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The post Engineer’s Toolbox: 3D Printing for Aerospace appeared first on Engineering.com.

Through a combination of 3D Systems’ applications expertise, direct metal printing (DMP) technology, and Oqton’s 3DXpert software, the researchers are developing new processes to build embedded, high-temperature passive heat pipes for titanium heat rejection radiators. According to 3D Systems, these 3D printed radiators are 50% lighter per area with increased operating temperatures compared to the current state-of-the-art.

In addition, the team at Penn State has also developed a process to 3D print functional parts using nickel titanium (aka nitinol) shape memory alloys (SMAs) that can be passively actuated and deployed when heated. The researchers believe a passive SMA radiator will have a deployed-to-stowed area ratio roughly six times larger than conventional satellite radiators, making them particularly useful for CubeSats.

The researchers designed the SMA radiator with an embedded integral porous network inside the walls of the heat pipes, then manufactured the radiators in a single piece from titanium and nitinol using DMP. According to 3D Systems, the titanium-water heat pipe radiator prototypes were successfully operated at temperatures of 230°C and weigh 50% less than conventional designs.

“Our long-standing R&D partnership with 3D Systems has enabled pioneering research for the use of 3D printing for aerospace applications,” said Alex Rattner, associate professor of mechanical engineering at Penn State, in a press release. “The collective expertise in both aerospace engineering and additive manufacturing is allowing us to explore advanced design strategies that are pushing the boundaries of what is considered state-of-the-art.”

“Thermal management in the space environment is an ideal application for our DMP technology,” said Mike Shepard, vice president for aerospace and defense at 3D Systems, in the same release. “[It] is an extremely common engineering challenge and the DMP process can deliver solutions that are effective for many industries including aerospace, automotive, and high-performance computing/AI datacenters.”

For more information on 3D printing for space-based applications, including further commentary from Shepard, check out 3 challenges for 3D printed space-based components.

The post 3D Systems supports NASA research on thermal management appeared first on Engineering.com.

CoT as structured engineering reasoning

Chain of Thought formalizes what expert engineers intuitively do: breaking complex problems into logical, sequential steps that can be examined, validated, and improved. Enhanced with AI partnership, this structured reasoning becomes scalable organizational intelligence rather than individual expertise.

At its core, leveraging AI is about mastering the art of questioning. The transformation occurs when engineers move from asking AI “What is the solution?” to guiding AI through “How do we systematically analyze this problem?” This creates transparent reasoning pathways that preserve knowledge, enable collaboration, and generate solutions teams can understand and build upon.

As such, here is a reusable CoT template for technical decision-making:

“To solve [engineering challenge], break this down systematically:

1. Identify core constraints: [performance/cost/regulatory requirements],
2. Analyze trade-offs between [options] considering [specific criteria],
3. Evaluate effects on [downstream systems/processes],
4. Assess implementation risks and mitigation strategies.”

This template works across domains—thermal management, software architecture, regulatory compliance—because it mirrors the structured thinking that defines engineering excellence.

Practical applications in product innovation

CoT methodology proves most powerful in early-stage ideation, complex trade-off analysis, and compliance reasoning where traditional approaches miss critical interdependencies. Based on the target persona, this can translate in various use cases, such as:

Early-stage product ideation:

“To develop [product concept], systematically explore: 1) User pain points and current solutions, 2) Technical feasibility and core challenges, 3) Market positioning and competitive advantage, 4) Minimum viable approach to validate assumptions.”

Engineering trade-off analysis:

“When choosing between [options], evaluate: 1) Performance implications on [key metrics], 2) Cost analysis including lifecycle expenses, 3) Risk assessment and failure mode mitigation, 4) Integration requirements and future modification impacts.”

Compliance and regulatory reasoning:

“To ensure [system] meets [requirements], structure analysis: 1) Requirement mapping to measurable criteria, 2) Design constraint implications, 3) Verification strategy and documentation needs, 4) Change management for ongoing compliance.”

These frameworks transform AI from answer-generator to reasoning partner, helping engineers think systematically while preserving logic for team collaboration and future reference.

PLM integration—CoT as a digital thread enabler

CoT becomes particularly powerful when integrated into Product Lifecycle Management (PLM) and related enterprise resource systems—creating data threads that preserve not just what was decided, but why decisions were made and how they connect across development lifecycle. Just imagine these scenarios:

Design intent preservation:

“For [design decision], document reasoning: 1) Requirements analysis driving this choice, 2) Alternative evaluation and rejection rationale, 3) Implementation factors influencing approach, 4) Future assumptions that might affect this decision.”

Cross-functional integration:

“When [engineering decision] affects multiple disciplines, analyze: 1) Mechanical implications for structure/thermal/manufacturing, 2) Software considerations for control/interface/processing, 3) Regulatory impact and verification needs, 4) Supply chain effects on sourcing/cost/scalability.”

Digital thread connection points:

• Link design decisions to original requirements and customer needs.
• Connect material choices to performance targets and compliance requirements.
• Trace software architecture to system-level performance goals.
• Map manufacturing choices to cost targets and quality requirements.

This ensures that when teams change or requirements evolve, critical decision reasoning remains accessible and actionable rather than locked in individual expertise. From a business outcome perspective, this can contribute to continuity across product generations and reduce time spent retracing design decisions during audits, updates, or supplier transitions.

Strategic reality: revolution or evolution?

While CoT methodology delivers measurable improvements, the strategic question remains whether this represents fundamental transformation or sophisticated evolution.

Evidence for transformation: Though evidence remains scarce, early adopters of structured CoT approaches report measurable improvements in knowledge transfer efficiency, design review effectiveness, and decision consistency. Organizations consistently cite enhanced team collaboration, reduced rework cycles, and improved knowledge retention when engineering reasoning becomes explicit and traceable. These patterns suggest systematic capability enhancement rather than marginal improvement.

Case for evolution: Critics argue CoT merely formalizes what competent engineers have always done. Revolutionary breakthroughs—the transistor, World Wide Web, breakthrough materials—often emerge from intuitive leaps that defy structured frameworks, suggesting excessive systematization might constrain innovation. Regardless, the accelerating sophistication of AI demands that engineers critically assess not just what they build, but how they think.

Strategic balance: Successful engineering organizations are not choosing between structured reasoning and creative innovation—they are developing meta-skills for knowing when each approach adds value. CoT excels in complex, multi-constraint problems where systematic analysis prevents costly oversights. Pure creativity dominates breakthrough innovation where paradigm shifts matter more than optimization.

Future-proofing perspective: As AI capabilities accelerate from text generation to multimodal reasoning to autonomous design, organizations building frameworks for continuous methodology evaluation—rather than optimizing current techniques—will maintain competitive advantages through technological transitions.

Chain of Thought may represent the beginning of engineering’s AI integration rather than its culmination. The methodology’s emphasis on explicit reasoning provides tools for navigating technological uncertainty itself, perhaps its most valuable contribution to engineering’s digital future. CoT may be the missing link between today’s prompt-based AI assistants and tomorrow’s agentic co-engineers—moving from reactive support to proactive design collaboration.

Whether revolution or evolution, CoT offers engineers systematic approaches for amplifying problem-solving capabilities in an increasingly AI-integrated technical landscape.

The post How Chain of Thought drives competitive advantage appeared first on Engineering.com.

The increased role of computer software in manufactured products has added to the complexity of product testing. For example, automobiles and aerospace vehicles contain numerous electronic control units (ECUs) that handle various functions and input/output (I/O), making comprehensive testing difficult.

Software can also become part of the solution, as computer-based simulation offers ways to perform virtual testing. Instead of physically testing finished prototypes, manufacturing and engineering teams can simulate actual conditions and perform testing on digital models of products, saving time and expenses. While this helps accelerate testing, software simulations sometimes fail to identify issues encountered in physical testing.

To handle testing complexities, manufacturers often turn to hardware-in-the-loop (HIL) testing, which simulates real-world conditions for assembled products or components. By connecting a controller to a system simulating the operation of the product in real-world conditions, product teams can test products early and often in the design cycle while keeping testing as realistic as possible. HIL testing essentially replaces a physical model, such as an automobile transmission system, with a virtual representation of that system that simulates the physical model.

HIL process overview

HIL testing typically connects real controller hardware with a simulated system via a combination of analog and digital I/O connections. The simulated system is typically a mathematical model of the actual system. The model is executed in real time to simulate the behavior of the actual system based on inputs from the controller hardware.

Specific HIL testing processes vary by industry and application, but in general, the process starts with creating a digital model that simulates the physical system. The model may be created with commercial or custom-built software capable of modeling electrical, mechanical and hydraulic components as well as physical behavior related to fluid mechanics, thermodynamics and other physics-based specialties.

Before connecting the system to the controller, the model is typically run on a test system to simulate responses from controller hardware inputs and verify the system reacts in a realistic manner. Once verified, the controller hardware is connected to the simulation hardware to interact with the simulated system. The I/O connections may use communication protocols such as Ethernet, CAN and ARINC to send signals that emulate physical behaviors.

HIL testing with the real controller hardware generates a variety of data acquired from the controller and simulated system. The data is used to provide feedback to the controller, enabling the controller to adjust its behavior based on the simulated system’s responses. Measurement and verification include a wide variety of tests, ranging from normal operating conditions to fault conditions. Numerous iterations and refinements can be run to verify model accuracy and system performance.

Benefits of HIL testing

HIL testing offers numerous benefits to product designers and manufacturers. Because testing can be conducted before final assembly, it can identify potential flaws when they can be fixed more affordably and efficiently. Tests can be rerun to determine the efficacy of major fixes and minor adjustments. Such testing can also consider numerous scenarios without the time and expense required for physical tests. Automation can be used to conduct multiple scenarios, sometimes simultaneously, to accelerate testing and development.

By connecting real controller hardware to a simulated system, HIL testing offers more realistic testing than software-only simulation. It essentially combines aspects of both physical testing and software-based modeling. This combination also enables more collaboration amongst professionals with various backgrounds. Using primarily digital methods, test results can be shared readily with other stakeholders. Pure physical and software testing are often conducted by experts with specific expertise in those areas who report results back to product managers and others less interactively. HIL testing often involves collaboration up front and more ongoing access to hardware and system data throughout the process.

HIL testing also offers safety benefits, enabling teams to simulate conditions without exposing humans to dangerous situations. For example, an automobile braking system or other safety-critical systems can be tested using HIL techniques before testing in actual conditions.

Challenges of HIL testing

While typically not as expensive as physical testing of finished prototypes, HIL testing can be time-consuming and costly. Significant planning and development are required to build a simulation model, prepare the hardware controller for connection to the HIL system and monitor the testing.

The accuracy of HIL simulation can also be a challenge. Even with sophisticated hardware and software, it may not perfectly emulate actual systems, requiring ongoing testing and adjustment.

Collaboration among multidisciplinary teams can also prove challenging. While a wide range of perspectives can be beneficial, it can also require additional coordination and “cat herding” to achieve consensus on the approach and interpretation of results.

Present and future applications

A wealth of opportunities are available for teams able to properly address the various challenges. For example, the automotive industry has been particularly active in employing HIL testing. In addition to the previously mentioned transmission and braking examples, it can be used to test vehicle dynamics, steering systems, cruise control systems, advanced driver assistance systems (ADASs) and other systems employing ECUs.

In the aerospace industry, HIL testing can be used for flight control systems, avionics, navigation modules and a host of other areas. With the ability to perform real-time simulation, HIL has proven helpful for certifying aerospace systems and components.

It has also been used in the energy industry to simulate power plant behavior and grid reliability, in the electronics industry to test components and systems, and in industrial automation systems to evaluate system effectiveness before deployment. Infrastructure systems, such as water and wastewater treatment facilities, can use HIL to simulate scenarios such as peak demand and emergency scenarios.

Looking ahead, HIL testing will likely employ artificial intelligence and machine learning to further automate and refine testing procedures. A variation called virtual HIL (vHIL) testing is being used to create and execute tests before the actual ECU hardware is available. With the vHIL approach, testing can begin earlier and be automated to guide subsequent testing. As manufactured products become more complex, HIL and other methods will also become more advanced to meet the needs of product manufacturers.

The post How does hardware-in-the-loop (HIL) testing work? appeared first on Engineering.com.

The two largest aerospace companies – Airbus and Boeing – both have lofty goals for reducing their carbon emissions and becoming more sustainable, but many of their targets are set for 2050. To put that in perspective, based on average life expectancy in the United States, anyone reading this article today over the age of 53 is unlikely to be alive to see whether or not those targets are achieved.

Apropos of nothing, the average age of a C-suite executive is 56.

So, rather than looking to the far future, let’s focus on the targets for 2030 and what Airbus and Boeing have committed to achieving in sustainability initiatives over the next five years.

Reducing greenhouse gas emissions in aerospace

While it’s certainly not the only measure of sustainability, reducing greenhouse gas (GHG) emissions – particularly CO₂ – is frequently a top line item in the sustainability goals of large enterprises. Airbus and Boeing are no exceptions, with both manufacturers setting targets for their Scope 1, Scope 2 and Scope 3 emissions.

• Scope 1: emissions from sources an organization owns or controls directly
• Scope 2: emissions caused indirectly as a result of energy purchased for operations
• Scope 3: emissions generated as part of an organization’s supply chain

Airbus has stated that it’s aiming to reduce Scope 1 and 2 GHG emissions by 63% and Scope 3 emissions by 46% by 2030, using 2015 as its baseline year. Meanwhile, Boeing has stated that it’s aiming to reduce Scope 1 and 2 GHG emissions by 55% by 2030 using 2017 as its baseline. The company has also pledged to switch to 100% renewable electricity “purchased directly and via renewable energy credits” by 2030.

For Airbus, that amounts to an average reduction of 4.2% per year for Scope 1 and 2 emissions and 3.1% per year for Scope 3 emissions. Coincidentally (but probably not), Boeing’s average annual reduction in Scope 1 and 2 emissions works out to the same amount: 4.2%.

This puts both companies slightly ahead of the targets from the Paris Agreement in 2015, which settled on a 45% reduction (i.e., 3% annually) in GHG emissions globally by 2030.

Sustainable aviation fuel

Arguably the most immediately impactful effort Airbus and Boeing can make involves the use of sustainable aviation fuel (SAF), a synthetic jet fuel similar to kerosene but made from renewable feedstocks, such as used cooking oil and agricultural waste. According to Airbus, SAF can reduce CO₂ equivalent emissions by up to 80% on average compared to traditional jet fuel.

Currently, Airbus aircraft can operate a mixture of up to 50% SAF and traditional jet fuel, but the company has committed to being able to operate on 100% SAF by 2030. Meanwhile, Boeing stated in 2024 that its military aircraft can operate on SAF up to an approved 50% blend limit and has similarly committed to all production commercial airplanes being 100% SAF compatible by 2030.

Of course, there’s a big difference between compatibility and implementation, and a 2023 report by the World Intellectual Property Organization (WIPO) stated that SAFs accounted for less than 0.1% of all aviation fuels consumed. This may be due, in part, to the fact that SAFs are considerably more expensive than traditional jet fuel – as much as triple the cost, according to some estimates.

Other sustainability efforts in aerospace

Airbus and Boeing have both made a variety of other sustainability commitments. In order of their likelihood to contribute to sustainability over the next five years, these include incorporating recycled parts and materials into new aircraft construction, new propulsion systems that use hydrogen fuel cells or electricity, and investing in carbon capture and storage technologies.

Setting cynicism aside for a moment (with much reluctance), it’s clear that sustainability does matter to the aerospace industry. The two largest aircraft manufacturers in the world have made significant commitments to achieving their sustainability goals in the next five years, and where Airbus and Boeing lead, the rest of the aerospace industry follows. There’s no question that there’s much more that could be done (getting rid of private jets for example) but, hopefully, we’re on the right track to meet our targets for 2030.

The post How important is sustainability in the aerospace industry? appeared first on Engineering.com.

“The world is evolving, and navigation must evolve with it. GPS is disturbingly vulnerable to challenging environments, harsh weather conditions and cyberattacks, with rising threats of jamming and spoofing. The question isn’t if GPS will fail, but when. Operators need to build resilience now,” said Chris Shaw, CEO and co-founder of Advanced Navigation.

Robust navigation demands a layered, inertial-first and multi-sensor architecture — held together by intelligent software — that can adapt and scale to meet the unique demands of each mission. Embracing a software-defined nature means updates and enhancements can be deployed with minimal hardware disruption. This paradigm shift ensures truly resilient navigation for critical applications across defense, aerospace, robotics and autonomous systems.

To achieve this, Advanced Navigation, headquartered in Sydney, Australia, integrated a strategic-grade fibre-optic gyroscope (FOG) inertial navigation system (INS) with a new class of navigation aid: a Laser Velocity Sensor (LVS). The result is a fused hybrid architecture that delivers unprecedented precision and reliability in even the most challenging environments.

LVS is a terrestrial adaptation of LUNA (Laser Unit for Navigation Aid), a space-grade navigation technology developed for autonomous lunar landings. LUNA enables reliable navigation in the harsh environment of space by providing precise three-dimensional velocity and altitude information relative to the Moon’s surface. After several years of research and development, LUNA is set to be demonstrated aboard Intuitive Machines’ Nova-C lander as part of NASA’s Commercial Lunar Payload Services (CLPS) program.

By leveraging the engineering insights gained from LUNA, LVS adapts space technology into an Earth-ready solution for terrestrial GNSS-denied navigation.

Why the LVS hybrid works

At the center of every reliable navigation platform is a trusted source of truth: the INS. The company’s FOG INS, which is sensitive enough to detect the Earth’s rotation, provides that foundation by delivering precise attitude, and the LVS uses infrared lasers to accurately measure a vehicle’s ground-relative 3D velocity. LVS performs reliably on ground and airborne platforms, as long as it maintains a clear line of sight to the ground or a stationary surface.

Beyond its role as a velocity aid, LVS also enhances navigation resilience by detecting GNSS spoofing. By comparing its independent velocity measurements against GNSS-derived velocity, LVS adds an extra layer of security to Assured Positioning, Navigation, and Timing (APNT) strategies.

AdNav OS Fusion draws on sophisticated algorithms to interpret and filter sensor data. The software is designed to dynamically weigh the input from each sensor, adjusting in real time based on reliability scores, environmental conditions and operational context. This ensures continuous, high-confidence state estimation even when signals are lost, degraded or distorted. This inertial-centered, multi-sensor approach delivers a step-change in GNSS-denied navigation performance, compared to traditional methods.

Testing LVS resilience with real-world data

To validate the accuracy and resilience of the LVS hybrid system, the company conducted a series of rigorous real-world driving tests. Across five trials, the system delivered exceptional performance with an average error per distance traveled of 0.053% compared to a GNSS reference. 

At the starting point, GNSS on the INS was disabled in the state estimation process, forcing the system into dead-reckoning mode. RTK GNSS was logged separately as a reference. This approach allows for a direct comparison between the computed dead-reckoning solution and a trusted position reference.

The data below shows dead-reckoning results from a 23-km drive around Canberra, Australia. GNSS was not used at any point in the drive for heading or position. RTK GNSS is shown as the red line, while the LVS hybrid system’s result is shown in blue.

The next results are from a 19.2-km drive around the Parliamentary Triangle in Canberra, Australia. Again, GNSS was not used at any point in the drive for heading or position. RTK GNSS is shown as the red line, while the hybrid system’s result is shown in blue.

The figure below is a zoomed section from the first test drive, showing GNSS (red) drop out as the test vehicle drove through a tunnel, which completely denied the GNSS reference measurement. The hybrid system’s result can be seen in blue, showing it did not suffer from this error.

These drives were done repeatedly, demonstrating consistent and reliable results each time. 

The LVS hybrid system was also tested on a fixed-wing aircraft combined with a tactical-grade INS, demonstrating a final error per distance traveled of 0.045% over the course of a low-altitude flight over 545 km. These results demonstrate the system’s impressive ability to improve navigation performance of the INS in GNSS-denied or contested scenarios.

To read the company’s white paper for a more in-depth look into the technology, visit advancednavigation.com/tech-articles/laser-velocity-sensor-lvs-high-accuracy-velocity-aid-gnss-denied-navigation.

The post LVS hybrid system ushers in new era of navigational resilience appeared first on Engineering.com.

Robot path planning is a complex, with most workcells using multiple robots requiring tedious work to create interference zones and interlock signals that ensure there are no collisions during manufacturing.

Manually validating the mechanical design, planning robot paths, determining sequencing to hit optimal cycle time targets, and defining those interlocks can take a team well over 100,000 hours for a single project. This complexity often leads to failures in hitting cycle time targets, adding significant rework.

Resolver works by selecting and testing potential solutions tens to thousands of times faster than a human programmer. The goal is to quickly generate optimal, collision-free motion paths and interlock signals. This can accelerate workcell design from months to days.

The company says Resolver is essentially infinitely scalable robotic simulation power that can used to reduce the time required for many tasks, including:

• Generating accurate proposals
• Designing optimal tools and fixtures
• Producing optimal robot programs
• Adjusting for as-built deviations during commissioning
• Assessing and minimizing the impact of product design changes

“It is widely understood that the future of the manufacturing industry lies in robotics and automation. However, that future is slow to materialize because of the outdated, time-consuming, and inefficient processes commonplace in the industry,” said Peter Howard, CEO of Realtime Robotics. “Few manufacturers have the time or resources needed to enact real change. We’ve engineered Resolver to help manufacturers improve their engineering, programming and production processes – and drive greater value from their current and future investments in robots.”

How it works

Realtime Robotics’ Resolver supports path planning with any number of robots, at any phase of the workflow, generating results in minutes. The solution requires minimal onboarding and currently allows users to work directly within Siemens Process Simulate. Support for other leading simulation platforms will be rolled out later in the year, enabling teams to work directly within their preferred simulation tool.

“Resolver has the computational power to generate better motion paths than human programmers in both simple and complex workcells,” added Howard. “This is because Resolver searches the possibilities open to robotic arms, while humans tend to stay within the possibilities of the human arm.”

Users upload the workcell information, configure their sequencing and conditions, and execute a run. In minutes, Resolver will generate motion paths—including interlocks. The longer Resolver runs, the more options it provides, shortening the cycle time until the desired outcome is reached. The paths and interlocks can then be easily imported back into the simulation software for validation and operation.

Beyond determining optimal motion plans and interlocks, Resolver can help with fixture design, reachability validation, target sequencing, and robot task allocation. It can also be used to design the paths and interlocks for an entire manufacturing line from the start.

The post New robot path planning software cuts weeks of programming appeared first on Engineering.com.

This episode is brought to you by Hexagon. Please complete the registration form to watch the full conversation.

In the 122 years of flight, the only thing that hasn’t changed is the air itself. Wood and fabric gave way to light metal alloys, and composites. Piston engines were followed by gas turbines, and today, electric propulsion. Control has evolved from cables and pulleys to hydraulic and electric actuators with software acting as intermediary between pilot and aircraft.

The evolution has been continuous, but many experts predict that we are on the cusp of a step change in aerospace, driven by the confluence of new materials and propulsion technologies and guided by innovative computational techniques, including AI. It is $350 billion global market and it’s projected to double over the next 10 years.

And all of is happening within a new imperative, sustainability.

Joining engineering.com on this episode of The Engineering Roundtable are four experts that discuss this paradigm shift in aerospace:

Panelists:

Aziz Tahiri, Vice President, Aerospace Industry, Hexagon
Dr. Waruna Seneviratne, Director of the Advanced Technologies Lab for Aerospace Systems, Wichita State University’s NIAR
Duncan Smith, Head of Structures and Stress, Hybrid Air Vehicles
Keith Perrin, Solutions Director, Hexagon

Moderator:

Jim Anderton, Multimedia Content Director, engineering.com

* * * 

Download Hexagon’s new white paper, “Sustainability: Aviation’s epic challenge” to learn about the latest digital tools and processes that are driving sustainable innovation for a new generation of aircraft that will enable manufacturers to deliver them at scale.

And, explore Hexagon’s digital manufacturing solutions that helps accelerate this innovation in aerospace.

The post The future of aerospace in the era of disruption appeared first on Engineering.com.

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