Advanced Virtual Manufacturing Lab for Research, Training, & Education

poster abstractThe research formed a base for innovative technology that was used to develop a product on its way to commercialization. The new product provides effective and integrated tool for training and education in advanced manufacturing. It is based on sound e-learning pedagogy and highly effective and integrated virtual reality learning environment

Defects, Process Parameters and Signatures for Online Monitoring and Control in Powder-Based Additive Manufacturing

Additive Manufacturing (AM) is a process that is based on manufacturing parts layer by layer in order to avoid any geometric limitation in terms of creating the desired design. In the early stages of AM development, the goal was just creating some prototypes to decrease the time of manufacturing assessment. But with metal-based AM, it is now possible to produce end-use parts. In powder-based AM, a designed part can be produced directly from the STL file (Standard Tessellation Language/ stereolithography) layer by layer by exerting a laser beam on a layer of powder with thickness between 20 μm and 100 μm to create a section of the part. The Achilles’ heel of this process is generation of some defects, which weaken the mechanical properties and in some cases, these defects may even lead to part failure under manufacturing. This prevents metal-based AM technology from spreading widely while limiting the repeatability and precision of the process. Online monitoring (OM) and intelligent control, which has been investigated prevalently in contemporary research, presents a feasible solution to the aformentioned issues, insofar as it monitors the generated defects during the process and eliminates them in real-time. In this regard, this paper reveals the most frequent and traceable defects which significantly affect quality matrices of the produced part in powder-based AM, predominately focusing on the Selective Laser Sintering (SLS) process. These defects are classified into “Geometry and Dimensions,” “Surface Quality (Finishing),” “Microstructure” and the defects leading to “Weak Mechanical Properties.” In addition, we introduce and classify the most important parameters, which can be monitored and controlled to avoid those defects. Furthermore, these parameters may be employed in some error handling strategies to remove the generated defects. We also introduce some signatures that can be monitored for adjusting the parameters into their optimum value instead of monitoring the defects directly

Simulate Turning Process using ANN, Predict Optimum Control Factors to achieve Minimum Surface Roughness

poster abstractAbstract Turning is a material removal process, a subtractive form of machining which is used to create parts of circular or rotational form of desired geometry/shape by removing unwanted material. Accuracy of any process depends on involvement of operational variables. The operating parameters that contribute to turning process are Cutting speed, Feed rate, Depth of cut. Vibrations, tool wear, tool life, surface finish and cutting forces etc are also in direct relation with values selected for process parameters. Hence to improve the efficiency of process and quality of the product it is necessary to control the process parameters. Surface roughness is the parameters with main focus, as it dictates the aesthetics and sometimes ergonomical characteristics of the product. The tests were carried out on AISI 4140 steel. 12 speed Jones and Lamson Lathe model was used for turning operation. The specimen with a diameter of 60mm, 500mm length and hardened 35 HRC is used. The tool used for this is one that is most commonly used for turning process DTGNR 163 C 0° Lead Angle 60° Triangle insert. It is product of Kennametal. Statistical Design of Experiments was used to reduce the total number of trials in order to save the time and resources without compromising the accuracy of prediction. These readings are used to train and validate the Neural Network. ANN is found to be very useful with simulations tasks which have complex and explicit relation between control factors and result of process. Neural Network was created using feed forward back propagation technique for simulation of the process using the Matlab Neural network toolbox. With assurance of accuracy of the predictive capabilities of the neural network, it was then used for optimization. Particle Swarm Optimization Algorithm, an evolutionary computation technique is used find out the optimum values of the input parameters to achieve the minimum surface roughness. The objective function used here is to minimize the surface roughness. Limits of the operational variables are used as constraints for developing the code for optimization algorithm. Keywords: Turning process, Surface roughness, Artificial Neural Network, Particle swarm optimization

A Thermomechanical Analysis of Conformal Cooling Channels in 3D Printed Plastic Injection Molds

Plastic injection molding is a versatile process, and a major part of the present plastic manufacturing industry. The traditional die design is limited to straight (drilled) cooling channels, which don't impart optimal thermal (or thermomechanical) performance. With the advent of additive manufacturing technology, injection molding tools with conformal cooling channels are now possible. However, optimum conformal channels based on thermomechanical performance are not found in the literature. This paper proposes a design methodology to generate optimized design configurations of such channels in plastic injection molds. The design of experiments (DOEs) technique is used to study the effect of the critical design parameters of conformal channels, as well as their cross-section geometries. In addition, designs for the "best" thermomechanical performance are identified. Finally, guidelines for selecting optimum design solutions given the plastic part thickness are provided

Investigation of Layer Based Thermal Behavior in Fused Deposition Modeling Process by Infrared Thermography

There are numerous research efforts that address the monitoring and control of additive manufacturing (AM) processes to improve part quality. Much less research exists on process monitoring and control of Fused Deposition Modeling (FDM). FDM is inherently a thermal process and thus, lends itself to being study by thermography. In this regard, there are various process parameters or process signatures such as built-bed temperature, temperature mapping of parts during deposition of layers, and the nozzle extrusion temperature that may monitor to optimize the quality of fabricated parts. In this work, we applied image based thermography layer by layer with the usage of an infrared camera to investigate the thermal behavior and thermal evolution of the FDM process for the standard samples printed by ABS filament. The combination of the layer based temperature profile plot and the temporal plot has been utilized to understand the temperature distribution and average temperature through the layers under fabrication. This information provides insights for potential modification of the scan strategy and optimization of process parameters in future research, based on the thermal evolution. Accordingly, this can reduce some frequent defects which have roots in thermal characteristics of the deposited layers and also, improve the surface quality and/or mechanical properties of the fabricated parts. In addition, this approach for monitoring the process will allow manufacturers to build, qualify, and certify parts with greater throughput and accelerate the proliferation of products into high-quality applications

Numerical simulation of aluminum extrusion using coated die

In aluminium extrusion, the life of the die tooling components is mainly limited by wear and fatigue. Therefore reliable predictions of the amount of wear and its distribution in dies are important factors for the die design. In this study the stress location and wear depth of the tooling components were calculated using finite element models incorporating the Archards wear model. A comparative study was conducted on an extrusion die without coating and with a bilayer (TiCN + Al2O3) chemical vapor deposition (CVD) coating. Stress distribution and the amount of wear were calculated. The results generated from the simulation would help predict the service life of the components through optimizing coating thickness

Heat Conduction and Geometry Topology Optimization of Support Structure in Laser-based Additive Manufacturing

Laser-based metal additive manufacturing technologies such as Selective Laser Sintering (SLS) and Selective Laser Melting (SLM) allow the fabrication of complex parts by selectively sintering or melting metallic powders layer by layer. Although elaborate features can be produced by these technologies, heat accumulation in overhangs leads to heat stress and warping, affecting the dimensional and geometrical accuracy of the part. This work introduces an approach to mitigate heat stress by minimizing the temperature gradient between the heat-accumulated zone in overhangs and the layers beneath. This is achieved by generating complex support structures that maintain the mechanical stability of the overhang and increase the heat conduction between these areas. The architecture of the complex support structures is obtained by maximizing heat conduction as an objective function to optimize the topology of support structure. This work examines the effect of various geometries on the objective function in order to select a suitable one to consume less material with almost same conduction. Ongoing work is the development of an experimental testbed for verification

Development of a Cone CVT by SDPD and Topology Optimization

The automotive industries have undergone a massive change in the last few decades. Nowadays, automotive industries and OEM manufacturers implement various innovative ideas to ensure the desired comfort while minimizing the cost, weight, and manufacturing time. Transmission system plays a major role in the aforementioned items. This paper aims to develop a conical roller with belt Continuously Variable Transmission (CVT) System by employing the System Driven Product Development (SDPD) approach and topology optimization of its traditional design. Furthermore, this paper explains the design steps of the CVT and its advantages and limitations compared with the other automatic transmission systems

Scanning Strategies in the PBF Process: A Critical Review

The powder-bed fusion (PBF) process is capable of producing near-fully dense metallic parts; however, various defects — particularly thermal abnormalities — can still be observed during the process. Some of these thermal defects — cracks, distortion, delamination of layers, and microporosity — cannot be removed by post-processing operations. The majority of these abnormalities are the result of residual stress, heat accumulation, lack of inter-track /inter-layer bonding, lack of powder fusion, or a combination of these factors. Modifying the scanning strategy (the topology of scanning tracks) can efficiently mitigate these abnormalities by adjusting the process parameters and adopting proper scanning patterns. The implementation of different scanning strategies significantly changes the ultimate quality of printed parts and manufacturing process lead time. Choosing a proper scanning strategy minimizes the residual stress and internal porosity, generates homogeneous microstructure, and avoids heat accumulation throughout the part during the printing process. In this work, we conducted a critical review of different scanning strategies, their pros and cons, limitations, and influence on the resulting properties of fabricated parts. Furthermore, we report the latest efforts for improvement of the current scanning strategies and introduce the-state-of-the-art strategies in the multi-laser PBF (ML-PBF) process. The insights provided here can assist scholars in evaluating existing scanning strategies and scanning patterns, and in identifying ways both to overcome scanning limitations and to modify them. On the other hand, it can assist manufacturers in selecting the best scanning strategies for their products based on their designs, demands, and resources

Experimental Study of Material Removal at Nanoscale

In order to develop nano-machining into a viable and efficient process, there is a need to achieve a better understand the relation between process parameters (such as feed, speed, and depth of cut) and resulting geometry. In this study, a comprehensive experimental parametric study was conducted to produce a database that is used to select proper machining conditions for guiding the fabrication of precise nano-geometries. The parametric studies conducted using AFM nanosize tips showed the following: normal forces for both nano-indentation and nano-scratching increase as the depth of cut increases. The indentation depth increases with tip speed, but the depth of scratch decrease with increasing tip speed. The width and depth of scratched groove also depend on the scratch angle. The recommended scratch angle is at 90°. The surface roughness increases with step over, especially when the step over is larger than the tip radius. The depth of cut also increases as the step over decreases