Browse Topic: Manufacturing systems
This document applies to the development of Plans for integrating and managing electronic components in equipment for the military and commercial aerospace markets, as well as other ADHP markets that wish to use this document. Examples of electronic components described in this document include resistors, capacitors, diodes, integrated circuits, hybrids, application specific integrated circuits, wound components, and relays. It is critical for the Plan owner to review and understand the design, materials, configuration control, and qualification methods of all “as-received” electronic components and their capabilities with respect to the application; and to identify risks and, where necessary, take additional action to mitigate the risks. The technical requirements are in Section 3 of this standard and the administrative requirements are in Section 4.
There is an increasing effort to reduce noise pollution across different industries worldwide. From a transportation standpoint, pass-by regulations aim to achieve this and have been implementing increasingly stricter emissions limits. Testing according to these standards is a requirement for homologation, but does little to help manufacturers understand why their vehicles may be failing to meet limits. Using a developed methodology such as Pass-by Source Path Contribution (SPC, also known as TPA) allows for identification of dominant contributors to the pass-by receivers along with corresponding acoustic source strengths. This approach is commonly used for passenger vehicles, but can be impractical for off-highway applications, where vehicles are often too large for most pass-by-suitable chassis dynamometers. A hybrid approach is thereby needed, where the same techniques and instrumentation used in the indoor test are applied to scenarios in an outdoor environment. This allows for
Large eddy simulations (LES) of two HVAC duct configurations at different vent blade angles are performed with the GPU-accelerated low-Mach (Helmholtz) solver for comparison with aeroacoustics measurements conducted at Toyota Motor Europe facilities. The sound pressure level (SPL) at four near-field experimental microphones are predicted both directly in the simulation by recording the LES pressure time history at the microphone locations, and through the use of a frequency-domain Ffowcs Williams-Hawking (FW-H) formulation. The A-weighted 1/3 octave band delta SPL between the two vent blades angle configurations is also computed and compared to experimental data. Overall, the simulations capture the experimental trend of increased radiated noise with the rotated vent blades, and both LES and FW-H spectra show good agreement with the measurements over most of the frequency range of interest, up to 5,000Hz. For the present O(30) million cell mesh and relatively long noise data collection
Over the past 30 years concerns about noise & vibration have become more critical in the design and manufacture of the automobile. Tools, both in physical testing and computer aided engineering have and continue to develop permitting more refined designs. Today’s customer can be very discerning when it comes to vehicle noises and vibrations. However, this is not a new concern for automotive customers or manufactures. This paper highlights the drive from automotive manufacturers to promote quiet, smooth and vibrationless operation of their products as well as some of the advances in vehicle component design over the past 100+ years. This is not an exhaustive study, but rather the intent is to bring to light the long history of noise and vibration in the automotive industry and its importance to the customers even in the infancy of the auto industry.
Additive manufacturing has been a game-changer in helping to create parts and equipment for the Department of Defense's (DoD's) industrial base. A naval facility in Washington state has become a leader in implementing additive manufacturing and repair technologies using various processes and materials to quickly create much-needed parts for submarines and ships. One of the many industrial buildings at the Naval Undersea Warfare Center Division, Keyport, in Washington, is the Manufacturing, Automation, Repair and Integration Networking Area Center, a large development center housing various additive manufacturing systems.
At a time when medical technology is advancing rapidly, the demand for precision in manufacturing has never been greater. The medical device industry is pushing the boundaries of design, requiring components that are not only smaller and more intricate but also biocompatible, reliable, and capable of meeting stringent regulatory standards. To address these challenges, manufacturers are increasingly turning to photochemical etching (PCE) — a process that is proving indispensable in high-precision medical applications.
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
Mesekon Oy, a Finnish welding manufacturer that produces complex welded steel structures for the marine, energy, and paper industries, needed a flexible and collaborative solution to improve efficiency, reduce defects, and enhance workplace ergonomics by automating repetitive and physically demanding welding operations.
Perkins details range of development efforts to power future off-highway machines, from clean-sheet diesel to hybrid-electric and hydrogen combustion. Many manufacturers in the construction and mining vehicle sectors have tabbed the Bauma trade show in April as the venue for major product debuts. Perkins is one of those, though it provided select media an overview of its latest powertrain developments and projects at a pre-Bauma briefing in early February. Hydrogen and hybrids were a large part of the discussion at the London media event, but Perkins began the day expounding on good old diesel-engine development. The company's engineers are still working hard to strengthen - and streamline - its diesel portfolio, all while readying new platforms for other fuels and applications.
U.S. Army Combat Capabilities Development Command’s Armaments Center Independence, MO usarmy.pica.jpeo-aa.mbx.jpeo-aa-public-affairs@army.mil
Los Angeles-based plastics contract manufacturer Kal Plastics deployed UR10e trimming cobot for a fraction of the cost and lead time of a CNC machine, cut trimming time nearly in half, and reduced late shipments to under one percent — all while improving employee safety and growth opportunities.
Time Sensitive Networking (TSN) Ethernet is a real-time networking capability that is being developed by a growing number of embedded computing companies for the earliest stages of adoption by aerospace and defense manufacturers and their suppliers. According to the Institute of Electrical and Electronics Engineers (IEEE) TSN working group, it is a set of standards that provides deterministic connectivity within IEEE 802-aligned networks.
Time Sensitive Networking (TSN) Ethernet is a real-time networking capability that is being developed by a growing number of embedded computing companies for the earliest stages of adoption by aerospace and defense manufacturers and their suppliers. According to the Institute of Electrical and Electronics Engineers (IEEE) TSN working group, it is a set of standards that provides deterministic connectivity within IEEE 802-aligned networks. Nigel Forrester is the Director of Product Strategy for Concurrent Technologies, a UK-based provider of high performance embedded computing solutions for aerospace, defense and many other industries. Check out our interview with Forrester about the potential impact of TSN Ethernet on new and legacy aerospace and defense applications, and how it is being adopted by manufacturers and system integrators below.
For my nearly 60-year lifetime, I have had the benefit of being part of a North American Automotive Industry that was, from a production perspective, completely rationalized and optimized. Given the unprecedented political events of the last couple of months, maybe we should all consider ourselves fortunate. Strong competition and a free market allowed for components, systems and vehicles to be produced in the optimal location with an optimized supply chain, all structured to serve markets in the U.S., Canada and Mexico with some exports mixed in. Consumers, dealers, suppliers and vehicle manufacturers all benefit from this optimized structure.
It is a fool's errand to make timely comments - in print! - about our current political turmoil. Even so, it feels important to place a marker in the sand to note the ongoing political reign of tariff threats, the upheaval potential of a demolished regulatory state affecting road and vehicle safety, and the damage that cuts to electric vehicle support might do to American automakers attempting to keep technological pace with their global automaker peers. It's a lot. The mainstream press is reporting the broad strokes of the industry's reaction to the new president. Ford CEO Jim Farley said Trump's erratic threats and changes are adding “a lot of cost and a lot of chaos” to the automotive industry and that a 25% tariff would “blow a hole in the U.S. industry that we've never seen.” Volvo Cars CEO Jim Rowan said that profitability would suffer under any tariffs, whether those are the general 25% tariffs on Canada and Mexico (now seemingly canceled after Trump backed down), just-announced
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
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 (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
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 SAE Recommended Practice establishes a procedure for the issuance and assignment of a World Manufacturer Identifier (WMI) on a uniform basis to vehicle manufacturers that may desire to incorporate it in their Vehicle Identification Numbers (VIN). This recommended practice is intended to be used in conjunction with the recommendations for VIN systems described in SAE J853, SAE J187, SAE J272, and other SAE reports for VIN systems. These procedures were developed to assist in identifying the vehicle as to its point of origin. It was felt that review and coordination of the WMI by a single organization would avoid duplication of manufacturer identifiers and assist in the identification of vehicles by agencies such as those concerned with motor vehicle titling and registration, law enforcement, and theft recovery.
Speed and flexibility are increasingly becoming the cornerstones of modern manufacturing, even as their continued adoption must align with existing values of cost and reliability all while keeping up with the demands for smarter, more complex products. This presents many challenges to machine builders since they must keep pace with the complexity of upcoming products while also being ready to meet the demands of the companies that will buy and operate these machines when it comes to efficiency, rapid production line ramp up, small batch sizes and high quality. Artificial intelligence will be a key tool going forward in achieving these results, offering the ability to more rapidly design, prototype, and implement changes and solutions through superior data analytics abilities and improved human-machine interactions.
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining approach that is well-known for its capability to create intricate forms in materials with high levels of hardness and intricate geometries. Invar 36, a nickel-iron alloy, is extensively utilized in industries that demand exceptional dimensional stability across a wide temperature range. The objective of this exploration is for optimizing the WEDM parameters of Invar 36 material. Additionally, a predictive model called Adaptive Neuro-Fuzzy Inference System (ANFIS) will be developed to forecast the machining performance. The study involved conducting experimental trials to analyze the influence of crucial factors in WEDM. These parameters included pulse-on time (Ton), pulse-off time (Toff), and current. The objective was to examine their influence on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of Design of Experiments (DOE) enabled a systematic
As we move towards sustainable transportation, it is essential to look for alternative powertrain technologies that might reduce emissions and depend less on fossil fuels. This paper offers a thorough analysis and comparison of several viable solutions along with their benefits, cost and conclusion for hydrogen fuel cells, solar cells, electric hybrid systems, compressed natural gas (CNG) and CNG hybrid systems alongside the latest proposal of using nuclear batteries. Hydrogen cars have zero emissions from their exhaust and can be refueled quickly, however there are some drawbacks like hydrogen production, storage, and infrastructure. The efficiency, affordability, and scalability of various hydrogen production techniques, fuel cell stack designs and storage technologies (compressed gas, liquid, and metal hydrides) are evaluated in this paper. Solar FCEVs on the other hand, are designed to utilize solar energy like Solar EVs but are very different in their operation and fundamentals
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
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
Wire Electrical Discharge Machining (WEDM) is a widely used manufacturing method that is employed to shape complex geometries in conductive materials such as cupronickel, which is highly regarded for its resistance to corrosion and ability to conduct heat. The aspiration of this investigation is to improve the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for cupronickel material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study analyzes the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness, material removal rate. Experimental trials are performed to collect data on these parameters and their corresponding machining characteristics. The TOPSIS optimization method is utilized to determine the most favourable parameter settings by evaluating each parameter combination against the ideal and
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Whether for vascular catheters or implantable devices, medical tubing must meet tough standards for flexibility, strength, and biocompatibility. That’s why more manufacturers are turning to thermoplastic polyurethanes (TPUs) that strike the ideal balance between these key properties, making them an excellent choice for high-performance medical tubing. Unlocking the best that TPUs have to offer means optimizing the extrusion process. This article looks at why TPUs are a top pick, the common obstacles in extrusion, and the ways manufacturers can fine-tune their process to get the most out of different grades.
Design and material choices can have a long-term impact on an original equipment manufacturer’s (OEM) production costs and product quality. When an OEM works together with an experienced contract design manufacturer (CDM) from the start of a project, many negative impacts to cost and quality can be avoided.
In September, after several months of evaluating the market, “Honda Xcelerator Ventures” — the automotive manufacturer’s startup investment subsidiary — made a major investment award to California-based silicon photonics startup SiLC Technologies, Inc., to develop next generation Frequency-Modulated Continuous Wave (FMCW) LiDAR for “all types of mobility.”
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