between fixturing and machining processes. Prior to starting the machining cycle, a sensor suite detects the geometry that is currently fixtured using computer vision algorithms and compare this geometry to a CAD reference. If the detected and reference geometries are not similar, the machining cycle will not start, and an alarm will be raised. The outcome of this project is the proof of concept of a low-cost, machine/controller agnostic solution that is applied to CNC milling machines. The Workpiece Verification System (WVS) prototype implemented in this work cost a total of $100 to build, and all of the processing is performed on the self-contained platform. This solution has additional applications beyond milling that the authors are exploring.

Loading-Unloading Cycles of 3D-Printing Built Bi-Material Structures With Ceramic and Elastomer

This paper studies the loading-unloading behaviors of a 3D-printing built bi-material structure consisting of an open-cellular plaster frame filled with silicone. The combination of the plaster (ceramic phase) and silicone (elastomer phase) is hypothesized to possess a non-linearly elastic property and a better ductility. Four-point bending test with programmed cycles of preceding deformations was conducted. The results show that there exists a linear-nonlinear transition when the bending deflection is around 2 mm in the first cycle bending. As the cycle proceeds, this transition is found at the maximum deflection of the previous cycle; meanwhile, the bending stiffness degrades. It is believed that the occurrence of micro-cracks inside the plaster frame is the mechanism behind the phenomenon. The ductile silicone provides a strong network suppressing the abrupt crack propagation in a brittle material. The effects of the frame structure and plaster-silicone ratio were also compared. A high plaster content and large cell size tend to have a higher stiffness and obvious linear to non-linear transition while it also has more significant stiffness degradation.

Thermal Analysis on Directional Freezing of Nano Aqueous Suspensions in Graphene Aerogel 3D Printing Process

This paper presented a novel 3D printing technique to fabricate graphene aerogel based on directional freezing. Thermal property of the graphene ink is one of key factors in this process which affects the material integrity and morphology as well as process efficiency and reliability. The major objective of this paper is to develop a heat transfer model to efficiently and reliably predict the temperature evolution of the printed materials and the waiting time between the layers for any input geometry. The simulation results show that the input geometry significantly affects the temperature evolution and waiting time. The proposed technique can not only improve the process efficiency and reliability, it can also serve as a flexible tool to predict and control the microstructure of the printed graphene aerogels.

A Hybrid RF-GA Approach to Bottleneck Machine Diagnosis and Suggestion in Parallel Machine Job-Shop Scheduling

In this paper, a hybrid Random Forest-Genetic Algorithm approach to detect and solve bottleneck machine problems in parallel machine Job-shop scheduling is developed with the aim of minimizing the makespan and the additional cost. The drawbacks of the existing methods for diagnosing bottlenecks is that they either do not consider the severity of the bottleneck or they do not consider the existence of multiple bottlenecks. In the existing models for solving bottlenecks, the cost is not considered as an objective function and only shifting of bottlenecks is utilized to solve the bottleneck machine problem. This approach is not feasible if the maximum capacity of the workshop has been reached. In this paper, a Random Forest classification model is utilized to diagnose bottleneck machine with different severity where the severity of the machines on the shop floor can either be none, low, medium, or high. Due to the lack of historical data, the Random Forest algorithm is trained using bottleneck data generated by simulating several identical parallel machine Job-shop scheduling problems. The trained Random Forest algorithm is then used in conjunction with Genetic Algorithm for finding the optimal actions to be taken for the most severe bottlenecks machines in order to reduce the makespan and the additional cost by optimizing the number of additional parallel machines to be utilized and overtime hours for the most severe bottleneck machines. The two objectives, makespan and additional cost, are combined into a single objective value by the use of weight values. These weight values depend on severity of the most severe bottleneck machine. If the bottleneck severity is “high” then makespan has a higher weight value than cost, if the severity is “medium” then both cost and makespan are weighed equally, and if the severity is “low” then cost has a higher weight value than makespan. In order to show the validity of the proposed approach it is used for diagnosing and solving the bottleneck problems in three different identical parallel machine Job-shop scheduling case studies 1. 3 jobs with 6 machines 2. 5 jobs with 9 machines and 3. 5 jobs with 12 machines. By utilizing the proposed approach the makespan and cost were reduced by 19.0%, 24.5% and 25.4% in case studies 1, 2, and 3 respectively. The results show that the trained Random Forest algorithm was able to correctly diagnose the bottleneck machines and their severity and Genetic algorithm was able to find the optimal number of additional hours and additional machines for the most severe bottleneck machines on the shop floor.

Identifying Uncertainty in Laser Powder Bed Fusion Models

A limitation frequently encountered in additive manufacturing (AM) models is a lack of indication about their precision and accuracy. Often overlooked, information on model uncertainty is required for validation of AM models, qualification of AM-produced parts, and uncertainty management. This paper presents a discussion on the origin and propagation of uncertainty in Laser Powder Bed Fusion (L-PBF) models. Four sources of uncertainty are identified: modeling assumptions, unknown simulation parameters, numerical approximations, and measurement error in calibration data. Techniques to quantify uncertainty in each source are presented briefly, along with estimation algorithms to diminish prediction uncertainty with the incorporation of online measurements. The methods are illustrated with a case study based on a transient, stochastic thermal model designed for melt pool width predictions. Model uncertainty is quantified for single track experiments and the effect of online estimation in overhanging structures is studied via simulation. The application of these concepts to estimation and control of the L-PBF process is suggested.

Design and Modeling of a Microscale Selective Laser Sintering System

The development of micro and nanoscale additive manufacturing methods in metals and ceramics is important for many applications in the aerospace, medical device, and electronics industries. Unfortunately, most commercially available metal additive manufacturing tools have feature-size resolutions of greater than 100 μm, which is too large to precisely control the microstructure of the parts they produce. A few research-grade metal additive manufacturing tools do exist, but their build rate is generally too slow for commercial applications. Therefore, this paper presents a new microscale selective laser sintering (μ-SLS) that can be used to improve the minimum feature-size resolution of metal additively manufactured parts by up to two orders of magnitude, while still maintaining the throughput of traditional additive manufacturing processes. In order to achieve this goal, several innovative design features like the use of (1) ultra-fast lasers, (2) a micro-mirror based optical system, (3) nanoscale powders, and (4) a precision spreader mechanism, have been implemented. The micro-SLS system is capable of achieving build rates of approximately 1 cm3/hr while achieving a feature-size resolution of approximately 1 μm. This paper will also present new molecular scale models that have been developed for the micro-SLS to quantify and certify the micro-SLS build process. Modeling of the micro-SLS process is challenging, because most macroscale models of the SLS process contain assumptions that are no longer valid when the size of the particles that are being sintered is smaller than the wavelength of the laser being used to sinter them. Therefore, in modeling the micro-SLS process we must account for the wave nature of light and can no longer rely on the ray tracing models commonly used to model the SLS process. Also, heat transfer in the micro-SLS process is dominated by near-field radiation due to the diffraction of the light off the nanoparticles in the powder bed and the ultrafast lasers that are used in the micro-SLS system. This means that the assumptions of heat transfer by conduction and far-field radiation in the macroscale SLS systems are no longer valid for the micro-SLS system. Finally, the agglomeration of nanoparticles in the powder bed must be accurately modeled in order to precisely predict the formation of defects in the final parts produced. Overall, the goal of this modeling effort is to be able to predict the quality of a part produced using any given processing conditions, in order to produce parts that are “born certified” and do not need to be tested post fabrication.

Simulation of Cross-Sectional Geometry During Laser Powder Deposition of Tall Thin-Walled and Thick-Walled Features

A model of the laser powder deposition (LPD) process is presented, which predicts the cross-sectional geometry of parts that are made up of thin-walled and thick-walled features, deposited via multiple passes. The model builds up the part shape incrementally by predicting the cross-section of a bead of material deposited on the part, updating part shape to reflect the added material, and repeating for each additional deposition pass. The effects of laser power and deposition speed are accounted for empirically, and the effect of nozzle stand-off distance is accounted for via a powder catchment model suitable for coaxial deposition nozzles. The model was calibrated via deposition experiments using stainless steel 316L powder and via measurement of nozzle characteristics. Validation tests showed that the powder catchment model captured the effect of nozzle stand-off distance on deposited bead size. Validation tests also showed that the model predicted the overall shapes of both thin-walled and thick-walled features, including rounding present at the edges of some thick-walled features. Using calibration data from short thick-walled depositions, the average error in predicted feature height, after ten layers, was 9.3% and 9.5% for thin-walled and thick-walled features, respectively. The model was also shown to predict the effects of using a step-up distance per layer that is too small, resulting in inefficient deposition, or too large, resulting in deposition failure after a few layers.

A Comparative Analysis Between High Speed Dynamic and Traditional Pocketing Toolpaths in Precision Milling Machines

Different types of toolpaths have been extensively studied with regards to different factors such as energy consumption and tool wear. However, toolpaths have been introduced recently, where high speeds and dynamic movements are combined to provide higher performance. The aim of this paper is to compare a spiral toolpath strategy, which has been studied previously with good results in energy consumption, with a high speed dynamic toolpath strategy, which combines helical and dynamic movements, with regards to energy consumption, tool wear and carbon emissions. Several advantages are identified with a high speed dynamic toolpath strategy over the typical spiral toolpath strategy in terms of tool wear, energy consumption and carbon emissions. The results show that the high speed dynamic toolpath is a better alternative for different milling operations such as slotting, pocketing, and face milling.