Covalent Expands Testing with AVE3

How Covalent Accelerated Advanced Materials Testing with the AVE3 Advanced Video Extensometer

About

Covalent is an advanced materials science and analytical services platform headquartered in the heart of Silicon Valley.

Location

Sunnyvale, CA

Laboratory Size

30,000 ft² | 75+ Instruments

Website

www.covalent.com

"The AVE3 has streamlined our workflow by eliminating the need to manage a separate contact extensometer for each gauge length. It has also reduced operator-to-operator variability, since once the method is set up, it is much more standardized and repeatable across users. That efficiency helps us improve turnaround time, increase testing speed, and offer more competitive pricing to customers."

Shivesh Sivakumar, M.S.
Manager, Mechanical and Thermal Analysis at Covalent

Supporting Innovation Across Industries

Covalent is an independent analytical laboratory supporting technology‑driven industries ranging from semiconductors and energy storage to advanced materials and electronics. As a trusted partner within the Silicon Valley innovation ecosystem, Covalent provides specialized mechanical testing and materials characterization services that help customers overcome critical development, qualification, and failure‑analysis challenges — without the need to build every capability in‑house.

To meet the demands of fast‑moving R&D and increasingly complex materials, Covalent continuously evaluates and adopts advanced testing technologies that expand its capabilities while maintaining efficiency and data quality.

The Challenge

Versatile, High‑Accuracy Strain Measurement — Without Slowing Down

As a service lab supporting a wide range of customers and test standards, Covalent routinely tests specimens of varying sizes, geometries, and materials. These include plastics, thin films, battery separators, metallic foils, and miniature metal specimens — many of which are fragile or incompatible with traditional clip‑on extensometers.

Key challenges included:

Accurately measuring strain across diverse standards such as ASTM D638, ASTM D882, and ASTM E345
Handling thin or delicate specimens without influencing test results
Minimizing setup time and operator‑to‑operator variability in a high‑throughput lab environment
Expanding testing capabilities to support new and demanding customer applications

Traditional contact extensometers often require removal mid‑test and additional operator intervention, creating inefficiencies.

Plastics Testing Application with the AVE3

The Solution

AVE3 Advanced Video Extensometer for Non-Contact Strain Measurement

To address these challenges, Covalent implemented the Instron^® AVE3 Advanced Video Extensometer, a non‑contact, video‑based strain measurement system designed for versatility and precision.

The AVE3 stood out for its ability to:

Support tensile, compression, and flexural testing on a single platform
Accurately measure strain on thin, fragile, and non‑standard specimens
Reduce setup time and operator intervention
Deliver consistent, repeatable results across users and applications

"Versatility is a major driver for us. The AVE3 is a strong fit because it gives us the flexibility to handle many different applications on one platform — and it’s especially valuable for materials that aren’t well suited for contact extensometers, such as battery separators and metallic foils."

Josh Campbell, Ph.D.
Member of Technical Staff at Covalent

The Results

Faster Throughput, Expanded Capabilities, and Confident Data

Since adopting the AVE3, Covalent has seen measurable improvements in efficiency, data quality, and testing capability.

Key outcomes include:

Significantly faster testing workflows | The non‑contact approach eliminates the need for multiple gauge‑length extensometers and reduces setup complexity. Once a method is established, testing becomes highly standardized.

At least 3× faster turnaround time | Streamlined setup and reduced operator variability have dramatically improved throughput — enabling faster delivery and more competitive pricing for customers.

Improved repeatability for delicate materials | For challenging applications such as battery separator testing, the AVE3 delivered excellent repeatability across multiple replicate tests, increasing customer confidence in the results.

Expanded service offerings | The AVE3 has enabled Covalent to test specimens that were previously incompatible with traditional extensometers. In fact, the system made it possible to support one of the lab’s largest clients — work that would not have been feasible otherwise.

"The AVE3 has significantly improved our ability to make accurate strain measurements while keeping setup fast and efficient."

Shivesh Sivakumar, M.S.
Manager, Mechanical and Thermal Analysis at Covalent

Delivering Value Where It Matters Most

By integrating the AVE3 Advanced Video Extensometer into its testing portfolio, Covalent has strengthened its role as a high‑value partner for advanced materials testing — helping customers move faster, generate reliable data, and solve problems that traditional contacting approaches can’t address.

See What Non‑Contact Strain Measurement Can Do for Your Lab

Explore the AVE3

Boston University | KABlab

About

Boston University's KABlab studies hierarchically structured soft materials including polymers and smart fluids.

Location

Boston, MA

Website

www.kablab.org/

At Boston University’s College of Engineering, an autonomous research system combines automation and machine learning to accelerate the pace of creating the most efficient energy-absorbing shape.

What is Energy Absorption Efficiency?

Keith Brown, Associate Professor of Mechanical Engineering, Physics, and Materials Science & Engineering at Boston University, explained to us that “energy absorption efficiency is a metric that is defined in the context of protecting a specific system and is used to define how much energy can be absorbed before that system is damaged.”

This is an important metric for a wide range of products – including bike helmets, packaging materials, etc. Applied to automobiles, vehicles have what is referred to as “crumple zones” which are areas of the vehicle designed to deform during a collision to absorb the impact energy, protecting the passengers.

The energy absorbing efficiency defines how much weight or volume the crumple zone must have in order to keep occupants safe. A high energy absorption efficiency means the protective components can be lighter or smaller.

Up until recently, the energy absorption efficiency record across all tested designs had stood at 71.8% – but in January of 2023, the KABlab created a structure that broke the record at 75.2% efficiency.

Video credit Boston University

How Automation and Machine Learning Helped Them Do It

The KABlab is a laboratory within Boston University’s College of Engineering that studies hierarchically structured soft materials including polymers and smart fluids. As part of these studies, they are interested in the mechanical properties of polymer structures produced using additive manufacturing.

Illustration of the MAMA BEAR self-driving lab.¹

Within the KABlab is a robot named MAMA BEAR (Mechanics of Additively Manufactured Architectures Bayesian Experimental Autonomous Researcher) which is a self-driving lab that combines multiple 3D printers, an Instron universal testing machine, a collaborative robot (cobot), and machine learning to autonomously create and test structures. In 2021 this system was put to work with the ambitious goal of creating a shape capable of absorbing the most energy possible – and it’s been running near-continuously ever since, resulting in over 25,000 tested 3D printed structures.

"Consistency is the main challenge in this type of work. From this perspective, our Instron system has been exceptional."

Keith Brown
Associate Professor of Mechanical Engineering, Physics and Materials Science & Engineering at Boston University

The Setup

Using 11 independent geometric parameters (which allow for more than a trillion unique design possibilities), MAMA BEAR 3D prints a small plastic structure – recording the shape and size. Then a cobot moves the structure through the system to the Instron universal testing machine where it undergoes a compression test. From this testing, the system measures how much energy the structure absorbed and how its shape changed after being compressed.

The process then starts over with the next 3D printed structure – but its design and dimensions are modified slightly by MAMA BEAR’s machine learning algorithm that automatically interprets the data from previous experiments and uses it to select subsequent designs to test.

Each maximum energy absorption efficiency measured over the first 21,500 experiments.¹

How the Data Being Used

The project was sponsored by the US Army and the research is currently supporting the development of high-performance structures for use in helmet pads. KABlab has also made the data available for all to see and use at kablab.org/data.

Even though MAMA BEAR already broke the record with 75.2% energy absorption efficiency, KABlab is keeping the system running to see if they can continue to improve upon their own record. After all, there are still more than a trillion unique design possibilities to test.

"The [Instron] system is very user friendly. Training new students is a fast process owing to the simple nature of the Bluehill software."

Keith Brown
Associate Professor of Mechanical Engineering, Physics and Materials Science & Engineering at Boston University

We’d like to thank Keith Brown, Associate Professor of Mechanical Engineering, Physics, and Materials Science & Engineering at Boston University, for discussing this work with us which helped inform this article.

Additional Reading

A Robot on a Mission
The Published Research on These Experiments: Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

References

¹ Snapp, K.L., Verdier, B., Gongora, A.E. et al. Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership. Nat Commun 15, 4290 (2024). https://doi.org/10.1038/s41467-024-48534-4

UCL Low Force Testing with ElectroPuls

Case Study | Bio | London, UK

“The ElectroPuls is able to give you a complete indentation all the way through, providing lots of information, at a very precise scale. .”

- Robert Shaw, UCL Medical Student

Mechanical testing tissue microstructure is becoming an increasingly active area of research, especially in oncology. It has been shown that changes in this microenvironment are key in cancerous growth, namely an increased tissue stiffness. External mechanical forces can directly influence cell signaling regulating and influencing growth. Investigating the mechanical properties of the microenvironment is, therefore, key to creating a biomechanical model of tumor growth.

Tumour cell samples are available in collagen scaffolds which are 150μm thick. Mechanical testing of such thin samples is a difficult problem. High sampling micro-indentation using the ElectroPuls™ E3000 was able to extract mechanical properties of these very small samples.

Indentation testing is becoming an increasing field for mechanical characterization of thin film tissues due to its ability to view response over a range of forces, shows a bulk modulus value and localized testing can be obtained.

Testing the Thin Films

The Instron ElectroPuls E3000 was fitted with a 5N load cell. 150μm thick collagen gels were placed into a fluid-filled petri dish container. Initially, a simple waveform was created with a 0.05μms^-1 indentation rate.

Since it was difficult to accurately place the indenter a few μm above the surface of the gel it meant that tests ran for a very long time. A more complex waveform was created that would indent the gel up to 0.5N then raise the indenter 150μm to just above the gel surface. This reduced the indentation times by a factor of 4 while still maintaining 0.05μm indentation rates.

This ultra-slow indentation allowed for precise mapping of the force-indentation profile of the thin gel from which stiffness can be estimated.

The results were processed using a low pass filter to further reduce noise as well as a downsample filter to reduce the file size. An algorithm was used that uses linear modeling analysis to find the start point of the test as well as the point where the plastic beneath became heavily involved

From the load – depth profile the modulus of the collagen gels was taken using the modeling formula:

Formula

Where F is the load applied, E is the elastic modulus, R is the radius of the indenter, δ is the indentation depth and ν is Poisson’s ratio of the material.

The Instron AdvantageTesting thin film tissue samples with the ElectroPuls™ E3000 on the same system that has been used for dynamic 3KN fatigue tests upon demonstrates the flexibility of the instrumentation to the user and the limits of what the machine can do are pushed by the ingenuity of the user. Instron® offers a whole range of load cells that can suit a whole range of different applications.

Since modulus values of materials depend upon the speed of attack, accurate modulus values for biomechanical modeling needs to be obtained from tests at physiological speeds. The high-fidelity control loops for positional accuracy allows the Electropuls™ to perform creep style tests in the order of micrometers per minute which approaches physiological speeds.

UCL Low Force Image

Since the postprocessing aims to give the user the raw data it means that the end user can process that data in whatever way they see fit and there are no integral system biases.

The ability of the calculations module to enhance and vastly improve the capabilities of the machine were demonstrated by the noise reduction technique employed. A simple noise reduction algorithm that takes the mean average of all the data entering the machine and records it is able to show how the system can be tuned to whatever test you want using the calculation module.

UCL Case Study Figure 1

This has allowed information from 10kHz of data to be used while only recording the data at 10Hz such that the file sizes are manageable but the noise levels are very low. Thus this meant that we were able to reduce levels of noise by significant margins simply by adding a small calculation.

Results

The two graphs is a simple but powerful graph showing that a 50N load cell is capable of providing mechanical information from soft tissue 150μm thick collagen gel.

UCL Case Study Figure 2

Final results are able to show that the modulus of collagen in this form is in the range 1800Pa – 2500 Pa (figure 2). These results not only are able to be incorporated into models of growth but they also demonstrate that it is possible to get results from these gels. This means that the further research will be able to characterize collagen embedded cancer cells to get a better model of cancer growth.

These results were validated using an atomic force microscope which is normally used for modulus values of much smaller samples. The results from the AFM concurred with those from the Electropuls™ suggesting that in this kind of testing the Electropuls™ has the capability and sensitivity to test thin biological tissues.

Many thanks to:

Dr. Md Pilar Garcia Souto, Medical Physics and Biomedical Engineering Dept, UCL.
Dr. Peter Wijeratne, Centre for Medical Image Computing, Computer Science Dept, UCL.

ZF Shanghai Case Study

挑战作为车轮和车身之间的承重环节，车桥在车轮和车身之间传递所有的载荷和力矩。这些负载和力矩首先来源于发动机产生的驱动力，其次是来自于道路的不平坦和驾驶动作。 axle test

开发车桥系统的关键目标是：

对有关车辆的底盘进行良好的调校
对外部影响的快速反应
通过系统性减重实现低非簧载质量
通过特别设计的部件实现灵敏、宽容的操控

为有效地试验这些系统，在实际驾驶条件下测得的负载时间历史可以在ZF开发实验室的 Hydropuls ® 轴试验台（也被称为 “SSP”（道路模拟器试验台））以最大精度再现。车桥试验台能够取代道路试验，其优点在于与试验轨道上的试验相比，试验时间可减少约 20%。

ZF 在轴试验台方面有着长期的经验。过去 15 年来，ZF 通过德国迪林根基地积累了大量专业知识，现在正用于中国市场。

axle test 借助上海的新开发中心，ZF 扩大了其在中国和亚洲市场的开发能力，推动自身能够更快速地应对具体的市场挑战。新开发中心的亮点之一是 Hydropuls ®轴试验台。

孙鹏先生自 2013 年以来一直在 ZF 底盘系统业务部工作，不仅是高级试验工程师，也是轴耦合台架的主要用户。

对彭先生及其团队而言，关键在于建立面向未来的系统，能满足未来 15 年的市场要求。彭先生及其团队认为，Instron 台架的一个特点是控制主动减震系统、伺服转向或类似的电子控制底盘模块的综合能力。

面临的主要挑战在于电子接口是否能够良好交互。与这类试验台的其他制造商相比，英斯特朗提供了最适合我们要求的技术。我们认为英斯特朗是一个可靠的合作伙伴，能够提供卓越的质量和出色的服务与维护。
- 孙鹏

Realtest Case Study

The OverviewRealtest GmbH, based in Blaubeuren, Germany, is an independent company offering advanced test and measuring technologies. The company conducts experimental fatigue life verification on components, assemblies and structures according to customer specifications.

Realtest Team Mechanical loads acting on a component are measured under real-life conditions and analyzed. This so-called "raw data" comprises, for example, loads, deformations, and accelerations measured over a defined time range.

Experimental durability testing of components and structures is conducted under uniaxial and multiaxial loading. Displacement-controlled shake test rigs, or, in the case of simple component testing, load-controlled test rigs are used for this purpose.

An essential advantage of a laboratory test consists in the capability of drastically reducing testing times. To achieve this, raw data is analyzed and non-damaging loads are removed from the load spectrum. By additionally adapting the test frequency, a synthetic load spectrum is then generated.

On the test rig, servohydraulic linear or rotary actuators introduce these random, dynamic loads into the component, whereby the damage induced is equivalent to that to which the component is exposed under actual operating conditions.

“The use of these software modules allows us to develop loading sequences for laboratory simulation that come as close as possible to the loads encountered under real-life conditions, achieving highly accurate and reliable results in the shortest possible time. Given the reliability and longevity of the Hydropuls® technology we have outstanding tools available in our laboratory that enable us to meet our customer's unique challenges.”

- Dr. Armin Tobuschat

The Solution Instron Test Machine

Dr. Armin Tobuschat, owner of Realtest, and his team, develop customized concepts tailored to the customer's specific requirements in the area of experimental durability testing, with the objective of obtaining reliable data characterizing the service life of a component. Long-standing experience combined with highly flexible measuring and testing systems enable Realtest to provide high-performance and cost-efficient solutions.To analyze and process the loads acting on components for use in the laboratory test, Dr. Tobuschat and his team rely on Instron´s RS Labsite software suite with fully integrated LMS (Siemens) Tecware modules.

For constant-amplitude loading or block programs, and for testing with single-channel sequences of peaks and troughs, Realtest uses Instron´s RS BasTest software module. To replicate loading histories measured on the test track, Dr. Tobuschat and his team rely on the RS TWR simulation software. Instron's software modules are ideally suited to conducting a variety of single- and multi-axial service load simulation tests.

LOLIWARE

Developing Materials Purposely Designed to DisappearEvery day, billions of single-use plastic products such as straws, cups, and containers are used and discarded. While many of these products can be recycled, the reality is that most of them end up in landfills. This overabundance of discarded plastic is a global issue that has manufacturers racing to develop new, sustainable materials from renewable sources. One of these companies is LOLIWARE, a New York startup that is developing bio-renewable, biodegradable materials derived from seaweed.Who Is LOLIWARE?

Back in 2015, LOLIWARE pitched their idea for a seaweed-derived cup to the Shark Tank investors and walked away with Mark Cuban and Barbara Corcoran as financial backers. They’ve been hard at work ever since, developing new material formulations which require daily testing to track how each change impacts the mechanical properties of their product.

Quote

In addition to researching new materials they have also launched their first product, the LOLIWARE Straw. The LOLIWARE Straw is a sustainable, ocean-safe straw that lasts for hours instead of centuries and is made from 100% food grade materials – which means that if you really wanted to, you could eat it. They call it Hyper-compostable™, which means that it will break down at the same rate as food waste in compost or in a natural environment. In terms of use-life, the straw has been tested to withstand 18+ hours of continuous use and temperatures up to 180°F.

LOLIWARE Straw

Since there are no specific standards discussing how to characterize seaweed-derived plastics, LOLIWARE has had to chart their own path when it comes to testing. Their solution was to use some of the relevant well-established ASTM standards as a framework, and then modify them to suit their application. Their testing includes performing tensile, 3-point bend, compression, and radial tests.

Why Seaweed?

Seaweed as a plastic replacement is a brand new concept based on the idea of leveraging seaweed alginate, which has been used in biotechnology for a long time.

LOLIWARE calls seaweed the “miracle material” because of how quickly it grows without requiring any special care or significant agricultural effort. In fact, some species of seaweed can grow up to 3 meters per day. It also works as a carbon sequester, helping to remove carbon dioxide from the atmosphere.

Seaweed also has sourcing benefits; the ocean is a big place with largely untapped agricultural prospects. In comparison, plant and paper-based replacement materials rely on cultivated crops, which means they must compete in the agricultural space against industries producing food for people and livestock.

Quote

Competing With Plastics

Developing a true replacement material for plastics is challenging on many fronts. For one, a whole manufacturing infrastructure is already scaled for producing single-use products using traditional polymers. LOLIWARE, along with many others in the plastic replacement industry, are developing their products with production in mind and making it as easy as possible for existing manufacturing equipment and processes to be used for their products. The second major challenge is that the final products must stand up to the expectations of the consumers, who are looking for the same level of performance they see when using standard single-use plastic products. To evaluate this, LOLIWARE relies on mechanical testing.

In January of this year (2021) LOLIWARE took the big step of opening their own R&D laboratory, where they are now able to test their new materials on their own 3400 Series universal testing system. Prior to this they worked with a contracted laboratory, which aside from being costly required them to send specimens out in batches and wait for a week or more to receive results. As a startup, being able to move quickly is a priority – so being able to perform their tests in-house is key to speeding up their development process. With the addition of a 3400 they are now able to see results immediately as they work toward developing new formulations. After all, for LOLIWARE the straw is just the beginning – the future is brimming with new applications just waiting to be discovered.

Learn more about LOLIWARE at www.loliware.com.

Siemens combine Machine Learning with ElectroPuls

Siemens combine Machine Learning with ElectroPuls^® Additive Manufacturing (AM), also known as 3D-printing, is the term used to describe a number of manufacturing techniques that allow three dimensional parts to be built up layer by layer, whilst utilizing only the material that makes up the final component. Not only do AM processes allow for the production of highly complex and structurally efficient parts, they also greatly reduce the amount of waste material associated with subtractive techniques, such as machining from a larger material volume.Siemens have taken data from a test campaign carried out on an ElectroPuls E10000 fatigue testing machine, which is optimized for fatigue testing on Additively manufactured products. The data was then used to assess the challenges associated with AM for metal alloys and offer a solution to some of these challenges with Siemens Digital Industries Software.The fatigue performance of a metallic component is determined by the features within the microstructure of the material and the geometry of the specimen. As with any manufacturing technique, the components produced through AM will contain features inherent to the process. These could come in the form of residual stresses, porosities or differences in surface roughness, and their presence will be highly dependent on the local geometry of the component, as depicted in Figure 1. This means the resulting relationship between the component and the microstructural features that will dictate its fatigue performance, becomes highly complex. Mathematical illustration of the interaction between features, as well as each feature’s individual impact is therefore impossible.

Figure 1 Factors affecting the fatigue life of a 3D-Printed structure.

For manufacturers producing performance critical components in Industry, the challenge of investigating fatigue characteristics will typically result in a heavy reliance on prototype testing and highly extensive testing campaigns at huge expense. This is due to a lack of Computer-aided Engineering (CAE) tools that can predict how dynamic performance differs depending on the array and combination of features found locally within each component.

Surface roughness is a clear example of how local features can differentiate in 3D-printed components, due to them being built up by overhanging subsequent layers of material by a small amount, as shown by the differing distributions in Figure 2. Manufacturers must therefore print and test parts in as many orientations as possible, in order to determine which produces the best fatigue characteristics.

Figure 2 Surface roughness as a function of overhang angle for differing build orientations, shown in Simcenter 3D.

It is not feasible to test all build orientations or assess all the combinations of microstructural features – not to mention the effects of loading conditions, residual stresses and post treatments. Nonetheless, for 3D-pirinted parts to be used in safety critical scenarios, these effects must be reliably predicted.

Through the power of machine learning, Siemens Digital Industries Software can assess the fatigue performance on 3D-Printed components that have not been mechanically tested. The methodology has been evaluated on specimens printed by 3D Systems according to a fixed set of processing parameters, for which the fatigue testing and analysis was performed by the Additive Manufacturing team of the Mechanical Engineering Department at KU Leuven , using an Instron^® ElectroPuls E10000.

With its integrated T-slot table, the ElectroPuls E10000 from Instron is optimized for fatigue testing on Additively manufactured products. The all-electric nature paired with user-friendly WaveMatrix^™3 software, as well as features such as patented Stiffness Based Tuning, culminate into a versatile and highly effective system for testing over a small footprint. Simple connectivity to strain gauges, DIC cameras and a host of other sensors, allow users to record detailed strain mapping across the surface of complex test components. As a result, producing a detailed data set for your test campaign has never been so quick and easy.From the real-world SN data acquired by the ElectroPuls system, the software can then predict the SN curve for untested combinations of conditions.

The machine learning uses a Gaussian Process Regression - a method that effectively provides a probability distribution for outcomes based on a minimal set of input data and offers advantages over classical approaches that utilize interpolation or extrapolation.

After incorporating the machine learning into the Siemens Simcenter 3D Specialist Durability software, fatigue properties can be defined by region or element and can include additional treatments to the material. From the CAD model, the system will then identify fatigue affecting features in the component and the Machine Learning Algorithm can then predict local SN curves corresponding to different surface elements in the model. A durability calculation can then quantify the damage accumulation for the entire structure based on its local features using the material properties and mapping of SN curves across the model to produce a simulation as shown schematically in Figure 3.

Figure 3 Depiction of workflow to determine durability computations using machine learning extension for 3D-printed parts.

The results in Figure 4 below, show the predicted fatigue behavior for three different samples. In each instance, the actual test data for each sample (represented by the blue data points), was not inputted into the machine learning algorithm. You can see from the graphs that the algorithm has successfully predicted the behavior of the samples, with all points sitting within the upper and lower bounds of the prediction and close to the predicted SN curve (solid red line). The samples shown include: a build orientated at 90 degrees, with subsequent machining and Hot Isostatic Pressing (HIP) treatment and two builds orientated at 50 degrees, one that received a Full Annealing (FA) treatment and the other HIP treatment.

Figure 4 Results of fatigue life prediction for 3D-printed parts closely matching real experimental data points.

This machine learning approach from Siemens looks to be a successful method for characterizing the fatigue properties of components made through additive manufacturing techniques and offers a solution that does not require specific inference of fatigue affecting factors. The method is also flexible enough to cater for local phenomena in the sample. Finally, for additive manufacturing facilities that likely have a limited number of knowledgeable individuals but a high demand for testing, the capability of the machine learning approach from Siemens, paired with the testing efficiency of the ElectroPuls E10000, results in a huge reduction in time and the number of tests required to achieve a wide variety of results.

If you want to read Siemens’ original article, it can be found here.

Vamp Tech

Challenges and Opportunities in the Global Marketplace

In the contemporary industrial landscape, companies face a number of challenges and opportunities. Among the main challenges is sustainability, as companies must adapt to increasing environmental and social regulations and standards. Increasing regulation is another challenge, as global regulations impose additional restrictions and constraints on business operations. However, these challenges also provide opportunities to grow and innovate for companies that are able to successfully navigate them, allowing them to differentiate themselves in the market and seize new business opportunities.

Vamp Tech: Excellence and Innovation

Vamp Tech, founded in 1988 in Busnago, Italy, is a leader in the flame retardant materials industry. With two plants, one in Italy and one in Malaysia, and 150 highly skilled employees, it excels in the research and production of specialized materials. The company offers a wide range of flame retardant materials, which make up 85% of its production, as well as special technical compounds such as thermally and electrically conductive, self-lubricating and high-temperature materials. Because of their high level of customization, with more than 400 active formulas, Vamp Tech develops tailor-made solutions to meet customers' specific needs, becoming a reliable partner in a variety of industries. As the transition to a more ecological product range, the company is now developing VampGreen and DeniGreen, technical products based on sustainable polymers.

Excellence in Flame Retardant Materials

Vamp Tech's core business, which makes up 85% of its offer, is the production of flame retardant materials. These materials are designed to resist fire and the spreading of flames, ensuring a high level of safety in critical applications. The company also produces special technical compounds, including thermally and electrically conductive, self-lubricating, and high-temperature materials.

What sets Vamp Tech's materials apart is their high degree of customisation. The company has more than 400 active product formulas, which are constantly updated and adapted to market needs. Thanks to a wealth of knowledge and expertise unparalleled in the industry, Vamp Tech is able to solve customers' most complex problems through tailor-made solutions, providing materials that go beyond simple commodities to become truly high-end specialised technical products.

Focus on Quality and Safety

At the core of Vamp Tech's operations are Instron instruments, which play a crucial role in ensuring the quality and safety of its products. Due to the complexity of their materials, they need equipment to monitor flame retardancy. Moreover, everything that is added to the material impacts all its other properties, so the standard tests are carried out alongside other tests, from fluidity testing (which gives an idea of how easily the material can be processed) to mechanical testing (to better understand the structural-type properties the material can withstand) and temperature testing.

Vamp Tech has been using the HDT & Vicat systems since the company was founded. For a company like Vamp Tech, it is critical to accurately determine the maximum temperature that a material can be exposed to without suffering deformation or loss of consistency. This parameter is vital for those producing materials intended for extreme environmental conditions and critical applications. The instrument used is the HV6M, which provides a certain level of automation to minimize manual operations while also ensuring a high level of safety in the working environment, particularly when using oils and lubricants. In addition, the use of Instron tools allows Vamp Tech to conduct in-depth studies on the thermal seal of its materials, especially those containing self-extinguishing additives that also act as plasticizers, leading to lower HDT temperatures. These studies are essential to ensure the maximum reliability and safety of Vamp Tech's products, providing pivotal information for the design and engineering of their products.

An Added Level of Security

The versatility and reliability of Instron instruments were a determining factor in Vamp Tech's choice. Their ease of use, even for less experienced operators, plus their focus on operator and environmental safety, have made Instron instruments a fundamental building block of quality and innovation at Vamp Tech.

Learn more about Vamp Tech at www.vamptech.com.

Wentworth Institute of Technology

Case Study | Academic | Boston, MA

Challenges and Opportunities in the Global Marketplace

Vamp Tech: Excellence and Innovation

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

Learn more about Vamp Tech at www.vamptech.com.

University Research

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Vamp Tech: Excellence and Innovation

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

Learn more about Vamp Tech at www.vamptech.com.

Polyset Company

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Vamp Tech: Excellence and Innovation

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

Learn more about Vamp Tech at www.vamptech.com.

A Comparative Study of Test Result Variability

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Vamp Tech: Excellence and Innovation

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

Learn more about Vamp Tech at www.vamptech.com.

Covalent Expands Testing with AVE3

How Covalent Accelerated Advanced Materials Testing with the AVE3 Advanced Video Extensometer

About

Location

Laboratory Size

Website

Supporting Innovation Across Industries

The Challenge

Versatile, High‑Accuracy Strain Measurement — Without Slowing Down

The Solution

AVE3 Advanced Video Extensometer for Non-Contact Strain Measurement

The Results

Faster Throughput, Expanded Capabilities, and Confident Data

Delivering Value Where It Matters Most

See What Non‑Contact Strain Measurement Can Do for Your Lab

Boston University | KABlab

Boston University | KABlab

About

Location

Website

What is Energy Absorption Efficiency?

How Automation and Machine Learning Helped Them Do It

The Setup

How the Data Being Used

UCL Low Force Testing with ElectroPuls

UCL Low Force Testing with ElectroPuls

ZF Shanghai Case Study

ZF Shanghai Case Study

Realtest Case Study

LOLIWARE | Seaweed Derived Plastics

LOLIWARE

Siemens combine Machine Learning with ElectroPuls

Siemens combine Machine Learning with ElectroPuls

Vamp Tech

Vamp Tech

Challenges and Opportunities in the Global Marketplace

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

Wentworth Institute of Technology

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

University Research

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

Polyset Company

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security

A Comparative Study of Test Result Variability

Wentworth Institute of Technology

Challenges and Opportunities in the Global Marketplace

Excellence in Flame Retardant Materials

Focus on Quality and Safety

An Added Level of Security