Experimental Techniques in Solid Mechanics
- Sumit Basu
- Jan 3
- 9 min read
Updated: Jan 5
We gathered five leading experts to discuss the challenges faced by experimental Solid Mechanics and brainstorm ideas to overcome them. Profs Nagamani Jaya Balila from Metallurgical Engineering and Materials Science, IITB, Nilesh Gurao from Materials Science and Engineering IITK, Ratna Kumar Annabattula from Mechanical Engineering IITM, P Venkitanarayanan from Mechanical Engineering IITK and Sankara Subramanian from IndicVision joined the discussion which was moderated by Prof Ushasi Roy, Mechanical Engineering IITK.
Highlights. The needs of the hour:
Precise measurement techniques for stress, strain and deformation in ultra-soft materials
Accessible and easy-to-use mobile compatible image correlation techniques.
Deep learning techniques incorporated into image correlation.
Reliable strain measurements inside electron microscopes.
Penetrating deeper into materials using high energy beams.
Encourage students to build new, specialised equipment and use available ones imaginatively.
In the 1800s, as engineering transitioned from craftsmanship to science, engineers recognised the need for materials with consistent properties not influenced by the source of procurement. In 1880, a Norwegian-born engineer, Tinius Olsen, who had recently moved to the USA, developed what was likely the first version of a Universal Testing Machine (UTM). Called the “Little Giant”, it allowed both tensile and compressive testing using one machine. The practice of rigorous materials testing and standardisation of testing protocols became an essential part of engineering practice in the 20th century.
Today, the aim of materials testing is not merely to determine mechanical properties. Nor is UTM the only tool in the engineer’s arsenal. In step with advancements in materials simulations, the experimental mechanics of solids now encompasses an imposing array of tools, including electron microscopy, tomography, X-ray diffraction, and a host of specialised loading arrangements. Even the early UTM has undergone a significant change and can now do much more than merely push and pull on a metallic sample. What began as a means of characterising metals has now evolved into an essential tool for understanding how materials deform and fail. With advanced tools, engineers can now map the sequence of events that lead to the failure of a material at ever-decreasing scales in length and time.
New materials and challenges
The Little Giant was meant for testing metals, which meant high fidelity at high loads. With the advent of soft robotics, increasing interest in the mechanical properties of biological materials, and non-contact stimuli-driven deformation of ultra-soft solids, the focus has shifted significantly. Researchers now demand high fidelity at extremely low loads. This trend is expected to intensify with the development of technological applications that utilise soft materials rather than stiff ones. Three-dimensional soft materials of the near future will most likely be printed on custom-designed printers using custom-designed inks of stimuli-sensitive materials. The printed structures will morph under light, at the optimal pH level of their surroundings, or in response to electromagnetic fields, and perform functions that stiffer materials cannot achieve. Coupled with tailored microstructures, these stimuli-responsive materials will be used to design exotic actuators that mimic nature. However, successful designs will also require robust material characterisation, carried out without the luxury of linearity, as time-dependent large deformations occur despite extremely small loads.
The success of niche technologies in stimuli-responsive soft matter depends on advances in experimental mechanics, which enable precise and accurate measurement/estimation of strains, stresses and deformations in these complex systems. While much of the soft matter research remains largely at the lab-scale demonstration phase, the field’s progress is hindered by a lack of robust engineering approaches to reliably estimate their durability and predict long-term performance under dynamic stimuli. The traditional design principles may not be suitable for these systems due to the materials being nonlinear, heterogeneous, and stimulus-dependent. It should be emphasised that a systematic mechanical testing and validation framework that can generate data for fatigue life, damage accumulation etc, is essential to bridge the gap between lab-scale proof of concepts to scalable, durable and mass deployable soft devices and actuators.
However, all experimental challenges do not lie in the small and the soft. Modern large-scale engineering structures, such as wind turbine blades, which can be over a tenth of a kilometre long, pose formidable challenges for the experimenter. How do we characterise the interaction of the blade with the air around it as it rotates? In fact, most fluid-structure interaction problems present unresolved issues of measurement, mainly related to the way forces are transferred from the liquid to the solid and how fluid flow is affected by the motion. Experimental techniques in both solid and fluid mechanics must be applied in tandem to extract meaningful answers.
Seeing strains and displacements
Solid materials are generally opaque, and experimental methods can only look at the surface. The change in distance between two points on the surface due to deformation can provide a local measure of deformation and strain. This was the idea behind extensometers and even strain gauges. However, Digital Image Correlation (DIC) methods have made full-field strain measurements possible using a camera, a suitable source of light, and a sufficiently random and well-defined pattern of speckle dots painted on the surface. Displacements and strains on a flat plane can be measured using a single camera. We can use two cameras to measure both in-plane and out-of-plane displacements.
The core of the DIC process is the software that identifies the positions of the speckle dots before and after deformation to calculate displacements through an image correlation algorithm. The strains are then computed by numerically differentiating the displacements over the region where the pattern exists. Strains will indeed be noisier than the displacements, but DIC software is continually improving. Today, we employ various numerical techniques to reduce noise, and massively parallelised codes enable us to perform DIC measurements over vast areas.
The possibilities of the image correlation technique, however, have not yet been exhausted. Since smartphones are now ubiquitous, DIC applications that can run on Android phones and provide near-accurate strain and displacement measurements quickly and easily should be relatively easy to develop. School-level fun experiments with these applications will require some initial training, but the basic principles of DIC are easy to appreciate.
Existing DIC applications have not leveraged deep learning techniques. Feature-matching algorithms, borrowed directly from engineers working on computer vision, can be helpful. Photometric stereo can be coupled with DIC to measure surface roughness, for example, to continuously monitor the condition of road surfaces.
Digital Image Correlation, a technique that correlates two digital images to determine the difference between them, can also be applied to non-optical images. For researchers interested in understanding the micromechanics of deformation and fracture, images generally mean micrographs obtained from an electron microscope. These images are produced inside the vaccum chamber of the microscope, using a thin electron beam that rasters over a tiny region of the sample surface under the action of a time-varying electric field. The image produced from this process basically records the count of electrons that bounce back from the surface into a suitably placed detector. The image produced recreates the surface on the computer and provides a host of other information about it. It is now possible to build small-sized UTMs that can fit into the vaccum chamber, deform the specimen while we keep imaging it with electron beams. These in-situ tests provide an unprecedented amount of information on the microstructural changes that accompany material deformation.
Unfortunately, images produced by the rastering electron beam sometimes lead to artefacts that are absent in optical images. These arise from the fact that the beam is often not thin enough and does not always raster perfectly along the paths that the electric field asks it to. Therefore, the images from in-situ tests may have to be cleaned up using sophisticated post-processing techniques. Designing these techniques requires an understanding of how these images are produced and the statistical implications of systematic and random deviations on signal errors emerging from the detectors.
In-situ tests also require miniaturised experimental set-ups. Whenever such set-ups are used, the obvious question that must be asked at the very onset is whether deviations from `carved in stone’ macroscopic standards will completely pollute the test outcome. Are quantities like yield strength, fracture toughness calculated from miniature specimens comparable to those from large scale tests? Or are they very different due to what has come to be known as the `effect of the length scale’? These questions are often not easy to answer. Computer models and sophisticated simulations help in answering them, but a critical evaluation requires in-depth investigations for every case.
Take the case of a `hole expansion test’, which is a standard test carried out on thin metallic sheets to assess how much the edge of the sheet can be stretched before cracks appear. In the miniaturised version, a micron-sized hole is punched into the sheet, which is then clamped, and a tiny punch is pushed into the hole to expand it until cracks appear at the edges. The reward for doing this is the knowledge of strains and micromechanical adjustments taking place incredibly close to the edge, giving a glimpse of how mechanics and microstructure connive to initiate a crack.
Some tests are more difficult. For example, a bulge test, where a thin film is pressurised from below to produce a circular bulge, uses the height of the bulge to gain information about the properties of the sheet. While this test can be miniaturised, measuring the out-of-plane displacement of the bulge inside the microscope proves to be a challenge.
Image correlation techniques are also expected to assume importance in situations where a large number of stress-strain responses for a material need to be generated under conditions of varying strain rates and temperature -- for instance, when you are generating data to train a machine learning algorithm. Instead of using standard samples to generate one data point at a time, it is possible to design samples that, utilising the power of 3-d DIC, can yield several stress-strain curves from a single test. Designing such samples that have a wide variation of physical conditions, like strain rate or temperature across their geometry, is challenging, but if you can design one, extracting the relevant data is not.
Beyond the surface
Can we overcome the limitation of measuring surface strains only and look inside an optically opaque solid while it is deforming? To some extent, this seemingly impossible feat is beginning to look possible, at least for tiny metallic samples.
At the heart of this technique is X-ray diffraction (XRD), a method that has been employed for over a century to determine the atomic arrangement within a material. When a X-ray hits a sample, it is scattered by the atoms in the sample according to Bragg’s law. A 2D detector positioned behind the sample can capture a snapshot of the scattered X-rays and convert them into an intensity pattern. For a pure single crystal the pattern is a set of dots, each indicating an atomic plane in what is known as the reciprocal space. For a polycrystalline material with grains that have different orientations of the same crystal structure, the pattern consists of thick rings. If the sample is deformed while being bombarded with X-rays, the rings change in size, shape, and width. Each of these changes carries signatures of strains produced by the deformation. In fact, these changes reveal more than just elastic strains. They contain information about the rotation of lattices, gradients of strain, and even slip activity deep inside the material.
With multiple 2-d images obtained under load, it is possible to link lattice strains to macroscopic strains and reconstruct the complete strain field. Using high-intensity and collimated X-ray sources as in synchrotron diffraction studies, we can penetrate deeper into the material and compute strain fields over larger volumes.
Democratising experiments
Experimental solid mechanics is a thriving field of research, vigorously learning from and utilising developments in many allied areas of science and technology. Experiments are necessary because they are, in some ways, the ultimate benchmark against reality for all theories and computations. Yet, students hesitate to take up experimental problems for their research. Developing experimental techniques and building new set-ups are considered too daunting and often, too risky if you want to complete your PhD on time.
The reasons for the hesitation may be hidden in the way experiments are conducted during the training of an engineering student. It is seldom recognised that experiments can be excellent supplements to dense theories. Experiments need to accompany learning at every step. Additionally, experimental facilities are often treated as fragile and sensitive tools that are unforgiving to untrained hands, particularly when attempting tasks beyond the most routine. Experiments are then never about exploration. Nor are they about extending the capability of the tool, let alone developing new ones. To make experimental research careers popular again, the costs of failure need to be significantly reduced.
Further reads
J.M. McCracken, B.R. Donovan and T.J. White, Materials as Machines, Advanced Materials, 32, 1906564 (2020)
K. Mehta, A.R. Peeketi, L. Liu, D. Broer, P. R. Onck and R. K. Annabattula, Design and applications of light-responsive liquid crystal polymer thin films, Applied Physics Reviews, 7, 041306 (2020)
S. Boukhtache, K. Abdelouahab, F. Berry, B. Blaysat, M. Grédiac, F. Sur, When Deep Learning Meets Digital Image Correlation, Optics and Lasers in Engineering, Volume 136, 2021, 106308, https://doi.org/10.1016/j.optlaseng.2020.106308.
Guilherme Potje, Felipe Cadar1, Andre Araujo, Renato Martins, Erickson R. Nascimento, XFeat: Accelerated Features for Lightweight Image Matching, https://openaccess.thecvf.com/content/CVPR2024/papers/Potje_XFeat_Accelerated_Features_for_Lightweight_Image_Matching_CVPR_2024_paper.pdf
Soudip Basu, Balila Nagamani Jaya, Sarbari Ganguly, Monojit Dutta, Indradev Samajdar; Novel miniature in situ hole expansion test coupled with microscopic digital image correlation. Rev. Sci. Instrum. 1 October 2023; 94 (10): 105104. https://doi.org/10.1063/5.0159098
Soudip Basu, Balila Nagamani Jaya, Rohit Kumar Yadav, Sarbari Ganguly, Monojit Dutta,
Size and microstructural factors affecting the micro-hole expansion ratio and fracture toughness of dual phase steel sheets,Materials Science and Engineering: A,Volume 919,2025,147517, https://doi.org/10.1016/j.msea.2024.147517.
Soudip Basu, Balila Nagamani Jaya, Harita Seekala, P. Sudharshan Phani, Anirban Patra, Sarbari Ganguly, Monojit Dutta, Indradev Samajdar, Correlative characterization and plasticity modeling of microscopic strain localizations in a dual phase steel, Materials Characterization,Volume 197,2023,
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