Browse Topic: Advanced manufacturing
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
The automotive industry leverages Fused Filament Fabrication (FFF) -based Additive Manufacturing (AM) to reduce lead time and costs for prototypes, rapid tooling, and low-volume customized designs. This paper examines the impact of print orientation and raster angle on the tensile properties of Polylactic Acid (PLA), selected for its ease of use and accessibility. Dog bone samples were designed to the ASTM D638 tensile testing standard and printed solid with a 0.2 mm layer height, two outer walls, and varying raster-fill angles, with layers alternating by 90°. Testing was conducted on the MTS Criterion Model 43, 50 kN system. Varying print orientation along the X and Y axes (double angle builds) produced a Young's modulus (YM) range of 0.7519, reflecting a 34.42% increase between the witnessed minimum and maximum values. These builds exhibited more brittle behavior than most single angle builds, except for X10 Y10 Z0 at a 45° raster (the lowest recorded YM) and X0 Y15 Z0 at a 30
Triply Periodic Minimal Surface (TPMS) structures have gained significant attention in recent years due to their excellent mechanical properties, lightweight characteristics, and potential for energy absorption in various engineering applications, particularly in automotive safety. This study explores the design, manufacturing, and mechanical performance of both general and hybrid TPMS structures for energy absorption. Three types of fundamental TPMS unit cells—Primitive, Gyroid, and IWP—were modeled using implicit functions and combined to form hybrid structures. The hybrid designs were optimized by employing Sigmoid functions to achieve smooth transitions between different unit cells. The TPMS structures were fabricated using Selective Laser Melting (SLM) technology with 316L stainless steel and subjected to quasi-static compression tests. Numerical simulations were conducted using finite element methods to verify the experimental results. The findings indicate that hybrid TPMS
A passenger vehicle hood is designed to meet Vulnerable Road User (VRU) regulatory requirements and consumer metric targets. Generally, hood inner design and its reinforcements, along with deformable space available under the hood are the main enablers to meet the Head Impact performance targets. However, cross functional balancing requirements, such as hood stiffness and packaging space constraints, can lead to higher Head Injury Criteria (HIC15) scores, particularly when secondary impacts are present. In such cases, a localized energy absorber is utilized to absorb the impact energy to reduce HIC within the target value. The current localized energy absorber solutions include the usage of flexible metal brackets, plastic absorbers etc. which have limited energy absorbing capacity and tuning capability. This paper focuses on usage of a novel 3D printed energy absorbers, based on various kinds of lattice structures. These absorbers are either sandwiched between the inner and the outer
The advance of regulatory emission standards for light-duty vehicles, trucks and motorcycles, coupled with rising sustainability concerns, particularly United Nations' Sustainable Development Goal 12 (responsible consumption and production), has created an urgent need for lighter, stronger, and more ecological materials. Polylactic acid (PLA), a biodegradable polymer derived from plant sources, offers promising mechanical tensile strength and processability. Nanocomposites, a solution that combines a base matrix with a nanoreinforcing filler, provides a path toward developing sustainable materials with new properties. Cellulose nanofibrils (CNF) are a valuable nanofiller obtained through industrial waste or vegetal fibers, offer a promising avenue for strengthening PLA-based materials. Additive manufacturing (AM) has gained popularity due to its ability to create complex parts, prototyping designs, and to evaluate new nanocomposite materials such as PLA/CNF, showing significant
Triply periodic minimal surface (TPMS) structure, demonstrates significant advantages in vehicle design due to its excellent lightweight characteristics and mechanical properties. To enhance the mechanical properties of TPMS structures, this study proposes a novel hybrid TPMS structure by combining Primitive and Gyroid structures using level set equations. Following this, samples were fabricated using selective laser sintering (SLS). Finite element models for compression simulation were constructed by employing different meshing strategies to compare the accuracy and simulation efficiency. Subsequently, the mechanical properties of different configurations were comprehensively investigated through uniaxial compression testing and finite element analysis (FEA). The findings indicate a good agreement between the experimental and simulation results, demonstrating the validity and accuracy of the simulation model. For TPMS structures with a relative density of 30%, meshing with S3R
As stepper motors become more and more widely used in engineering systems (vehicles, 3-D printers, manufacturing tools, and similar), the effects of their induced magnetic fields present a concern during the packing and orientation of components within the system. For applications requiring security, this is also a concern as the background electromagnetic radiation (EMF) can be captured at a distance and used to reproduce the motion of the motor during operation. One proposed alternative is to use customized non-magnetic plastic shields created using additive manufacturing. Some small studies have been completed which show some effectiveness of this approach but these studies have been small-scale and difficult to reproduce. To seek a more rigorous answer to this question and collect reproducible data, the present study used full factorial design of experiments with several replications. Three materials were used: Polylactide (PLA), PLA with 25% (weight) copper powder, and PLA with 15
With the development of additive manufacturing technology, the concept of integrated design has been introduced and deeply involved in the research of body design. In this paper, by analyzing the structural characteristics of the electric vehicle body, we designed a body in white with the additive manufacturing process, and analyzed its mechanical properties through finite element method. According to the structural characteristics of the body, the integrated structure was modeled in three dimensions using CATIA. For the mechanical properties of the body, the strength and stiffness of the body structure were simulated and analyzed based on ANSYS Workbench. The results show that for the strength of the body, the maximum stress of the simulation results was compared with the permissible stress, and the maximum stress was calculated to be less than the permissible stress under each working condition. For the body stiffness, the displacement of the body deformation was used to measure, and
The tensile and low-cycle fatigue (LCF) properties of Ti6Al4V specimens, manufactured using the selective laser melting (SLM) additive manufacturing (AM) process and subsequently heat-treated in argon, were investigated at elevated temperatures. Specifically, fully reversed strain-controlled tests were performed at 400°C to determine the strain-life response of the material over a range of strain amplitudes of industrial interest. Fatigue test results from this work are compared to those found in the literature for both AM and wrought Ti6Al4V. The LCF response of the material tested here is in-family with the AM data found in the literature. Scanning electron microscopy performed on the fracture surfaces indicate a marked increase in secondary cracking (crack branching) as a function of increased plastic deformation and demonstrating equivalent performance when compared to the wrought Ti6AL4V at RT (room temperature) at 1.4% strain amplitude and better performance when compared to the
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
Additive manufacturing has made it possible for the design of increasingly complex structures that require precise manufacturing. This may be particularly beneficial for heat pipe and vapor chamber design – particularly for the wick structure, a very important component. This study uses numerical simulation to analyze three different types of lattice structures of increasing complexity, in terms of their capillary performance. This is one of the most important parameters which determine the wick efficacy. Simple cubic, Column and Octet lattice models are computationally designed and CFD is used to simulate capillary action in a pipe of 0.4 mm inner radius for 2 milliseconds, after validation of the numerical model with existing experimental results. It is found that the Octet lattice (with the most complex inner structure) has the greatest capillary rise in the same amount of time. The rate of rise is not uniform for any structure, but is highest for Octet. This study demonstrates the
In recent years, Additive Manufacturing (AM), more especially Fused Deposition Modeling (FDM), has emerged as a very promising technique for the production of complicated forms while using a variety of materials. Polyethylene Terephthalate Glycol, sometimes known as PETG, is a thermoplastic material that is widely used and is renowned for its remarkable strength, resilience to chemicals, and ease of processing. Through the use of Taguchi Grey Relational Analysis (GRA), the purpose of this investigation is to improve the process parameters of the FDM technology for PETG material. In order to investigate the influence that several FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, have on significant outcome variables, such as dimensional accuracy, surface quality, and mechanical qualities, an empirical research was conducted. For the purpose of constructing the regression prediction model, the obtained dataset is used to make
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
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
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful
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
This study investigates the fabrication and characterization of overhanging structures using the Cold Metal Transfer (CMT) pulse based Wire Arc Additive Manufacturing (WAAM) technique, specifically targeting automotive applications on commercial aluminum components. Focusing on optimal welding strategies for overhanging structures, components are fabricated by providing offsets during consecutive deposition of layers, thus producing parts with angles of 45°, 60° and 90° inclinations from the substrate. Three specimens undergo around twenty-five layers of deposition, resulting in structurally sound joints within this specified angle range. AA 4043 electrode is utilized, and welding parameters are optimized through trials by verifying with bead on plate deposition. Successful outcomes are achieved within the specified angle range, though challenges arise beyond 60°, complicating the maintenance of desired weld quality. The study further evaluates the microstructure, microhardness, and
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
The advancement of wire-arc additive manufacturing (WAAM) presents a significant opportunity to revolutionize the production of automotive components through the fabrication of complex, high-performance structures. This study specifically investigates the metallurgical, mechanical, and corrosion properties of WAAM-fabricated ER 2209 duplex stainless steel structures, known for their superior mechanical properties, excellent corrosion resistance, and favorable tribological behavior. The research aims to optimize WAAM process parameters to achieve high-quality deposition of ER 2209, ensuring structural integrity and performance suitable for both marine and various automotive applications. Microstructural analysis of the produced samples revealed the alloy’s dual-phase nature, with roughly equal amounts of ferrite and austenite phases uniformly mixed across the layers of deposition. This balanced microstructure contributes to the alloy’s excellent mechanical properties. Yield strength
This article explores the utilization of simple-cubic, diamond, octet-truss, and X-type lattice structures for low-pressure turbine blades in engine turbines to enhance natural frequency and decrease overall engine weight while maintaining structural integrity. The research method involves analyzing polylactic acid (PLA) hollow T106C blades with fully infilled and 50–80 location-based lattice arrangements. The study modifies the strut thickness of lattice structures using both constant and variable-based approaches and applies a generalized formula based on relative density to evaluate how changes in lattice thickness and arrangements influence natural frequencies. Furthermore, the investigation extends to multi-lattice configurations, introducing a parameter 𝑘 to signify the transition between different lattices. The modified blades were 3D printed using PLA and tested for natural frequencies through modal testing. The results demonstrate that location-based 50–80 exponential-based
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
This article addresses the machines and automated guided vehicles (AGVs) concurrent scheduling with alternative machines in a multi-machine flexible manufacturing system (FMS) in order to provide the best optimum sequences for the minimization of makespan (MKSN).The assignment of AGVs and related trips, such as the dead headed trip and loaded trip times of AGVs to jb-ons, as well as the decision to select machines for job-operations (jb-ons) and the sequencing of jb-ons on the machines, make this problem extremely difficult to solve. This paper offers a mixed integer nonlinear programming (MINLP) formulation for modeling the problem, as well as the crow search algorithm (CSA) to solve the problem. For verification, a manufacturing company's industrial problem is employed. The findings indicate that CSA performs better than the existing techniques, and that the utilization of alternative machines for the operations can bring the MKSN and cost down.
The intended upper bound of this specification is that the particle size distribution (PSD) of powders supplied shall be <60 mesh (250 µm) and that no powder (0.0 wt.%) greater than 40 mesh (425 µm) is allowed.
Additive manufacturing technologies, particularly wire arc additive manufacturing (WAAM), have gained recognition for their ability to produce large metallic components efficiently and cost-effectively. This study investigates both the mechanical properties and microstructure of 304L austenitic stainless steel produced via WAAM, focusing on orientation-dependent behavior. Tensile specimens were prepared in transversal, diagonal, and longitudinal orientations according to ASTM E8 standards, and their mechanical properties were evaluated. The results show that the diagonal sample exhibited the highest tensile strength of 555 MPa with an elongation of 47.9%, while the longitudinal sample demonstrated the highest ductility with a notable elongation of 61.4%. Microstructural analysis, including scanning electron microscopy (SEM), revealed refined grain structures and alignment that influenced mechanical properties and stress distribution. Hardness measurements showed an increase across all
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
Artificial intelligence (AI) is poised to significantly impact metal additive manufacturing (AM). Understanding how one might use AI in AM is challenging because AM experts are not AI experts, nor the other way around. This document introduces AI in AM and guides researchers in accessing relevant literature. It also discusses the hype surrounding AI in AM, the rush to publish peer-reviewed papers that use AI in AM, and the resulting uneven quality of the literature. Conclusions regarding the application of AI in both large and small enterprises are discussed. This document is intended to help illuminate AI in AM for Hands-on engineers who need to quickly understand what levels of problems they might encounter when dealing with AI in AM Engineering managers who need to stay current on emerging trends in their technical realm of responsibilities Policymakers who may not have the relevant technical expertise Faculty and students who want an introduction to AI in AM NOTE: SAE Edge Research
Letter from the Guest Editors
3-Dimensional (3D) printing is an additive manufacturing technology that deposits materials in layers to build a three-dimensional component. Fused Deposition Modelling (FDM) is the most widely used 3D printing technique to produce the thermoplastic components. In FDM, the printing process parameters have a major role in controlling the performance of fabricated components. In this study, carbon fibre reinforced polymer composites were fabricated using FDM technique based on Taguchi's Design of experimental approach. The matrix and reinforcement materials were poly-lactic acid (PLA) and short carbon fibre, respectively. The goal of this study is to optimize the FDM process parameters in order to obtain the carbon fibre reinforced PLA composites with enhanced hardness and compressive strength values. Shore-D hardness and compression tests were carried out as per American Society for Testing and Materials (ASTM) D2240 and ASTM D695 standards respectively, to measure the output responses
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 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 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
The market for battery-fitted electric cars continues to experience robust growth globally as well as in Indian market. During the charging process heat generation happen because of internal resistance of the battery cells and electrical connectors. Making an efficient battery cooling system is vital for all electric vehicles. One common cause of battery overheating is due to low cooling efficiency. So this research highlights the importance of scientifically designing coolant circuits and selecting appropriate coolant hose materials. Currently, EPDM (ethylene propylene diene monomer) material is widely used for battery cooling hoses due to its design Flexibility, Compatibility with a 50:50 glycol-water mixture and Resistance to thermal and ozone cracking [1]. This study benchmarks EPDM hose technical properties with leading EV battery cooling plastic hose materials, such as mono layer polyamide, mono layer TPVs (thermoplastic vulcanizates) and PA PP two layer hose. Comparative
This specification establishes process controls for the repeatable implementation of the CSAM process for the manufacturing of metallic and metal-nonmetal blend components.
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
This specification covers an alpha-beta Ti-6Al-4V alloy produced by laser powder bed fusion (L-PBF) additive manufacturing and subjected to hot isostatic press (HIP) operation. Typically, this material is used for complex-shaped aerospace products made to near net shape dimensions. These products have been used typically for parts requiring operating strength up to 750 °F (399 °C), but usage is not limited to such applications.
Researchers at the Johns Hopkins Applied Physics Laboratory have developed a machine learning method that could have a huge impact on understanding how material is formed during the additive manufacturing process. John Hopkins Applied Physics Laboratory, Laurel, MD Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin. “We anticipate that this new approach will be extremely impactful in helping design and understand material formation during additive manufacturing processes, and this fits into our overarching strategy focused on accelerating materials development for national security,” said
As “point of need” additive manufacturing emerges as a priority for the Department of Defense (DoD), Australian 3D printing provider SPEE3D is one of several companies demonstrating that its machines can rapidly produce castings, brackets, valves, mountings and other common replacement parts and devices that warfighters often need in an on-demand schedule when deployed near or directly within combat zones. DoD officials describe point of need manufacturing as a concept of operations where infantry and squadron have the equipment, machines, tools and processes to rapidly 3D print parts and devices that are being used in combat. Based in Melbourne, Australia, SPEE3D provides cold spray additive manufacturing (CSAM) machines that use a combination of robotics and high-speed kinetic energy to assemble and quickly bind metal together into 3D-printed parts without the need for specific environmental conditions or post-assembly cooling or temperature requirements. Over the last two years, the
Honda has long been at the cutting edge of mobility and tech, with everything from the Asimo robot of 20 years ago to plans for reusable rockets to launch lightweight satellites into orbit. During a Tech Day event in early October in Tochigi, Japan, the Japanese automaker announced further details of its upcoming Honda 0 architecture (Honda calls it “Honda Zero” but writes it with the number), its first in-house electric platform designed from the ground up. Honda also discussed some of the advanced manufacturing techniques it's pioneering to reach its core design and technology tenants.
Biomedical engineers have developed a “bio-ink” for 3D-printed materials that could serve as scaffolds for growing human tissues to repair or replace damaged ones in the body. Bioengineered tissues show promise in regenerative, precision, and personalized medicine; product development; and basic research, especially with the advent of 3D printing of biomaterials that could serve as scaffolds or temporary structures to grow tissues.
In recent years, engineers at ETH Zurich have developed the technology to produce liquid fuels from sunlight and air. In 2019, they demonstrated the entire thermochemical process chain under real conditions for the first time, in the middle of Zurich, on the roof of ETH Machine Laboratory. These synthetic solar fuels are carbon neutral because they release only as much CO2 during their combustion as was drawn from the air for their production. Two ETH spin-offs, Climeworks and Synhelion, are further developing and commercializing the technologies.
Researchers have successfully demonstrated the four-dimensional (4D) printing of shape memory polymers in submicron dimensions that are comparable to the wavelength of visible light. 4D printing enables 3D-printed structures to change their configurations over time and is used in a variety of fields such as soft robotics, flexible electronics, and medical devices.
Researchers have developed a printing process that prints strong nonmetallic materials in record time — five times faster than traditional 3D printing. The process, called SWOMP, which stands for Selective dual-wavelength Olefin metathesis 3D printing, uses dual-wavelength light, unlike the traditional printing process.
Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin.
This work aims to define a novel integration of 6 DOF robots with an extrusion-based 3D printing framework that strengthens the possibility of implementing control and simulation of the system in multiple degrees of freedom. Polylactic acid (PLA) is used as an extrusion material for testing, which is a thermoplastic that is biodegradable and is derived from natural lactic acid found in corn, maize, and the like. To execute the proposed framework a virtual working station for the robot was created in RoboDK. RoboDK interprets G-code from the slicing (Slic3r) software. Further analysis and experiments were performed by FANUC 2000ia 165F Industrial Robot. Different tests were performed to check the dimensional accuracy of the parts (rectangle and cylindrical). When the robot operated at 20% of its maximum speed, a bulginess was observed in the cylindrical part, causing the radius to increase from 1 cm to 1.27 cm and resulting in a thickness variation of 0.27 cm at the bulginess location
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