International Research Awards on Mechanics of Functional Materials and Structures

Tuesday, November 19, 2024

Mechanical Engineers Face a Changing Future

The soft skills of problem-solving and communication will be needed, while AI, additive manufacturing, and robot/human interaction will grow in importance.

At a Glance
Engineers will have to become better communicators.
Certifications will grow in importance.
Managing data will become paramount.

In order to get a grasp on the skills needed in the coming decade, the American Society of Mechanical Engineers (ASME) and Autodesk conducted a study to identify the skills that mechanical engineers, manufacturing engineers, and CNC machinists will need to do their work.

The report included a literature review, in-depth phone interviews with 30 thought leaders, and a survey of 324 respondents from the US, Canada, and UK. Respondents were chosen from industry based on their involvement in manufacturing physical, discrete, or mechanical products and from academia based on their instruction of mechanical or manufacturing engineering or CNC machining.

The report points to these essential changes for mechanical engineers, manufacturing engineers, and CNC machinists:
Mechanical Engineers

“I think that machine learning and AI will also greatly change the mechanical engineer,” said Timothy Robertson COO of the Institute for Advanced Learning and Research. “I think they will need to have more in-depth understanding of the manufacturing processes because the amount of data that’s going to be available is going to be insane, so they’re going to need to put that into the design process.”

Mechanical engineers will continue to improve upon engineering designs and become more involved in manufacturing implementations and processing production data results to improve designs for manufacturability.

60% of industry believe interdisciplinary engineering knowledge will increase for mechanical engineers over the next five to 10 years. This was consistent across small, medium, and large manufacturers.

Continued emphasis on “soft” skills like problem solving and communication skills to complement their growing focus in software tool functionality, data analytics, programming, and “smart” and sustainable design techniques.
Manufacturing Engineers

“The barriers between engineering and manufacturing are coming down,” said Jeffrey Reed director of engineering at Northrop Grumman Corporation. “I think 10 years from now you are going to see manufacturing engineers and mechanical engineers with equivalent degrees coming out of college.”

The manufacturing engineer position will become even more interdisciplinary as it blends skills with both mechanical engineers and CNC machinists.

Within industry, 72% of respondents believe human-robotic interaction will increase and 74% believe automation will increase for this role.

Like mechanical engineers, future manufacturing engineers will still need enhanced communication skills and will be expected to incorporate additive manufacturing and utilize artificial intelligence /machine learning (AI/ML), digital twin, and data analytics to improve throughput and efficiencies.
CNC Machinists

“CNC machines are going to print all kinds of different materials. There’s going to be an explosion in the different types of materials everybody’s using,” said Pierre Larochelle, professor at South Dakota School of Mines and Technology. “They’re going to have to know how to work with all kinds of funky steel, aluminum, titanium, and lithium.”

CNC machinists' roles will evolve dramatically, from a CNC operator to an engineering technician who programs CNC machines, and over time, they will take on other manufacturing engineering functions.

The factory environments in which future CNC machinists will work will become more complex through the use of cobotics, 3D printers, AI/ML and multi-axis machines, and will require greater mental dexterity (such as programming) and productive collaboration with engineering teams.

According to industry professionals, becoming increasingly fluent in computer-aided design and manufacturing (CAD/CAM) software and programming will enable machinists to increase their use of technologies, including five-axis machines (65%), additive/hybrid manufacturing (66%), and robotics/cobotics interaction (65%).Across all three roles, 90% of survey respondents indicated that teaching deeper design-for-manufacturing knowledge was the most impactful way for academia to develop the future manufacturing workforce.
Additional Findings from the Survey

Communication is paramount. – The research suggests an increased focus on exchanging data between groups of people through cloud collaboration. Digital transformation of roles will shape communication across roles as workflows change. o 86% of total respondents strongly or somewhat agreed that there is a need for a collaborative design process between all three disciplines.

Certifications show specialization. – Academia expects to promote supplementing degrees with certifications. Degrees will likely serve as the foundation, while certifications will showcase specialized skills. 86% of academics embrace less reliance on degrees and welcome more specialized certifications developed in partnership with industry. o 84% of all survey respondents believe employers and academia should partner on new types of certification programs based on employer needs. • Academia embraces emerging tech.

Mechanical engineers need to have applied knowledge throughout the manufacturing process. For mechanical engineers over the next five to 10 years, 79% of industry believe electrical and software engineering will increase and 77% believe system engineering skills will increase.

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Thursday, November 14, 2024

AI and Quantum Mechanics Accelerate Drug Discovery

A recent article published in the Journal of Chemical Information and Modeling researchers at Southern Methodist University (SMU) have developed SmartCADD, an open-source virtual tool designed to speed up drug discovery.

SmartCADD combines artificial intelligence, quantum mechanics, and computer-assisted drug design (CADD) techniques to screen billions of chemical compounds, significantly shortening the time needed for drug development.

In their study, researchers identified promising HIV drug candidates, highlighting the platform's potential for broader applications in drug research. The tool's development was made possible through an interdisciplinary collaboration between SMU’s chemistry and computer science departments.
Related Work

In the past, drug discovery was slowed by challenges such as limited computational power and the manual screening of chemical compounds. Traditional methods also struggled to handle today’s vast chemical databases and predict drug behavior in complex biological systems, leading to longer timelines for identifying promising candidates.
SmartCADD in Drug Discovery

SmartCADD is a virtual tool designed to enhance drug discovery by integrating artificial intelligence (AI), quantum mechanics, and Computer Assisted Drug Design (CADD) techniques. The method starts with SmartCADD's Pipeline Interface, which collects data and runs a series of filters to analyze chemical compounds.

This interface processes vast amounts of information, quickly screening through billions of compounds to identify those that show potential as drug candidates. The AI-driven models allow for rapid, large-scale analysis, addressing the time-consuming nature of traditional drug discovery methods.

The next step involves SmartCADD's Filter Interface, which tells the system how to apply different filters to the chemical compounds. These filters are key to narrowing down the vast number of candidates by assessing various drug-related properties.

For instance, the filters predict how each compound will behave in the human body and evaluate the structural compatibility between the drug and target proteins. It helps to significantly streamline the drug testing process, ensuring only the most promising compounds advance to the next stages of analysis.

SmartCADD combines 2D and 3D modeling techniques to visualize the drug molecules and understand their interaction with biological targets. These models provide a detailed understanding of the chemical structure, helping researchers optimize the fit between potential drug molecules and the proteins they aim to interact with.

Additionally, SmartCADD uses explainable AI, which means that the AI's decision-making process is transparent. This helps researchers understand why certain compounds are considered promising and how the predictions were made.

In a recent study, researchers applied SmartCADD to HIV drug discovery by analyzing data from the MoleculeNet library. By screening 800 million compounds, SmartCADD identified 10 million potential candidates, further refined using filters that focused on the properties of approved HIV drugs.

While the study focused on HIV, the researchers emphasized that SmartCADD can be adapted for various other drug discovery projects, making it a versatile and efficient tool for advancing drug research across multiple fields.
Innovative Drug Screening

The researchers showcased SmartCADD's effectiveness by applying it to HIV drug discovery in three case studies, targeting specific HIV proteins. Using data from the MoleculeNet library, which contains 800 million chemical compounds, SmartCADD quickly screened and identified 10 million potential drug candidates. The platform then refined these results by comparing them to existing HIV drugs, advancing the most promising candidates for further analysis.

SmartCADD’s AI-driven models also provided insights into how these compounds behave in biological systems, predicting their pharmacokinetics and pharmacodynamics—key factors for understanding drug interactions with the human body. This streamlined approach not only accelerated the identification of viable drug candidates but also demonstrated SmartCADD's adaptability for other therapeutic targets beyond HIV.

The success of SmartCADD highlights its potential to revolutionize drug discovery across multiple fields, including antibiotics and cancer therapies. It offers a promising tool for tackling urgent global health challenges.

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Sunday, November 10, 2024

AI training method can drastically shorten time for calculations in quantum mechanics

The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising AI for their respective fields of study. KAIST researchers have now succeeded in dramatically shortening the calculation time of highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel approach to teach AI.

Professor Yong-Hoon Kim's team from the School of Electrical Engineering has developed a 3D computer vision artificial neural network-based calculation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations performed using supercomputers to derive the properties of materials.

The density functional theory (DFT) calculations in quantum mechanics using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow for fast and accurate prediction of quantum properties.

However, in current density functional theory (DFT) calculations, a complex self-consistent field (SCF) process of generating three-dimensional electron densities and solving quantum mechanical equations must be repeated tens to hundreds of times, which limits its application to hundreds or thousands of atoms.

Professor Yong-Hoon Kim's research team asked whether it would be possible to avoid the self-consistent field process using the artificial intelligence technique that has recently been rapidly developing. As a result, they developed the DeepSCF model to accelerate calculations by learning chemical bond information distributed in three-dimensional space through a neural network algorithm in the field of computer vision.

The research team focused on the fact that according to density functional theory, electron density contains all the quantum mechanical information of electrons, and in addition, the residual electron density, which is the difference between the total electron density and the sum of the electron densities of the constituent atoms, contains chemical bond information, and selected it as a target for machine learning.

Afterwards, the team adopted a data set of organic molecules containing various chemical bond characteristics, and the atomic structures of the molecules included in it were subjected to arbitrary rotations and deformations to further improve the accuracy and generalization performance of the model. Finally, the research team demonstrated the validity and efficiency of the DeepSCF methodology for complex and large systems.

Professor Yong-Hoon Kim, who led this research, said, "We have found a way to correspond quantum mechanical chemical bonding information distributed in three-dimensional space to an artificial neural network. Since quantum mechanical electronic structure calculations are the basis for all-scale material property simulations, we have established the overall basic principles for accelerating material calculations through artificial intelligence."

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Thursday, November 7, 2024

Opinion | Hinduism and Quantum Physics: How Vedas Inspired Western Scientists

The great thinkers of ancient Bharat spoke of how energy and matter at atomic levels could influence one another from long distances. This is now called Quantum Entanglement

Hinduism is the only spiritual tradition which accurately estimates the age of the universe as per modern science. Sanatana Dharma is also an intellectual and multi-cultural tradition attached to sophisticated schools of philosophy, its own system of medicine and psychology, architecture, astronomy, cosmology, metaphysics, metrology, mathematics, and various art forms. Sacred geometry, trigonometry, calculus, the concept of zero and negative numerals, the decimal system, pi, sine and cosine, the idea of multiple big bangs and an inter-dimensional multiverse, can all be traced back to the great minds of Vedic sages.

The very notion of this relationship was unknown and laughable to many in the scientific communities of the West. It was even unknown and laughable to many Indian scientists during and after the colonial rule. It wasn’t until 1989 and 1993, when author Deepak Chopra’s books, ‘Quantum Healing’ and ‘Ageless Body, Timeless Mind’ became popular that the idea that any religion could have much of anything to do with the field of modern science was at all acceptable in any level of academia.

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Tuesday, November 5, 2024

A new model offers robots precise pick-and-place solutions

SimPLE learns to pick, regrasp, and place objects using the objects’ computer-aided design model.

Pick-and-place machines are a type of automated equipment used to place objects into structured, organized locations. These machines are used for a variety of applications — from electronics assembly to packaging, bin picking, and even inspection — but many current pick-and-place solutions are limited. Current solutions lack “precise generalization,” or the ability to solve many tasks without compromising on accuracy.

“In industry, you often see that [manufacturers] end up with very tailored solutions to the particular problem that they have, so a lot of engineering and not so much flexibility in terms of the solution,” Maria Bauza Villalonga PhD ’22, a senior research scientist at Google DeepMind where she works on robotics and robotic manipulation. “SimPLE solves this problem and provides a solution to pick-and-place that is flexible and still provides the needed precision.”

A new paper by MechE researchers published in the journal Science Robotics explores pick-and-place solutions with more precision. In precise pick-and-place, also known as kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement. The approach, dubbed SimPLE (Simulation to Pick Localize and placE), learns to pick, regrasp and place objects using the object’s computer-aided design (CAD) model, and all without any prior experience or encounters with the specific objects.

“The promise of SimPLE is that we can solve many different tasks with the same hardware and software using simulation to learn models that adapt to each specific task,” says Alberto Rodriguez, an MIT visiting scientist who is a former member of the MechE faculty and now associate director of manipulation research for Boston Dynamics. SimPLE was developed by members of the Manipulation and Mechanisms Lab at MIT (MCube) under Rodriguez’ direction.

“In this work we show that it is possible to achieve the levels of positional accuracy that are required for many industrial pick and place tasks without any other specialization,” Rodriguez says.

Using a dual-arm robot equipped with visuotactile sensing, the SimPLE solution employs three main components: task-aware grasping, perception by sight and touch (visuotactile perception), and regrasp planning. Real observations are matched against a set of simulated observations through supervised learning so that a distribution of likely object poses can be estimated, and placement accomplished.

In experiments, SimPLE successfully demonstrated the ability to pick-and-place diverse objects spanning a wide range of shapes, achieving successful placements over 90 percent of the time for 6 objects, and over 80 percent of the time for 11 objects.

“There’s an intuitive understanding in the robotics community that vision and touch are both useful, but [until now] there haven’t been many systematic demonstrations of how it can be useful for complex robotics tasks,” says mechanical engineering doctoral student Antonia Delores Bronars SM ’22. Bronars, who is now working with Pulkit Agrawal, assistant professor in the department of Electrical Engineering and Computer Science (EECS), is continuing her PhD work investigating the incorporation of tactile capabilities into robotic systems.

“Most work on grasping ignores the downstream tasks,” says Matt Mason, chief scientist at Berkshire Grey and professor emeritus at Carnegie Mellon University who was not involved in the work. “This paper goes beyond the desire to mimic humans, and shows from a strictly functional viewpoint the utility of combining tactile sensing, vision, with two hands.”

Ken Goldberg, the William S. Floyd Jr. Distinguished Chair in Engineering at the University of California at Berkeley, who was also not involved in the study, says the robot manipulation methodology described in the paper offers a valuable alternative to the trend toward AI and machine learning methods.

“The authors combine well-founded geometric algorithms that can reliably achieve high-precision for a specific set of object shapes and demonstrate that this combination can significantly improve performance over AI methods,” says Goldberg, who is also co-founder and chief scientist for Ambi Robotics and Jacobi Robotics. “This can be immediately useful in industry and is an excellent example of what I call 'good old fashioned engineering' (GOFE).”

Bauza and Bronars say this work was informed by several generations of collaboration.

“In order to really demonstrate how vision and touch can be useful together, it’s necessary to build a full robotic system, which is something that’s very difficult to do as one person over a short horizon of time,” says Bronars. “Collaboration, with each other and with Nikhil [Chavan-Dafle PhD ‘20] and Yifan [Hou PhD ’21 CMU], and across many generations and labs really allowed us to build an end-to-end system.”

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Friday, October 18, 2024

URI mechanical engineering professor awarded NSF, DARPA early career honors

Yang Lin earns early career honors for biomedical engineering, environmental monitoring research

KINGSTON, R.I. – Oct. 8, 2024 –University of Rhode Island mechanical engineering assistant professor Yang Lin has been awarded a U.S. National Science Foundation Early Researcher Initiation and a Defense Advanced Research Projects Agency Young Faculty Award.

Both DARPA and ERI projects focus on distinct applications in biomedical engineering and environmental monitoring, respectively, and will utilize acoustofluidic technology, but the applications and approaches will be different.

The NSF program grant supports new investigators at non-R1 institutions as they initiate their engineering research programs and advance in their careers as researchers, educators, and innovators.

“Being awarded the NSF ERI is a tremendous honor and a significant opportunity to advance our research on microplastic pollution,” said Lin.

The project that secured the NSF award – a grant of $200,000 – focuses on developing a novel microfluidic-based system for monitoring microplastics in seawater. Traditional methods of sampling and separating microplastics are often time-consuming and ineffective, especially for the smallest particles. Lin’s approach leverages the unique capabilities of acoustofluidics to sort and concentrate microplastics by adjusting the frequency and layout of surface acoustic waves. Acoustofluidic technologies utilize acoustic waves to manipulate fluids and particles within fluids, in this case microplastics, all in a contact-free and biocompatible manner.

Additionally, he and his team are integrating machine learning with Raman spectroscopy to accurately identify these microplastics, even those that have degraded over time. The goal is to create a label-free, reliable method for monitoring microplastics as small as one millionth of a meter in seawater, which could have a profound impact on how to address plastic pollution in marine ecosystems and has the potential to set a new standard in microplastic monitoring.

“These tiny microplastics are the most prevalent in our oceans, yet they are incredibly difficult to isolate and identify using traditional methods,” said Lin. “This award not only validates our research approach but also provides the resources necessary to develop innovative solutions to this global environmental issue.”

The Young Faculty Award provides funding, mentorship, and access to Department of Defense and industry contacts to support early-career researchers in developing innovative ideas to address critical national security challenges. The long-term vision of the program is to cultivate a new generation of academic leaders in science, engineering, and mathematics who will make lasting contributions to the Defense Department and national security issues.

Lin is the first faculty member at URI to receive the prestigious award, marking a significant achievement for his career. Lin will receive approximately $800,000 under this award, spanning a two-year base period with the option of extending it to a third year.

“Receiving the Young Faculty Award is an incredible honor and a significant milestone in my career. This award not only recognizes the innovative potential of our research, but also provides crucial support to explore and develop groundbreaking technologies,” said Lin.

The proposal is centered on developing a next generation microphysiologic system called the Precision Lattice Acoustofluidic Tissue Ecosystem, or PLATE. Traditional 2D cell cultures and even many 3D models often fail to replicate the intricate architecture and dynamic processes of living tissues, particularly when it comes to nutrient distribution, waste removal, and controlled delivery of growth factors.

The PLATE system addresses these limitations by integrating advanced digital acoustofluidics, acoustic bubble technology, and nanoscale 3D printing using two-photon polymerization to develop organoids engineered to replicate the functionality and complexity of real tissues, allowing him to investigate dynamic physiological processes in a controlled environment. Lin will construct these organoids with precise spatial control, starting with breast cancer tumoroids and expanding to other organoid types such as intestinal and brain organoids.

The primary objectives of the PLATE system are to enhance vascularization, optimize nutrient exchange, and enable precise spatial control of biochemical cues, which could redefine the future of tissue engineering. Lin will attend the award ceremony at DARPA headquarters in Arlington, Virginia, and present his work along with the other awardees.

“I’m excited about the future of this research and grateful for the opportunity to make contributions to advance in these fields of research,” said Lin.

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Thursday, October 17, 2024

Best laptops for engineering students in 2024

The best laptops for engineering students easily handle complex CAD projects and datasets. If you're getting ready for the Back to School season, these are the most powerful and lightweight laptops we recommend for any engineering course.

Our expert team of reviewers have tested of the best student laptops - and for engineering, we like the Dell XPS 15. With some great specs inside a light, compact chassis, it's ideal for taking to class. For alternatives, check out the budget HP Victus 15 and the always impressive m2-powered Apple Mac Air.

In reviewing the best laptops for engineering students, we've compared specs, benchmarked performance. We also explored core features such as ports, connectivity, and a comfortable keyboard for essay-writing, to help you find the right laptop, whichever branch of engineering you study.Best laptop for AutoCAD deals: We tracked down the best AutoCAD laptop deals around right now

"Laptop perfection" is how we described Dell's excellent XPS 15. Maintaining its outstanding classic design from previous generations, the 2022 model is a solidly built device. It's made with a brushed CNC aluminum chassis and carbon fiber palm rests that, alongside the ever-comfortable Dell keyboard design, we found comfortable for prolonged use. If you're writing a lot of essays, you'll feel the benefit of this design very quickly. We were also impressed with the extended battery life, letting you work across campus without worrying about losing charge too fast.

However, limiting portability (without the use of a laptop docking station at least) is the lack of available ports. You'll find three USB Type-C ports, which won't be ideal if you're using USB-A peripherals.

It's a small complaint, perhaps, in an otherwise great laptop. Paired with a beautiful screen, a great keyboard, and plenty of storage space, you'll blaze through your latest project. Better still, this is a Windows laptop - and many engineering programs, particularly Autodesk's line-up, only run on Windows.

It might be marketed as a gaming laptop. Still, the HP Victus 15's simple design (free from the gaudy 'gamer aesthetic' of many powerful laptops) and RTX-equipped models make it a solid choice for any engineering student who will be running graphically-demanding programs.

It's also remarkably cheap for a laptop with a discrete GPU, likely to please any budding engineer on a budget. During testing, it performed below other gaming laptops in our standard benchmarks, but not by a huge margin. We also found that it takes air circulation seriously, boasting a dual-fan setup and a large air vent at the bottom. That's going to be a major benefit when working in those resource-intensive apps and large, complex projects that really test the mettle of any engineering laptop.

A bright 15.6-inch FHD display and generally robust build quality mean that while this isn't the most lightweight laptop in the business, it's tough enough to withstand the bumps and bruises of college life. If you're looking to save space in your dorm, this machine easily pulls double-duty as a laptop and a desktop replacement. The Apple MacBook Air (M2, 2022) isn't just one of the best laptops Apple has ever made. It's one of the best laptops for engineering students who value portability and power. However, it's worth checking course requirements here, as depending on your discipline, many engineering classes require a Windows laptop for software compatibility.

But if it's right for you, then you'll find this machine will absolutely breeze through just about any project, thanks to its incredibly powerful M2 processor. During our time with the MacBook Air, the laptop performed incredibly well, blazing through tasks like video editing and graphic design without a single drag on performance. So, it's more than capable of handle compatible engineering apps. Better still, for those working in libraries and other busy, communal areas, we found the fan was silent under normal operating conditions (although we would expect the volume to amp up when running heavier workloads).

The laptop is thin, light, stylish, durable with an incredible battery life that lasts over 11 hours on a single charge. In other words, perfect for carrying around campus. Its screen is gorgeous, and the new-and-improved keyboard means you can effortlessly produce coursework for hours.

The Asus ROG Strix SCAR G834JY boasts an incredible array of specs that promise to make this machine rocket to the top of wishlists for engineering students looking for a laptop powerful for work and play (because college isn't all about studying, right?).

While built with gamers in mind, the G834JY is built to effortlessly handle processor and GPU-intensive workloads on engineering courses. Specs are excellent - 13th Gen Intel Core i9-13980HX processor and an NVIDIA GeForce RTX 4090 Laptop GPU. RAM tops at 64 GB of DDR5-4800 MHz memory and comes equipped with 2 TB of PCIe 4x4 configured in RAID 0. So, this machine should last you throughout your course - it's not going to age-out or need upgrading.

The screen is great, too - 18-inch QHD+ 16:10, with more screen real estate to keep total oversight over your work. Especially good for designers, the display boasts 100% DCI-P3 color gamut and Pantone Validation.

On the downside, it's not ultra-portable. It's broad and heavy and you won't want to lug this laptop around college ground a lot. Cooling isn't great here. Nor did we find battery life especially good. The Asus ROG Strix SCAR G834JY is ideal, however, for setting up in one or two locations each day. Connectivity and port selection aren't great either, the only highlight being a single Thunderbolt 4 port, so you'll need a docking station if you have a lot of peripherals.

With its top-end specs and those minor shortcomings on a very high-performance laptop, this one gives us desktop alternative vibes.

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nternational Research Awards on Mechanics of Functional Materials and Structures

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