Browse Topic: Rapid prototyping
The increasing pressure to decarbonize manufacturing systems is pushing industry beyond conventional lightweighting strategies toward material and process paradigms, capable of delivering functional performance with radically lower environmental impact. In this context, polymer-based composite Additive Manufacturing (AM) offers an underexplored yet highly promising pathway for sustainable production of load-bearing components. This study presents a preliminary comparative cradle-to-gate Life Cycle Assessment (LCA) of a Formula SAE brake pedal, assessing the environmental transition from conventional sheet metal fabrication and finishing operations of Aluminum 7075-T6 to additive manufacturing solutions, with specific focus on Carbon-Fiber-Reinforced Polymer (CFRP) composites. Two topology-optimized designs, respectively for Powder Bed Fusion (PBF) in AlSi10Mg and Material Extrusion (MEX) in Polyethylene Terephthalate Glycol with Carbon Fiber (PETG-CF) are compared to conventional fabrication aluminum benchmark. The analysis is integrated in the product and process design following ISO 14040/14044 standards and is implemented using the Environmental Footprint 3.0 methodology within the 3DEXPERIENCE platform. Results outline that Material Extrusion (MEX) composite manufacturing achieves the lowest environmental impact across all evaluated categories. Compared to conventional manufacturing, the PETG-CF solution enables an approximate 50% reduction in Global Warming Potential and an almost complete elimination of mineral depletion. Unlike metal additive manufacturing, which remains constrained by high process energy demand, MEX benefits from low processing temperatures, minimal auxiliary systems, and highly efficient material deposition. Crucially, these sustainability gains are achieved while maintaining functional performance through design-driven topology optimization. AM composite solutions, by merging advanced material science with additive flexibility, may lead to design approaches which cease to be ‘potential’ enablers of sustainable manufacturing for the Industry 5.0 transition.
In the rapidly evolving aerospace and defense landscape, simply keeping pace with trends isn't enough. Technology is advancing faster than ever, and in mission critical applications, failure is not an option. Systems must endure harsh environments while meeting uncompromising quality standards - an imperative that demands relentless innovation. Enter the Coyotes: WOLF's specialists in next generation rugged embedded systems, small form factor design, and bold, practical ideas. Whether on Earth or in orbit, they expand what high performance embedded computing can do across ground, orbital, lunar and deep space operations. Their work spans R&D, rapid prototyping and new product development for edge computing and artificial intelligence (AI) enabled imaging.
Model Based Design (MBD) uses mathematical modelling to create, test and refine systems in simulated environment, primarily applied in control system development. This paper discusses an approach to control gear shifting using shift logic on vehicle level for twin clutch transmission using prototype controller. Twin clutch transmission is a concept with two clutches, one at input end of the transmission called primary clutch and the other at output end of the transmission called secondary clutch. This concept is proposed to counter the challenges with conventional transmission which include increased gear shift time and effort in lower gears, potential rollback of vehicle in uphill condition and chance of missed shifts. The advantages of this concept include reduced gear shift effort and improved synchronizer life with potential for reducing the size of the synchro pack. This paper proposes a methodology to develop shift logic, integrate hardware with software, flashing and calibration on vehicle using prototype controller. The shift logic is implemented using state machines in ASCET. The state machine uses vehicle speed, accelerator pedal position, gear shifter input, shift maps and current gear to determine most optimal gear shift in real-time. This control logic is then converted into c-code and integrated with hardware using INTECRIO, which is a build environment where inputs and outputs are mapped accordingly for shift actuation. In the next step, .a2l and .cod files are generated, which are flashed onto rapid prototyping hardware from ETAS using INCA. The same is used to actuate shift solenoids of transmission to change the gear. Use of rapid prototyping hardware has significantly reduced the number of iterations required for integrating software with ECU from specific supplier, thus reducing overall development time. Final vehicle level calibration is done using INCA, followed by validation process to ensure optimal performance.
This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.
In February, the Joint Interagency Field Experimentation (JIFX) team at the Naval Postgraduate School (NPS) executed another highly collaborative week of rapid prototyping and defense demonstrations with dozens of emerging technology companies. Conducted alongside NPS’ operationally experienced warfighter-students, the event is a win-win providing insight to accelerate potential dual-use applications.
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 experts from rapid prototyping and validation of the technological piece. This creates a need for frameworks that can provide adequate compartmentalization for domain experts (to carry out platform agnostic research) and yet permit flexible encapsulation of multiple robotic code deployment architectures (legacy or otherwise). In this work, we explore various facets of these challenges for autonomous operations with a simulated/physical Clearpath Husky robot by developing Robot Operating System (ROS) based Docker containers, that isolate different functions of the robot operations and yet interact with each other in real-time for a synergistic deployment.
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 demonstrated that the GRA approach is an effective tool for determining optimal parameter settings to enhance printing productivity and ensure the production of high-quality components. This study provides deeper insights into the Fused Deposition Modeling (FDM) process for Polyethylene terephthalate glycol (PETG) material, offering valuable strategies for improving manufacturing processes. By leveraging the GRA approach, this work highlights a reliable method for enhancing printing efficiency and quality, thereby promoting the wider adoption of FDM technology across various industries such as prototyping, manufacturing, and healthcare.
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 based on multiple criteria, was used to analyze the experimental data and determine the best parameter settings. TOPSIS provides a comprehensive method for optimizing parameters in FDM by taking into account both the closeness to the ideal solution and the distance from the negative ideal solution. The results demonstrated the efficacy of the TOPSIS method in pinpointing the most advantageous parameter combinations for improving the printing quality and efficiency of PLA components. The optimization framework that has been developed offers valuable insights into the optimization and control of processes, thereby facilitating the wider implementation of FDM technology across different industries. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides useful techniques for optimizing FDM parameters. Manufacturers can improve printing productivity, quality, and sustainability by utilizing the TOPSIS approach. This, in turn, will help promote the wider use of AM technology in various applications.
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 developed to forecast printing performance by utilizing input parameters and ANFIS. The ANFIS predictive models' ability to accurately predict the printing performance of ABS material was demonstrated by the results. Moreover, the models provide vital insights into the most effective parameter configurations for ensuring high-quality parts and maximizing printing efficiency. This investigation improves the understanding of Fused Deposition Modeling (FDM) for Acrylonitrile Butadiene Styrene (ABS) material and offers a practical instrument for manufacturing process optimization. By employing ANFIS predictive models, manufacturers can enhance the quality and productivity of printing. This will facilitate the expansion of the application of FDM technology in various sectors, including healthcare, manufacturing, and prototyping.
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 properties. The utilization of design of experiments (DOE) methodology enabled a methodical exploration of parameters. Statistical techniques were employed to develop regression models that establish relationships between process parameters and performance indicators. These models offer a prognostic instrument for optimizing FDM parameters and attaining desired printing results. The results demonstrated the effectiveness of the regression models in accurately forecasting the printing performance for TPU material. The models provide valuable insights into the optimal parameter configurations for maximizing printing efficiency, quality, and mechanical robustness. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material and provides useful techniques for optimizing the manufacturing process. Manufacturers can improve printing productivity and quality by utilizing regression models, thereby promoting the wider use of FDM technology in industries that need flexible and durable components.
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 criteria, was used to assess the trial data and identify the optimal parameter values. TOPSIS offers a thorough approach for improving parameters in FDM by considering both proximity to the ideal solution and distance from the negative ideal solution. The findings revealed the effectiveness of the TOPSIS technique in identifying the optimal parameter combinations for enhancing the printing quality and efficiency of PLA components. The proposed optimization framework provides significant insights into the optimization and control of processes, hence promoting the broader use of FDM technology across many sectors. This work improves the understanding of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) and offers effective methods for improving FDM settings. Manufacturers may enhance printing productivity, quality, and sustainability via the use of the TOPSIS methodology. This will subsequently facilitate the broader use of additive manufacturing technologies across many applications.
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 like dimensional accuracy, surface finish, and mechanical properties. The Taguchi method enabled the systematic exploration of parameters through the design of experiments (DOE). The experimental data was analyzed using Grey Relational Analysis (GRA) to determine the optimal parameter settings. The GRA methodology offers a comprehensive approach to assess and prioritize various performance criteria, taking into account the inherent uncertainties in the manufacturing process. The results demonstrated the efficacy of the Taguchi-based GRA method in pinpointing the optimal parameter combinations for improving the printing quality and efficiency of TPU components. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material and provides a useful framework for optimizing the manufacturing process. Manufacturers can enhance printing productivity, quality, and reliability by utilizing Taguchi-based GRA. This, in turn, promotes the wider use of FDM technology in various industrial applications that demand flexible and long-lasting components.
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 loop test system is designed with PXI hardware and NI VeriStand software. The simulation results indicate that the working current density is 1.0 Acm-2, at 25°C and 55°C, the electrolytic voltage of PEM electrolytic cell is 2.0093V and 2.06218V, respectively, and the electrolytic efficiency is 74.25% and 72.35% relative to the thermal neutral voltage of 1.492V. Performance simulation of electrolytic hydrogen production system and hardware in the loop testing of the controller were completed on this development platform. Practical applications have shown that this model based development platform can greatly improve development efficiency. It is applied to the development process of PEM electrolysis controllers to accelerate development speed and efficiency, and has certain engineering significance.
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. Tribological tests were conducted under varying normal loads (5, 10, 15, and 20 N) and sliding velocities (1 and 3 m/s), following the ASTM G99 standard test procedure. Significantly notable enhancements in wear and friction properties were consistently observed across all tribometer test conditions when the composites underwent annealing at 85°C for 60 min, surpassing the performance of other samples. These particular samples, subjected to the 85°C/60-min annealing process, exhibited elevated hardness, diminished wear rates, and reduced coefficients of friction (COF). A detailed examination using a scanning electron microscope revealed that the wear mechanism on the surface of the tribometer-tested samples exhibited milder wear when carbon fiber was added, followed by annealing at 85°C for 60 min, compared to the 90-min annealing. These promising results suggest that the proposed composites have potential applications in industries such as prosthetics, aerospace, and automobiles.
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 based on multiple criteria, was used to analyze the experimental data and determine the best parameter settings. TOPSIS provides a comprehensive method for optimizing parameters in FDM by taking into account both the closeness to the ideal solution and the distance from the negative ideal solution. The results demonstrated the efficacy of the TOPSIS method in pinpointing the most advantageous parameter combinations for improving the printing quality and efficiency of PLA components. The optimization framework that has been developed offers valuable insights into the optimization and control of processes, thereby facilitating the wider implementation of FDM technology across different industries. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides useful techniques for optimizing FDM parameters. Manufacturers can improve printing productivity, quality, and sustainability by utilizing the TOPSIS approach. This, in turn, will help promote the wider use of AM technology in various applications.
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 forecast printing performance by utilizing input parameters. The results demonstrated the effectiveness of the ANFIS predictive models in accurately predicting printing performance for PLA material. The models offer valuable insights into the most effective parameter configurations for maximizing printing efficiency and ensuring high-quality parts. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides a useful tool for optimizing the manufacturing process. Manufacturers can improve printing productivity and quality by utilizing ANFIS predictive models. This will help promote the wider use of FDM technology in different industries such as prototyping, manufacturing, and healthcare.
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 exceptional printing quality and mechanical properties of PETG parts. Furthermore, Grey Relational Grades helped to identify the key factors that affect the performance of the AM process. This study enhances the progress of additive manufacturing methods, particularly Fused Deposition Modeling (FDM), for PETG material. It focuses on meeting the increasing need for efficient and affordable production in diverse industries such as aerospace, automotive, and medical sectors. This study utilizes Taguchi Grey Relational Analysis to offer valuable insights into parameter optimization strategies that can be applied to a wide variety of additive manufacturing applications.
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 configuration that maximizes the overall printing quality by considering multiple objectives simultaneously. The effectiveness of the TOPSIS-optimized FDM process is assessed using statistical analysis and compared to the baseline outcomes. The proposed TOPSIS optimization method offers a valuable tool for optimizing the AM process. It allows manufacturers to enhance productivity and product quality while minimizing production costs. This study enhances the comprehension of Fused Deposition Modeling (FDM) techniques using Thermoplastic Polyurethane (TPU) material and provides valuable knowledge for improving Additive Manufacturing (AM) operations in different industrial sectors.
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.
In the proposed article, the authors will focus on two manufacturing method FUSED FILAMENT FABRICATION (FFF) and FUSED DEPOSITION MODELING (FDM) showing examples of application in aviation production and the resulting benefits.
Tohoku University Sendai, Japan
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 PA6 with carbon fiber exhibited superior mechanical properties than PA6. In further, this study offers valuable insights into the influence of FDM post-processing techniques, such as annealing, on the mechanical behavior of printed components. In FDM Annealed PA6 with carbon fiber emerged as the combination with the most superior mechanical properties across hardness, impact strength, and tensile strength. This study underscores the significance of carbon fiber reinforcement and annealing in enhancing the mechanical performance of PA6 components, providing valuable insights for applications demanding robust mechanical properties.
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 test samples in various dry sliding test conditions, encompassing applied load and sliding velocity. This outcome strongly indicates that the utilization of a 40m/s infill speed in the fabrication of PP with CF composites yields enhanced tribological performance. This enhancement can be attributed to the proficient bonding of uniformly distributed particles and the efficient adhesion between successive layers throughout the entire sample structure. Visual scrutiny of scanning electron microscope (SEM) images depicting the worn surfaces further elucidated the underlying wear mechanism. Particularly striking was the observation that samples subjected to the 40m/s infill speed exhibited diminished accumulation of debris and reduced plastic flow in comparison to their counterparts. Findings strongly activist the use of this composite in automotive and aerospace components requiring rotational or oscillatory motion.
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.
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.
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 dedicated to sharing some of the most common pitfalls encountered when designing parts for the selective laser sintering (SLS) and multi-jet fusion (MJF) 3D printing processes and how to avoid them.
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.
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