Browse Topic: Rapid prototyping
Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain
Fused Deposition Modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability, making it well-suited for use in industries such as footwear, automotive, and consumer goods. Hoses, gaskets, seals, external trim, and interior components are just a few of the many uses for thermoplastic polyurethanes (TPU) in the automobile industry. The objective of this study is to enhance the performance of Fused Deposition Modeling (FDM) by optimizing the parameters specifically for Thermoplastic Polyurethane (TPU) material. This will be achieved by employing a Taguchi-based Grey Relational Analysis (GRA) method. The researchers conducted experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses
Fused Deposition Modeling (FDM), a form of Additive Manufacturing (AM), has emerged as a groundbreaking technology for the production of complex shapes from a variety of materials. Acrylonitrile Butadiene Styrene (ABS) is an opaque thermoplastic that is frequently employed in additive manufacturing (AM) due to its affordability and user-friendliness. The purpose of this investigation is to enhance the FDM parameters for ABS material and develop predictive models that anticipate printing performance by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS). Through experimental trials, an investigation was conducted to evaluate the influence of critical FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including mechanical properties, surface polish, and dimensional accuracy. The utilization of design of experiments (DOE) methodology facilitated a systematic examination of parameters. A predictive model was
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings
Fused Deposition Modeling (FDM) is a widely recognized additive manufacturing method that is highly regarded for its ability to create complex structures using thermoplastic materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability. TPU has several applications, including automobile instrument panels, caster wheels, power tools, sports goods, medical equipment, drive belts, footwear, inflatable rafts, fire hoses, buffer weight tips, and a wide range of extruded film, sheet, and profile applications.. The primary objective of this study is to enhance the FDM parameters for TPU material and construct regression models that can accurately forecast printing performance. The study involved conducting experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses, including dimensional accuracy, surface quality, and mechanical
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized the manufacturing sector by enabling the production of complex geometries using various materials. Polylactic Acid (PLA) is a biodegradable thermoplastic often used in additive manufacturing (AM) because to its eco-friendliness, cost-effectiveness, and processing simplicity. This research seeks to enhance the parameters of Fused Deposition Modeling (FDM) for PLA material with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The researchers conducted experimental trials to investigate the influence of key FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on essential outcomes such as dimensional accuracy, surface quality, and mechanical qualities. The design of experiments (DOE) technique facilitated a systematic investigation of parameters. The TOPSIS method, a decision-making tool based on several
PEM electrolysis system has characteristic of excellent performance such as fast response, high electrolysis efficiency, compact design and wide adjustable power range. It provides a sustainable solution for the production of hydrogen, and is well suited to couple with renewable energy sources. In the development process of PEM electrolysis controller, this article originally applied the V-mode development process, including simulation modeling, RCP testing, and HIL testing, which can provide guidance in the practical application of electrolytic hydrogen production. In this paper, we present modeling and simulation study of PEM water electrolysis system. Model of electrolytic cell, hydrogen production subsystem and thermal management subsystem are constructed in Matlab/Simulink. Controller model was designed based on PI control strategy. A rapid prototyping controller with MPC5744 chip was used to develop the control system of electrolytic hydrogen production system. Hardware in the
Fused deposition modeling (FDM) is a rapidly growing additive manufacturing method employed for printing fiber-reinforced polymer composites. Nonetheless, the performance of printed parts is often constrained by inherent defects. This study investigates how the varying annealing parameter affects the tribological properties of FDM-produced polypropylene carbon fiber composites. The composite pin specimens were created in a standard size of 35 mm height and 12 mm diameter, based on the specifications of the tribometer pin holder. The impact of high-temperature annealing process parameters are explored, specifically annealing temperature and duration, while maintaining a fixed cooling rate. Two set of printed samples were taken for post-annealing at temperature of 85°C for 60 and 90 min, respectively. The tribological properties were evaluated using a dry pin-on-disc setup and examined both pre- (as-built) and post-annealing at temperature of 85°C for 60 and 90 min printed samples
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a highly promising method for creating intricate shapes using different materials. Polyethylene Terephthalate Glycol (PETG) is a highly utilized thermoplastic that is recognized for its exceptional strength, resistance to chemicals, and effortless processing. This study aims to optimize the process parameters of the FDM technique for PETG material using Taguchi Grey Relational Analysis (GRA). An empirical study was carried out to examine the impact of various FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on important outcome variables like dimensional accuracy, surface quality, and mechanical properties. The Taguchi method was used to systematically design a series of experiments, while GRA was used to optimize the process parameters and performance characteristics. The results unveiled the most effective parameter combinations for attaining
Additive Manufacturing (AM), specifically Fusion Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of complex structures using a wide range of materials. The objective of this study is to enhance the FDM process for Thermoplastic Polyurethane (TPU) material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study examines the influence of FDM parameters, such as layer height, nozzle temperature, and infill density, on important characteristics of the printing process, such as tensile strength, flexibility, and surface finish. The collection of experimental data is achieved by conducting systematic FDM printing trials that cover a variety of parameter combinations. The TOPSIS optimization method is utilized to determine the optimal parameter settings by evaluating each parameter combination against the ideal and anti-ideal solutions. This method determines the optimal parameter
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a revolutionary technology for creating intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly properties, affordability, and ease of use. The objective of this study is to optimize the FDM parameters for PLA material and create predictive models using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast printing performance. An investigation was carried out through experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes such as dimensional accuracy, surface finish, and mechanical properties. The utilization of design of experiments (DOE) methodology enabled a methodical exploration of parameters. A predictive model using ANFIS was created to
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly nature, affordability, and ease of processing. This study aims to optimize the parameters of Fused Deposition Modeling (FDM) for PLA material using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The researchers performed experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including dimensional accuracy, surface finish, and mechanical properties. The methodology of design of experiments (DOE) enabled a systematic exploration of parameters. The TOPSIS approach, a technique for making decisions
Smaller than a coin, this optical device could enable rapid prototyping on the go. Massachusetts Institute of Technology, Cambridge, MA Imagine a portable 3D printer you could hold in the palm of your hand. The tiny device could enable a user to rapidly create customized, low-cost objects on the go, like a fastener to repair a wobbly bicycle wheel or a component for a critical medical operation. Researchers from MIT and the University of Texas at Austin took a major step toward making this idea a reality by demonstrating the first chip-based 3D printer. Their proof-of-concept device consists of a single, millimeter-scale photonic chip that emits reconfigurable beams of light into a well of resin that cures into a solid shape when light strikes it.
Tohoku University Sendai, Japan
Additive manufacturing (AM) is a common way to make things faster in manufacturing era today. A mix of polypropylene (PP) and carbon fiber (CF) blended filament is strong and bonded well. Fused deposition modeling (FDM) is a common way to make things. For this research, made the test samples using a mix of PP and CF filament through FDM printer by varying infill speed of 40 meters per sec 50 meters per sec and 60 meters per sec in sequence. The tested these samples on a tribometer testing machine that slides them against a surface with different forces (from 5 to 20 N) and speeds (from 1 to 4 meters per sec). The findings of the study revealed a consistent linear increase in both wear rate and coefficient of friction across every sample analyzed. Nevertheless, noteworthy variations emerged when evaluating the samples subjected to the 40m/s infill speed test. Specifically, these particular samples exhibited notably lower wear rates and coefficients of friction compared to the remaining
This article explores the impact of As-built versus annealed Fused Deposition Modeling (FDM) on the mechanical properties of test samples fabricated from two distinct materials: Polyamide 6 (PA6) and PA6 with carbon fiber filament. Employing the FDM technique, these samples were meticulously produced, with significant process parameters maintained at optimal values. Two sets of printed specimens were prepared for examination, one composed of PA6 and the other of PA6 with carbon fiber (CF) reinforcement. The first set was subjected to mechanical testing in its As-built condition, while the second set underwent an annealing process utilizing a muffle furnace. The annealing reduces internal stresses, enhances interlayer adhesion, and promotes crystallinity. For both the sent samples exposed to comprehensive assessments to evaluate various mechanical performance attributes, including hardness, impact strength, tensile strength and flexural strength. The results of this study elucidate that
A digital twin is a virtual model that accurately imitates a physical asset. This can be as complex as an entire vehicle, a subsystem, and down to a small functioning component. The digital twin has a level of fidelity that aligns to the goals of the project team. The usage of a digital twin inside a digital engineering (DE) ecosystem permits architecture and design decisions for optimized product behavior, performance, and interactions. This paper demonstrates a methodology to incorporate the digital twin concept from requirement analysis, low fidelity feature level simulation, rapid prototypes running inside a System Integration Lab, and high fidelity virtual prototypes executing in an entirely virtual environment.
The Software Production Factory (SPF) is a cyber physical construct of computers, hardware and software integrated together to serve as an ideation and rapid prototyping environment. SPF is a virtual dynamic environment to analyze requirements, architecture, and design, assess trade-offs, test Ground Vehicle development artifacts such as structural and behavioral features, and deploy system artifacts and operational qualifications. SPF is utilized during the product development as well as during system operations and support. The white paper describes the components of the SPF to build relevant Ground Vehicle Rapid Prototyping (GVRP) models in accordance with the model-centric digital engineering process guidelines. The factory and the processes together ensure that the artifacts are produced as specified. The processes are centered around building, maintaining, and tracing single source of information from source all the way to final atomic element of the built system.
Purdue University researchers have developed a patent-pending method to add particles to filament and disperse them evenly through a traditional fused deposition modeling, or FDM, 3D printer, which will aid industry in manufacturing functional parts.
Additive manufacturing produces parts by adding material layer by layer with respect to time based on a computerized 3D solid model. The design model of Robotic arm was prepared using the solid works. The various components such as fingers, gripper, etc., were created and connected. After meticulous mathematical calculations, the design features of the robotic arm, including force analysis, are arrived at, and the arm is prepared to be operated using Bluetooth. Major challenges were faced during conversion of the designed model to prototype model. Furthermore, the components were created utilising fast rapid prototyping, which is more efficient than other traditional approaches. This technology has been effectively used in the production of light weight prototypes, tooling and the development low-cost bespoke designs. Finally, all the parts are assembled with Bluetooth control systems and validated with payload up to maximum of 10kgfor lowering and lifting.
Avoiding the pitfalls of 3D printing requires knowing the process limitations - and how to work around them. An expert at a leading AM specialist shares insights on getting it right. As additive manufacturing (AM) technology and its applications expand, engineers are recognizing that different industrial 3D printing processes have different constraints that can affect designed parts in production. Some constraints are universal across the different processes, and some are more specific to the type of process used. It is thus essential to understand the technology you are working with to maximize its potential as a production method. With this understanding it is possible to design around the general limitations of AM as well as the specific process constraints that could impact a product or part. While design for manufacture (DfM) is not a new concept, the rules for designing for additive manufacture (DfAM) require design engineers to take a different approach. This article is
Engineers have created a highly effective way to paint complex 3D-printed objects, such as lightweight frames for aircraft and biomedical stents, that could save manufacturers time and money and provide new opportunities to create “smart skins” for printed parts.
Senior level military members are constantly evaluating risk and finding ways to articulate and mitigate risks on the battlefield. When it comes to new technology adoption and consideration, these same leaders must take a similar approach to justify spending taxpayer dollars and implementing new processes to improve operational outcomes.
When weight reduction is the primary goal, 3D-printed aluminum alloys are a frequent choice for aerospace and high-performance motorsports applications. Aluminum is much lighter than nickel alloys and has been particularly popular for laser powder-bed fusion (LPBF) because it’s good for prototyping and easy to post-process.
Fiber-reinforced polymer composites propose exceptional directional mechanical properties, and combining their advantages with the potential of 3D printing has resulted in many novel research fronts. Industries have started using 3D printed components which are rapidly replacing conventional material components in most of the industries. Carbon fiber reinforced Polylactic Acid (PLA) often finds its application in reasonably high loading conditions working at lesser speed like lightweight gears, spanners, nuts, and bolts. Wear reduction is an important factor that plays an important role in prolonging the component's life. Hence, it is crucial to optimize 3D printing parameters to get desired strength according to the application. The aim of this paper is to conduct the wear rate test on the Fused Deposition Modelled (FDM) printed carbon fiber reinforced PLA parts, to identify the optimum printing parameters which are crucial for wear reduction. Two process parameters i.e. fill density
The utilization of Additive Manufacturing (AM) technology in the current manufacturing sector is growing day - by - day. This is made possible by the constant development of new materials and techniques to overcome the difficulties that are encountered while fabricating a part. In AM, parts are fabricated by laying successive layers on one another till the complete part is build. This gives AM an edge over conventional manufacturing. Even intricate or hollow parts can be fabricated with the same ease as fabricating a solid part. The key objective of this project is to evaluate and compare mechanical properties of Polyethylene Terephthalate - Glycol modified (PETG) and Carbon fiber reinforced Polyethylene Terephthalate - Glycol modified (CF - PETG), which are fabricated using Fused Deposition Modelling (FDM) process of AM. The ASTM standards D638 and D790 were followed for fabricating tensile test and Flexural test specimens respectively. Subsequently, fractured specimens are analyzed
Marotta Controls Montville, NJ 973-334-7800
Assessment of the boundaries for self-ignition of unburned charge in spark ignition engines (also related to knock) is required for development of the engine concepts and controls with respect to charge composition, spark advance and valve timing when designing the gas engines with wide range of the fuel compositions and converting compression ignition engines to gas engines. In this paper the combination of the single-zone model of the SI engine and chemical kinetics modeling is evaluated as a rapid prototyping tool for prediction of the self-ignition of the unburned charge in SI engine. The single-zone model simulates the cylinder pressure history based on Wiebe heat release function. The simulation of the self-ignition of the unburned charge is performed with coupled solution of the system of ordinary differential equations for temperature and species concentration with detailed chemical kinetic mechanism. Three fuels were considered: primary reference fuel, methane, hydrogen. The
3D printing has the potential to revolutionize product design and manufacturing in a vast range of fields, from custom components for consumer products, to 3D-printed bone and medical implants. But the process also creates a large amount of expensive and unsustainable waste and takes a long time, making it difficult for 3D printing to be implemented on a wide scale.
ABSTRACT Considering the growth of unmanned vehicles in Defense and Government applications, a simple and efficient way to design, develop and deploy trusted and secure systems is imperative. Secmation’s SecMUAS brings a platform for the rapid design and development of secure modular unmanned systems to defense applications and beyond. SecMUAS “bakes in” cybersecurity features using a modular design framework for unmanned systems. SecMUAS enables affordable, high assurance, “future-proof” solutions to rapidly transition from design to operational use. Secmation’s SecMUAS hardware and software will provide developers a capability to address cybersecurity requirements and related certification approval processes, enabling the rapid transition of technology to the warfighter. Citation: H. Aldridge, F. Livingston, “Secure Rapid Prototyping for Unmanned Systems”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 10-12, 2021.
Additive manufacturing, also known as 3D printing, allows the fast and cost-effective production of complex high-quality components in a range of materials. The rise of this technology has been fast, and it is rapidly altering the manufacturing landscape. In 2019, the global additive manufacturing market size was valued at $11.58 billion and is predicted to grow at a CAGR (compound annual growth rate) exceeding 14% from 2020 to 2027 (GVR). Additionally, research from Deloitte shows that additive manufacturing is empowering industry 4.0.
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