International Research Awards on Computer Aided Design in Mechanical Engineering

Engineering by Cuts: How Kirigami Principle Enables Unique Mechanical Properties and Functionalities

 Abstract:

Kirigami, the ancient art of paper cutting, has evolved into a design and fabrication framework to engineer multi-functional materials and structures at vastly different scales. By slit cutting with carefully designed geometries, desirable mechanical behaviors—such as accurate shape morphing, tunable auxetics, super-stretchability, buckling, and multistability—can be imparted to otherwise inflexible sheet materials. In addition, the kirigami sheet provides a versatile platform for embedding different electronic and responsive components, opening up avenues for building the next generations of metamaterials, sensors, and soft robotics. These promising potentials of kirigami-based engineering have inspired vigorous research activities over the past few years, generating many academic publications. Therefore, this review aims to provide insights into the recent advance in this vibrant field. In particular, this paper offers the first comprehensive survey of unique mechanical properties induced by kirigami cutting, their underlying physical principles, and their corresponding applications. The synergies between design methodologies, mechanics modeling, advanced fabrication, and material science will continue to mature this promising discipline.

                                                       

 Introduction:

Kirigami—“cut-paper” in Japanese—is an ancient art of creating beautiful decorations by simply cutting and manipulating a thin piece of paper. The seemingly infinite possibilities of developing 2D or 3D geometries by the kirigami principle have inspired countless implementations in our modern life, from children's pop-up books to art and architecture. Meanwhile, kirigami received a growing interest from the science and engineering communities, who are transforming this humble artistic activity into a framework for architecting, fabricating, and functionalizing a wide variety of engineered systems like flexible electronics, metamaterials, morphing structures, and soft robotics. The rapidly increasing number of kirigami-related academic publications over the recent years is a testimony to this exciting development.

In our traditional perception, kirigami is frequently related to origami (aka “fold-paper”) due to their apparent similarities. Indeed, one could consider kirigami a variation of origami by allowing cutting in addition to folding. However, the introduction of cuts makes the working principle underpinning kirigami fundamentally different from origami. For example, a periodic and tessellated origami folding pattern turns paper into a kinematically over-constrained system, reducing the overall degree of freedom. Furthermore, if their facets are stiff, origami could possess only one kinematic degree of freedom, commonly referred to as “rigid folding.” Miura-ori, which is the foundation of many deployable structures and metamaterials,[1-3] is a classic example of 1DOF rigid-foldable origami. On the contrary, kirigami cutting introduces the opposite effect via releasing the continuous constraint in the constituent sheet material and significantly increasing the kinematics degree of freedom. As a result, the kirigami principle is powerful for imparting compliance to inextensible sheet materials, while origami typically creates load-bearing and space-filling 3D topologies.

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Electronic mechanical braking system executive mechanism design, calculation, and modeling based on dynamic control

 Introduction: As science and technology develop, automobiles are moving toward intelligence and electrification and need better braking systems.

Methods: To improve the braking system’s response speed and braking effect, a longitudinal dynamics control system for automobiles based on the electronic mechanical braking system was proposed, and the electronic mechanical braking system was improved through automatic disturbance rejection control.

Results: The experimental results show that the time required for achieving the target clamping force in the electronic mechanical braking system using self-disturbance rejection control and proportional integral differential control is only 0.01 s, but there is an issue of excessive control in the proportional integral differential system between 0.12 s and 0.2 s, while the self-disturbance rejection controller does not have this problem. Meanwhile, regardless of the interference applied, the electronic mechanical braking system with automatic disturbance rejection control can ensure that the clamping force does not fluctuate. In the joint simulation experiment, the expected acceleration and actual acceleration can remain consistent, and if the expected braking force is 9000 N, then the actual braking force of the electronic mechanical brake (EMB) is also 9000 N.

Discussion: The above results indicate that the vehicle longitudinal dynamics control system using the electronic mechanical braking system not only responds fast but also has a good braking effect, avoiding the problem of excessive control and improving the driving experience.

 Introduction

           Over more than 100 years since the official birth of the first car in 1886, related automobile technologies have been fully developed and matured. With the breakthroughs in artificial intelligence, new energy, and other technologies, cars have gradually developed towards intelligence and electrification. In this trend, the braking system of automobiles needs to meet the requirements of energy recovery and active braking for the safety and energy-saving of electric vehicles. However, although the commonly used hydraulic braking system has a relatively sensitive response and good follow-up, it is laborious to operate, provides limited braking torque, and does not satisfy the new requirements (Huang et al., 2019; Jing and He, 2019). In addition, the traditional hydraulic brake control method has not been able to eliminate the vibration phenomenon that often occurs in the vehicle under braking conditions, which has an adverse effect on the control accuracy of the system. The wire-controlled braking system abandons all or part of the traditional hydraulic pipelines with fast response speed and high control accuracy, becoming a new research direction for braking systems. The wire-controlled brake system includes electronic hydraulic brake (EHB) and electronic mechanical brake (EMB) systems. EMBs eliminate all hydraulic components, thereby reducing response delay and improving the accuracy of brake pressure control. At the same time, the EMB system can also achieve energy recovery (Wu et al., 2019; Weng et al., 2021). However, in the EMB system, traditional proportional integral differential (PID) controllers are prone to external interference, resulting in reduced braking control effectiveness. Therefore, in order to improve the anti-interference ability of EMBs and compensate for the brake pressure change caused by the system vibration, the active interference inhibition control (ADRC) is introduced in the EMB system and applied to the longitudinal dynamics control of the vehicle to eliminate the vibration problem causing the braking torque change (BTV) and realize accurate and fast braking control.

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International Research Awards on Computer Aided Design in Mechanical Engineering

 

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability.

Introduction

The Optimization Problem (OP)1 as defined by refers to the task of identifying the optimal choice among various strategies and parameters under specific conditions. This problem is prevalent in real-world applications and encompasses a wide range of scenarios where the goal is to find the best solution within a set of alternatives. Some of the classic intelligent optimization algorithms, including Particle Swarm Optimization (PSO) inspired by bird foraging behaviors2,3, Genetic Algorithm (GA) simulating genetic and evolutionary processes4, Ant Colony Optimization (ACO)5 mimicking ant collective pathfinding, and Simulated Annealing (SA)6 emulating material annealing, have been widely applied in various fields. In recent years, researchers have introduced novel intelligent optimization algorithms for solving optimization problems. For instance, the Bat Algorithm (BA)7 is inspired by the echolocation behavior of bats in detecting prey and navigating around obstacles. The Grey Wolf Optimization Algorithm (GWO)8 draws inspiration from the leadership and hunting behavior of wolf packs. The Hybrid Frog-Leaping Algorithm (SFAL)9 is inspired by the foraging mechanisms of frogs in constrained environments. Additionally, the Moth Flame Optimization Algorithm (MFO)10 is based on the spiral flight behavior of moths around flames. These emerging algorithms have shown promise in addressing a wide range of optimization challenges. Different intelligent optimization algorithms continue to drive advancements and transformations in the industrial sector and real-world applications. For instance, scheduling problems11,12,13, industrial manufacturing14,15, aviation16,17, facial recognition18,19,20, and medical imaging21,22, among others, have all seen the influence and application of various intelligent optimization algorithms.

The Whale Optimization Algorithm (WOA)23 is a novel intelligent optimization algorithm proposed by Australian researchers in 2016. It is inspired by the collective hunting behavior of whales in the natural world. This algorithm offers advantages such as simplicity in principles, fewer parameters, and ease of implementation. It has successfully been applied to solve a variety of problems in fields such as image retrieval24, image segmentation25, medicine26, energy27, neural networks28, feature selection29, wind speed prediction30, key recognition31, and sentiment analysis32, among others. However, WOA still faces challenges when applied to nonlinear, high-dimensional, and complex optimization problems, including issues related to low optimization precision, slow convergence, and susceptibility to local convergence. To address these challenges, researchers have proposed various strategies to enhance WOA.

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Exploring the Job Market for Mechanical Engineers

 

Traditionally, mechanical engineers are responsible for researching, designing, developing, testing, and improving new and existing products, machines, and tools. However, according to research by ASME and Autodesk, mechanical engineering skills are expanding beyond their traditional scope. As a result, engineers will play crucial roles in developing better, sustainable, and more-efficient products with intelligent product iterations that create new business models. This expansion will include designing with electronics and for product modularity to extend product lifecycles.

Are Mechanical Engineers in Demand?

Excitingly, this evolution in mechanical engineering is creating a high demand for trained workers. Roberta M. Rincon, Ph.D., associate director of research at the Society of Women Engineers (SWE), advises that now is an excellent time to look for a job in this field, as the number of openings far exceeds the number of trained workers available to meet demand.

She cites the recent passage of the Bipartisan Infrastructure Law that increases investment in transportation and green technologies. “Civil engineers, environmental engineers, industrial engineers – new graduates and experienced professionals are needed if the U.S. is to meet the goals set under the current administration," said Rincon. “Technology is changing rapidly, and we need engineers who are nimble, continuous learners to help our global society manage these changes in an ethical and responsible manner while also conducting innovative research.”

What Skills Do Mechanical Engineers Need? 

Rincon said that communication is the one skill hiring managers wish more engineers developed.

“I have heard numerous times that a manager will select an engineer with strong communication skills and so-so technical experience over a strong technical engineer with poor communication skills,” said Rincon.

The reason being that technical skills can often be learned on the job, while communication skills are essential for effective collaboration and project management. However, many engineering programs don't provide enough opportunities for students to hone their public speaking, writing, and team-building skills. As a result, it's up to individuals to actively seek opportunities to develop these abilities.

Rincon advises new engineers to actively pursue opportunities to present their work, engage with peers and faculty, and collaborate on projects to build their communication skills. For example, participating in competitions like the American Society of Civil Engineers’ Concrete Canoe Competition, the Society of Women Engineers’s Collegiate Poster & Rapid Fire Competition, or the American Solar Challenge, where teams design, build, and race solar-powered vehicles. These types of opportunities allow students to build up their technical and communication skills even before entering the workforce.

For established mechanical engineers looking to switch jobs, Rincon said they might be surprised how many skills you gain in one position are transferable to others and across industries. However, she has tips for job seekers to help ease the transition.

“First, find out if there are certifications or courses that you can take that would be useful in the job that you are looking to transition into. There are so many options these days for low-cost or no-cost skills building to take advantage of," said Rincon.

The ASME study highlights the critical importance of developing key skill sets as manufacturing firms increasingly embrace digital transformation through Industry 4.0. These skill sets can be grouped into three categories: hard skills, soft skills, and interdisciplinary skills, which enable productive collaboration across the manufacturing process.

Hard skills encompass a range of technical abilities, including generative design, artificial intelligence/machine learning for product development, design for manufacturing (DfM), coding, 3D modeling/design with an emphasis on aesthetics, data analytics and visualization, prototyping, engineering simulation, and digital twin simulation. Developing these skills is essential for engineers involved in the design phase, as they must possess knowledge of subsequent manufacturing processes.

Soft skills, on the other hand, encompass a range of interpersonal abilities, including creative problem-solving, individual and team collaboration, and effective communication (both written and verbal). These skills are critical for effective collaboration and project management in today's complex manufacturing environment.

Finally, interdisciplinary skills encompass various knowledge areas, including systems engineering, electrical and software engineering, and sustainability. These skills are essential for effective collaboration across diverse teams and disciplines and are critical for driving innovation and sustainable development in modern manufacturing processes.