Optical Solutions for Next-Gen Wireless Network Challenges

Advancements in the Internet of Things (IoT) and next-generation wireless networks like 5G and 6G have escalated the demand for higher bandwidth and faster data transmission rates.1 Optics offers innovative solutions, such as optical fibers and optical wireless communication (OWC) technologies, to address these network challenges.

Optical fiber communication uses lasers for information transmission, while OWC uses light beams to modulate information. High-speed and secure wireless communication using these optical solutions can strengthen the foundation of emerging fields, such as cloud computing, virtual reality, big data, and artificial intelligence.

Role of Optical Fiber in Enhanced Network Capacity

Optical fibers use electromagnetic waves (light pulses) for data transmission along a glass or plastic fiber as thin as a human hair. They do not use electricity and are favored for long-distance and efficient communication because of their high bandwidth and transmission speeds.

Transmitting high-quality audio, visual, and digital data over long distances using a single low-loss fiber optic cable eliminates the need for amplifiers and repeaters.

Technological enhancements in optical fibers have allowed optical fiber networks to gradually replace copper wires and become the prominent mode of communication.

Today, optical fibers connect all continents and form the backbone of modern communication structures in metropoles, cities, towns, and even homes. This shift can be attributed to the advantages of optical fiber communication, such as immunity from external noise, electromagnetic interference, fire, and explosions.

Further advancements in fiber materials and room-temperature semiconductor lasers have increased data capacity and reduced latency in optical fiber communication. Thus, the optical fiber medium is now being extended to the free-space medium for flexible last-mile connectivity.

These technologies are being deployed in wireless infrastructure to support the massive increase in data traffic and connectivity requirements.

OWC Technologies

Wireless communication provides IoT devices and users complete flexibility and necessary mobility. Most wireless networks currently use radio-frequency (RF) technologies. However, the RF network is too congested to future-proof wireless communication.

The expansion of the 5G cellular network has further pushed the limits of RF technologies. Thus, researchers are now focusing on OWC technologies, which operate across a broad, license-free spectrum covering infrared, visible, and ultraviolet bands. OWC technologies such as free space optics (FSO) and visible light communication (VLC) are great alternatives to RF communications in congested areas.

FSO communication systems, long used by the military, have recently gained commercial attention. They use light propagation across free space (atmosphere, space, or vacuum) to communicate between transceivers in the line of sight (LOS).

FSO uses a point-to-point mechanism and the infrared spectrum to transfer information at speeds up to hundreds of gigabytes per second, far surpassing those of optical fibers.2,3 The LOS nature of OWC also provides the network with an additional layer of physical security, making it more secure than optical fiber communication.

VLC uses the visible wavelength range and is widely employed in indoor, terrestrial, and underwater applications. It is favorable for communication and light-emitting diode (LED)-based illumination of living quarters, workspaces, and businesses. VLC links can be installed to accomplish indoor wireless networks and establish power line communication between buildings.

Among several potential applications, the concept of smart cities is a major driver of OWC research. OWC can be used to develop indoor systems capable of monitoring and adjusting building lights, ventilation, and temperatures according to ambient conditions.

They also support building management services such as power management, security, surveillance, and communications, utilizing OWC-compatible receivers for each function. In such smart environments, OWC can revolutionize healthcare through active medical implants like ventricular heart assistance devices, biomedical sensors, and wearable devices.

Integration Challenges and Technological Hurdles

Despite their potential, wireless technologies, including OWC, face several fundamental issues.

A primary challenge is integrating OWC with existing communication networks, as this requires LOS between modules to achieve high data transmission rates. Unfortunately, misalignment can hinder maintaining a reliable LOS link, especially in mobile applications.

In indoor systems, wireless networking systems must connect to many devices simultaneously. This can create imbalances due to the small sizes of detectors handling diffused optical power.

The mobile terminals in a wireless network randomly change orientation, obstructing the mobile terminal-fixed access point connection. This is due to the strict LOS requirements of OWC systems.

The presence of numerous optical link points in a network can lead to overlapping light signals and interference. This results in poor service quality due to signal fading, low mobility, limited range, slow data rates, and high costs.

Open communication is not entirely secure as it is prone to interference and jamming. Atmospheric twinkling (light intensity fluctuations across time and space) and unstable refractive indices due to changing air temperature can deflect light paths, increasing interference challenges and lowering the signal-to-noise ratio negatively. This negatively impacts the reliability and efficiency of the wireless network.

In indoor applications, point sources like lasers in wireless communication systems raise concerns over eye safety. Transforming these point sources into extended ones requires additional steps.

To fully leverage OWC technologies in next-generation wireless networks, issues related to frame rate, safety, synchronization, and ambient conditions must be addressed.
Future Prospects and Innovations in Communication Technology

The realization of 6G communication hinges on overcoming the continuously changing obstacles in the LOS path between a base station and a user. Researchers are therefore focusing on the terahertz band (microwave and infrared), which provides near-field links for mobile network users.

However, most current FSO systems are designed for far-field operation and are suitable for long-range links. To address the issue of blockages in wireless communications, novel terahertz links that exploit self-accelerating beams are currently under development.

Optical materials and components form the foundation of wireless optical networks. Their performance and sensitivity determine data rates, link distance, and overall link budget.

Significant efforts are being invested in advancing optical devices, such as high-gain antennas with directional beams, which can overcome free-space path loss and achieve ultrahigh data rates. Arrays of micro-LEDs and novel lasers, like vertical-cavity surface-emitting lasers, are being developed for high-speed OWC applications.

Hybrid systems that combine novel OWC technologies with existing RF wireless networks and fiber optics can improve the overall communication system with reduced interference and higher capacity. Thus, dynamic network management algorithms like software-defined networking are being developed to seamlessly integrate optical wireless networks with existing RF wireless networks and realize heterogeneous wireless networks.

In this regard, integrated photonics and advanced modulation techniques can help achieve efficient and scalable next-gen wireless communication.

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The future of AI in the aerospace industry

 

Using AI to fly an airplane will be an enormous achievement; the ultimate reflection of AI’s ability to manage complexity. It will be a critical piece to implementing the changes that will be required in the next decade as A&D organizations reassess manufacturing automation in their factories. The recent pandemic, furloughs, bankruptcies and retirements are all impacting productivity and widening the talent gap, forcing the industry to adopt new technology such as AI to reimagine their business.
What is AI and machine learning?

AI, like so many technology buzzwords, can mean different things to different people. For us, an AI system is one that leverages software functions created through a machine learning process rather than through traditional programming. Data, rather than source code, is the critical element. The performance of an AI application is shaped by the data used to train the application.

Without going into detail on machine learning algorithms or approaches, which is beyond the scope of this paper, we can generalize that the power in AI comes from machine learning’s ability to model complex systems and environments far beyond what we can reasonably build in traditional software.

Imagine building a speech recognition system through traditional programming -having a function for every word or a case statement for every pronunciation or accent. It would take a staggering amount of time to cover even 10 percent of the English language. Machine learning models, on the other hand, have made short work of this task, to the point that robust and accurate systems can understand a full vocabulary from hundreds of languages and accents.
Analytical AI vs. operational AI

Today, two distinct classes of AI applications are emerging across industries. The first is analytical AI, as in the type of system that can predict when a machine is going to fail, detect credit card fraud or recommend the next book to buy on Amazon. Operational AI, the second class of AI applications, actually does something in the physical world. It can manage a factory process, fly a plane, drive a vehicle or act on predicted events. It’s artificial intelligence at work.
Analytical AI is maturing

The development tools and environment for building analytical AI applications are rapidly maturing. Previously, data scientists wrote Python code to enable most algorithms and approaches to machine learning. They did a lot of heavy lifting, extracting from various sources and then transforming the data to ingest into AI algorithms.

We can now address some of the more complex challenges the industry is trying to solve. Where does the industry need to pivot, from a technology point of view, so it can thrive during moments of disruption? We can also see coming technology advancements such as the introduction of more drones and air taxis.

What the industry really needs to do is to scale up the building of complex operational AI systems. We need the self-driving car level of AI across the A&D industry. To do this, we need a different approach to developing AI systems. The scale of data needed to train these complex systems is many orders of magnitude larger than what has been done before. This data must be well-managed through a significantly more complex machine learning system, where AI software is often trained in stages, leveraging data that is synthetically generated at key points. We must also consider the testing and validation environment, since we are talking about building systems that operate complex machinery in the real world.
Operational AI in aerospace and defense: Three use cases

Unlike AI development for analytical use cases, the development of toolchains and methodologies for complex operational AI is only just emerging. We believe that success in any complex operational AI endeavor will be determined most critically by having access to a robust development ecosystem.

Factory automation. Many of the woes in A&D over the past 10 to 15 years have been caused by the inefficient supply chain, which has created cost overruns, delays and even bankruptcies. Many companies are struggling with major supply chain problems, from a lack of control-in both delivery and timeliness-to a lack of quality control. Across the industry, supply chain problems have cost companies tens of billions of dollars in unnecessary costs. In short, the supply chain in the A&D industry has been a major problem.

The automotive industry is highly digitized and highly automated from a manufacturing standpoint, because there is a relatively stable and manageable supply chain of 10 or 15 original equipment manufacturers (OEMs), along with cooperation and partnerships so everyone can invest in automation. By contrast, the global aerospace industry lacks this level of partnership and, critically, the necessary manufacturing volume to make similar investments. A&D companies depend on a very deep and unwieldy supply chain with almost no automation and very little planning control or quality control.

In terms of delays and quality issues, the root causes are poor manual processes and poor manual planning. Underpinning automation, the industry also needs a strong digital foundation where both machine usage and labor can be tracked and optimized automatically. Automation not only brings conformity and control, but it also brings an automatic improvement in quality that is sorely needed. The answer to automating in A&D lies in using general-purpose robotics, more specifically, cobots (collaborative robots), which are general-purpose humanoid robots that can work among a human population on both factory floors and typical A&D production lines. Cobots introduce automation at a much lower cost because retooling of manufacturing programs is not needed.

These cobots still need to be programmed, and that’s where AI comes in. Programming traditional automation solutions is an expensive proposition, as each task needs to be custom programmed to fit a particular factory and production line. With AI, programming countless different tasks one by one is not needed. AI plus cobots enable the automation of that operational program and factory at high, low and no scale, and at a dramatically lower cost, so that implementing automation is achievable, given the constraints of the aerospace industry.

Air traffic management. AI is critical for managing the anticipated disruptions in this industry over the next 10 years. One such disruption being watched closely is the pending introduction of drones and air taxis, often known as urban air mobility systems. Thus, the second use case is building an AI air traffic controller.

Today, air traffic management is generally managed by people. Based on many projections of this growing industry, in 15 to 20 years there will be 30 times the volume of air traffic flying over a large city such as Los Angeles than there is now. Human beings would be hard-pressed to manage that huge amount of air traffic, and it may be impossible.

But this is not the type of problem where more humans can be added and each person given a smaller slice of the air traffic pie when we consider the amount and type of new air traffic expected to operate, especially at the 0 to 3,000-foot level. Given all this, there is wide consensus that the industry needs AI to manage this exponentially higher level of complexity.

Fully autonomous vehicles. This third use case is obvious because we are inundated with news about the self-driving car every day. First, being autonomous is different from being unmanned. Drones flying today don’t have pilots sitting in them, but they are still overseen by a pilot from the ground. Such drones have a ground station that has aspects of a cockpit repurposed on a desk, with a human flying the drone remotely.

The future progression from this state is full autonomy, where an AI system is constantly evaluating and reacting to the airspace and making decisions to act in accordance with its mission. The impetus for fully autonomous flight is the same as it is on the ground. A new, generally accepted roadmap has now been published and with autonomy, not only can we imagine a more efficient world, but we can imagine a safer one as well.
Creating complex operational AI systems

To create complex operational AI systems in the A&D industry, very different needs must be met for data management and for algorithm creation and implementation. Very robust simulation is also needed in the testing phase. Above all, there is a strong need for a solution that enables complex operational AI DevOps.

When training an AI system, there are two datasets. First is the historical dataset for the predictive use case containing the instances of what is being predicted. Next comes a testing or validation dataset. In the complex operational world, organizations often investigate neural networks to handle the pattern recognition. Algorithms can be used to automatically build better and better neural networks, based on the performance of the best-performing neural networks from past iterations.

But given the much higher complexity of the environment in which pattern recognition and training are run, higher-level approaches must be considered. For complex operational AI, the various machine learning approaches demand data at a higher scale and management at a greater complexity.

What is needed is a tiered approach to building different datasets. Start with a dataset that is focused on the physics of what you are trying to model; then build a dataset that accounts for the operating environment. After that, extrapolate the data to create a dataset that is orders of magnitude larger.

Algorithms then need to be constructed to create synthetic data to fill into that next-larger phase. Plus, building a robust simulated environment that matches real life can be a key factor in acceptance, and where necessary, certification. For example, certifying an airborne system is all about proving it is safe, and complex operational AI DevOps can play a crucial role in providing a robust simulation environment in the testing phase.
Operational AI is not easy, but it is possible

Building complex operational AI systems is no simple task. There are many challenges in managing the development environment and specific workflows. Data management over the full development life cycle is key, as is using the right technology to manipulate, extrapolate and scale data. It is essential to build a system that effectively manages and curates the data that represents the foundational dataset; then integrates, manages and curates the data from that environmental representation dataset; and ultimately provides the platform and simulation for the synthetic extrapolation of that merged dataset into a much, much larger dataset.

It is no easy feat to provide the data hosting, management and curation environment through these data stages that are each so massive in scope. Providing the simulation environment is enormous, because that is the “secret sauce” needed to be able to take that large dataset, combine it and then extrapolate it to ultimately produce a robust algorithm.

The good news-not only for the A&D industry but for all industries-is that a solution does exist that provides the DevOps environment to make all this come together and work at scale. DXC Robotic Drive Cloud Services on AWS is the first solution that provides a soup-to-nuts development toolchain and management environment for building complex operational AI.

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Aerospace engineer Ron Barrett-Gonzalez named National Academy of Inventors Fellow

 

LAWRENCE-For the second year in a row, the National Academy of Inventors has added a Jayhawk engineer among its fellows.Ron Barrett-Gonzalez, professor of aerospace engineering, was named among the 170 academic inventors in the 2024 Class of NAI Fellows on Dec. 10. Election as an academy fellow is the highest professional distinction awarded solely to academic inventors.

Barrett-Gonzalez directs KU’s Adaptive Aerostructures and Aircraft Design Laboratories, which support the design, research, development and flight testing of a variety of unmanned aerial vehicles (UAVs) ranging in speed from hover through hypersonic. The lab has pioneered the world's fastest micro quad-copters, known as QuadRockets, which can reach up to 130 mph.

His latest inventions include guided hypersonic ammunition for air-to-air combat, a flight safe flying motorcycle propulsor, flying Jayhawk and even a remote-controlled flying feather-ball. The ammunition and flying feather-ball have been licensed by Watson Industries.

He has disclosed more than 38 inventions to the KU Center for Technology Commercialization (KUCTC) and is the primary inventor on 23 issued utility and design patents, most with student co-inventors.

“It has been a pleasure to work with the KUCTC,” Barrett-Gonzalez said. “They make the patenting and licensing process easy for inventors like me.”

Cliff Michaels, the center’s executive director, said, “KUCTC, as KU’s technology transfer office, was happy to help nominate Dr. Barrett-Gonzalez for this recognition. He has been one of our most prolific inventors, collaborating with the office to help protect and advance innovations from his lab toward the market.”

Barrett-Gonzalez also guides winning teams of KU engineering students whose designs have included an unmanned hypersonic spy plane, a disaster-relief medical quadcopter, a flying motorcycle and a family of fast, eco-friendly jets dubbed Skyblazers.

“Innovation and invention are core components of the culture and mission of KU research, and are among the many ways that research at KU is translated into real-world solutions that make a difference in our community and our world,” said Shelley Hooks, vice chancellor for research. “This recognition acknowledges the combination of grit, inspiration and collaboration that define great inventors like Dr. Barrett-Gonzales. We are especially proud that Ron is a KU alumnus who has continued his career here as an engineering professor, educating and inspiring the next generation of Jayhawk innovators.”

He is the current head of KU’s Aerospace Design Program. Over the past 20 years, his students extended KU’s winning streak from 60 to 104 awards total, building on the records established by the program’s founders, the late professors Jan Roskam and Saeed Farokhi, whom Barrett-Gonzalez considers his most impactful mentors.

Ron’s contribution to the field and to the School of Engineering are a source of inspiration, said Mary Rezac, dean of the School of Engineering. His innovation and mentorship of students are essential in helping KU Engineering maintain its place as a global leader in aircraft design.

Barrett-Gonzalez received a bachelor's degree in aerospace engineering from KU before earning a master's degree in the discipline from the University of Maryland. He returned to KU for his doctorate in aerospace engineering.

Since its inception in 2012, the NAI Fellows program has grown to include more than 2,000 exceptional researchers and innovators, who hold more than 68,000 U.S. patents and 20,000 licensed technologies. NAI Fellows are known for the societal and economic impact of their inventions, contributing to major advancements in science and consumer technologies. Their innovations have generated more than $3.2 trillion in revenue and generated more than 1.2 million jobs.

KU was named a member of the National Academy of Inventors in 2013. Since then, seven faculty members have been named fellows while at KU:

• 2024-Ron Barrett-Gonzalez.
• 2023-Brian McClendon, research professor in the Department of Electrical Engineering & Computer Science.
• 2018-Mark Shiflett, Foundation Distinguished Professor of Chemical & Petroleum Engineering.
• 2017-Cory Berkland, Solon E. Summerfield Distinguished Professor in KU’s departments of Pharmaceutical Chemistry and Chemical & Petroleum Engineering.
• 2016-Raghunath Chaudhari, Deane E. Ackers Distinguished Professor of Chemical & Petroleum Engineering.
• 2015-Val Stella, distinguished professor emeritus in the Department of Pharmaceutical Chemistry, and Bala Subramaniam, the Dan F. Servey Distinguished Professor in the Department of Chemical & Petroleum Engineering.

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Michigan Tech Introduces New Bachelor Of Science In Aerospace Engineering

 

Study In US: Michigan Tech College of Engineering has announced a new course from fall 2025. The Bachelor of Science in Aerospace Engineering was introduced with experiential opportunities, an industry-guided curriculum, and talented faculty.

Interested undergraduate students can apply for this course for the 2025-26 academic year. The Department of Mechanical and Aerospace Engineering (MAE) will conduct this course.

While commenting on the new course, Professor Jason R. Blough, the chair of the department said, "The addition of this new degree is in recognition of the expertise of our faculty, growing research in the areas of aerospace and space technology and engineering, and a tremendous amount of student interest. We are very excited to welcome our first freshman class into Aerospace Engineering in the fall of 2025.

Our students have been asking for this degree and we listened. We're thrilled to launch the new Aerospace Engineering degree here at Tech. With the technical chops and innovative, hands-on focused curriculum to train the next generation of aerospace engineers, the sky is no longer the limit as our students tackle tomorrow's space and aircraft needs. We can't wait to get started!," said Michelle Scherer, dean, College of Engineering. Benefits Of Pursuing Bachelor Of Science In Aerospace Engineering At Michigan Tech

The following are the benefits of this course:Students will get the opportunity to work with various expert researchers and faculty on research projects related to the Space Systems research group.
They will get hands-on product development experience.
Michigan Tech Aerospace Engineering Research Center (MARC) will be the focal point of all the related activities.

Planetary Surface Technology Development Lab will train them apart from developing new technologies to explore the surface of Mars, Moon and beyond.

From satellites to autonomous flight to aircraft design, the Michigan Tech College of Engineering is leading the pack in all things aircraft and space, added Michelle Scherer.

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Future Trends in Global Advanced Manufacturing

 

Flexible Manufacturing

From the strategic plans of developed countries regarding the development of advanced manufacturing, the concept of manufacturing and its added value are gradually shifting from hardware to software, services, and solutions-intangible assets. Compared to traditional manufacturing, today’s manufacturing is largely driven by software, which enhances hardware functionality, controls it, and profoundly impacts it. Moreover, unlike traditional hardware products, the demand for services and solution-based products attached to goods is rapidly increasing.

Flexible manufacturing refers to increasing product-added value by expanding services and solutions. As opposed to hardware, the software, services, or solutions embedded within a product are usually intangible and flexible. In flexible manufacturing, “hardware” production is no longer viewed as the sole purpose of manufacturing. Instead, it’s recognized that software plays a dominant role in the manufacturing process, and the services or solutions produced by goods have a significant impact on the value of manufacturing. For example, at modern ports, the use of unmanned intelligent technologies allows AGV Transfer Carts to operate autonomously throughout the entire process. Thus, future manufacturing must abandon the traditional “hardware-focused” mindset and develop from the perspective that added value arises from software and services.

Software-led Innovation: In recent years, developed countries have increasingly used software to define product functions and performance, shifting the source of manufacturing product value from hardware to software. This shift has raised the entry barriers to the manufacturing industry. Electronic products are a typical example, where the majority are preloaded with operating systems and embedded with various software features. Many electronics can install additional applications (apps) through network connections. Even in the automotive industry, traditionally a hardware-dominated sector, the phrase “software determines product value” is becoming increasingly evident. Software now coordinates various hardware components in vehicles to improve fuel efficiency, and autonomous driving technology heavily depends on software.

In the U.S., companies like GE and IBM have long emphasized the role of software. GE moved away from traditional manufacturing thinking, investing heavily in software and data analytics, transforming itself into a data analysis software company. IBM is even more advanced, recognizing the coming era of big data management and focusing on the mathematical analysis abilities required for such management. Similarly, Europe has long realized that the future competitiveness of manufacturing on a global scale will depend on software. In the EU framework program, a substantial investment of 2.7 billion euros was dedicated to embedded software research (ARTEMIS). Large companies like Siemens and Bosch have also transitioned to IT enterprises.

Service and Solution-Oriented Business Models: Another key trend in the advanced manufacturing of developed countries is a strong focus on after-sales service, customer service, and solution-oriented businesses. The future manufacturing business model will be centered around continuously solving customer problems. In this model, companies will no longer only sell hardware but will generate additional value through the sale of maintenance services and providing follow-up services for the products. This shift toward service-oriented manufacturing has become common in the U.S., Germany, and the UK. Large U.S. companies tend to standardize services and solutions and promote them in emerging markets, while German and UK companies often succeed by consulting and making “manufacturing services” the core business model.

From “Physical” to “Information”

With advances in packaging and digitalization, the production and processing technologies for components are rapidly shifting to emerging markets, making it difficult for the profits from individual components to be maintained. Manufacturing in developed countries is increasingly focusing on assembling components into packaged systems, modularizing certain functional units, and systematizing functions to add value. Modularization involves assembling standardized parts to design products, allowing for a quicker response to diverse market demands and fulfilling consumers’ varied needs. However, modularization is just one feature of the product; future manufacturing will place greater emphasis on systematization, expanding new applications and services. By adopting a system-driven approach, manufacturers can gain more added value with “information” functionality compared to the “physical” components. Without controlling the system, even the best components will not dominate market prices.

American companies have consistently sought to obtain added value by focusing on upstream value chains, exemplifying the system-based approach. Companies at the upper end of the value chain control the market through systems. GE, for example, transitioned to an energy systems company in the 1980s and now applies those successful experiences to areas like healthcare services. Germany’s strategy revolves around Cyber-Physical Systems (CPS), with large companies like Siemens and Bosch fully recognizing the importance of systemization. Bosch, for instance, has launched packaging systems based on the AUTOSAR international standard and is aggressively expanding into emerging markets like India and China.

From “Group” to “Individual”

As developed countries transfer large-scale, mass-production manufacturing bases to emerging markets, a shift toward customized, small-batch production is becoming mainstream. The future direction of manufacturing in developed countries will focus on personalized demands, leading to the rise of “mass customization.” At the same time, consumers will have the ability to turn their own needs into production specifications.

With the widespread adoption of digital and information technologies such as 3D printing, the barrier to entering manufacturing is decreasing, and even individuals who need factories or production equipment can easily participate in manufacturing. This trend suggests that unexpected enterprises or individuals may participate in manufacturing, potentially changing business models significantly.

In the U.S., where individuality is strongly valued over organizational conformity, the trend toward personalized manufacturing is becoming evident. Smaller-scale, highly specialized manufacturing businesses focusing on efficient production and design flexibility are emerging in urban areas. These businesses offer customizable services based on consumer needs, creating differentiated competition against mass production.

Interconnected Manufacturing

Today, many products can access the internet, and society is becoming more networked. Smartphones and “smart appliances” are common examples. As cars move toward autonomous driving, vehicles may one day function as mere network terminals. Traditional material handling equipment like overhead cranes and gantry cranes are also starting to integrate the Internet of Things (IoT). The continuous networking of products, just as systemization is important, signifies the rise of “interconnected manufacturing,” where mastering control over the network will be key. Companies that first take control of this will benefit from first-mover advantages.

With the spread of information technology, the internet, and e-commerce, the competition in manufacturing markets is shifting. Manufacturing enterprises are now required to continuously gather information via networks and respond rapidly to market demands. They must also integrate and share resources effectively to optimize usage.

Interconnected manufacturing allows for rapid market response, quickly reconfiguring and dynamically collaborating with other manufacturers to allocate resources. By improving product quality, reducing time-to-market, and increasing market share, it also helps share the costs of infrastructure, and equipment investment and reduces business risks.

As a future trend, factories will evolve into interconnected facilities through the internet, advancing toward the trend of “smart factories.” This will involve collecting and analyzing data from the production floor and feeding it back to consumers. The data collected from the factory floor will serve as big data, which, when analyzed, will uncover new business opportunities. Processing the vast data gathered from hardware in the factory will largely determine the value of services and solutions.

The U.S., with its IT giants like Google and IBM, is leading in big data applications, placing a strong emphasis on creating new value for society. Google has acquired several manufacturing companies to gain control of the market. Similarly, GE is investing in data analytics and software development, collecting data from the shop floor to provide solutions and explore new business opportunities. In Germany, factory intelligence is a key national strategy, aiming to maximize the capabilities of factories through information technology.

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Smart Textiles New adhesive systems for e-textiles and wearables

 Webinar will showcase materials specially designed to meet the high requirements of the flexible electronic devices sector

 

Bonding and creating an electrical connection in a single step-small chips are fixed to flexible tracks with a conductive connection.

A free-to-attend webinar on wearable sensors, e-textiles, printed electronics and related fields is being held by TechBlick, the Frankfurt, Germany-based digital learning platform for industrial markets, on December 2nd.

Among presenters will be Tobias Kaposim of Panacol, the developer of flexible conductive adhesives for thin film solar cells and flexible, in-mould electronic wearables

“When it comes to electronics, characteristics like bendability, flexibility, foldability and wearability are becoming increasingly important,” he says. “To produce functioning devices with those characteristics it is necessary that all device parts are flexible or bendable. Usually, the electronic parts-especially those mounted on PCBs or flexible PCBs-are connected by soldering, but solder materials are very rigid and inflexible.”

In addition, he adds, soldering temperatures of 316-371°C are an increasing challenge for heat-sensitive modern electronic devices. Soldering to create conductive connections between the separate electronic parts is therefore not an option for modern flexible electronic devices.

Conductivity

Conventional conductive adhesives were invented for this purpose, and already make it possible to bond two electronic components on circuit boards without major temperature stress. These conventional adhesives, however, must be applied on a hard surface to create a high conductivity. For softer more flexible surfaces, like thin film solar cells, in-mould electronics or electronic wearables, other ways of contacting are required, in addition to a high flexibility of the whole device.

Epoxy-based flexible conductive adhesives are a smart alternative to bond and electrically connect components on flexible circuits in a single step. The adhesives are as flexible as the materials they are applied on, and cure at low temperatures.

The latest generation of electrically conductive adhesive systems are specially designed to meet the high requirements of the flexible electronic devices sector. They are particularly suitable for bonding with temperature-sensitive films or flexible PCB materials and possess high peel strength and extreme vibration resistance. Flexible conductive adhesives allow component attachment with minimal temperature stress.

                                      Stress-strain diagram of brittle and flexible adhesive systems

These features result from the high flexibility of the adhesive systems themselves. Even with a silver content of 80% and more, they show a significantly higher strain compared to brittle conventional adhesives. As shown in Figure 2, typical tensile test results indicate that flexible conductive adhesives (in grey) are minimally stressed when strain is applied. In comparison, the brittle conventional conductive adhesives (blue) become severely stressed as more strain is added. The new adhesives can withstand vibration and bending loads without breaking.

Breakage

The behaviour of a traditional conductive adhesive and a flexible conductive adhesive film are shown on a bendable substrate in Figure 3. The two different adhesives were applied on a thin bendable copper foil and subsequently bent in a manner that might be required in a real flexible electronic device. When the foils were bent, the conventional, more brittle adhesive shows clear signs of breakage. In comparison, the flexible adhesive adapts to the movement and shape of the copper foil and remains completely unimpaired.

                       Comparison of bent foils with brittle and flexible electrically conductive adhesives

Compared to other traditional conductive adhesives and solder, this new generation of flexible conductive adhesives provides additional important benefits.

Flexible conductive adhesives show excellent adhesion to plastics, including polyimide, PC, PVC, ABS, and FR4 board. They can be applied in very thin layers and are lightweight. These flexible adhesives can be precisely dosed and applied quickly in high volumes for automated manufacturing, which makes them perfectly suitable for die attach applications and component assembly on flexible films and PCBs.

A further advantage is the very easy handling and storage of these materials. Thee single-component adhesives can be dispensed, and cured within minutes at temperatures as low as 100°C. This makes it possible to bond semiconductors and create electrical connections at the same time. Furthermore, they only need to be cooled and not frozen during transport and are not classified as dangerous goods for transportation.

See the full webinar agenda, register for the free and connect with industry leaders including Panacol here.

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