Grammars for Machine Design

Mechanical design is the process of defining a mechanical system for a specific behavior. To support mechanical engineers in their tasks, research in studies on the design process is important. Currently there is a lack of meso-level studies on iterations in mechanical design research. We propose a documentation method for such studies. Target of the method is enable identification of iterations in mechanical design processes. Iterations occur regularly in design processes and can have an impact on project cost and quality. Therefore, they can be used to identify methodical need. We apply the method in three mechanical design projects and show its suitability for mechanical design process research.

ABSTRACTIntroduction: Conventional surgical drills are rigid straight instruments used to make holes in bones. They lack the ability to follow a curved pathway, making them impractical for several surgical procedures. For this reason, there is a continuous need for improved devices for surgical drilling of curved holes.Areas covered: This review provides a comprehensive overview and classification of the patent literature of surgical drills able to produce a curved hole. The goal is to identify the fundamental mechanical designs of the drills. The medical section of the Web of Science Derwent Innovation Index was scanned combining keywords for both steering and drilling. Overall, 41 unique patents were reviewed and categorized.Expert opinion: Drills were subdivided in four groups based on the capability of either drilling a single curved path or a multi-curved path and on their ability to adjust the path after insertion of the drill into the bone. We found patents describing instrument designs for all these four groups. The insight in the drilling capabilities and in the mechanical designs described in the patents may serve as a source of inspiration for the design of novel surgical drills and the development of new surgical procedures.

This issue presents a special supplement on the theme of Spatial Mechanisms and Robot Manipulators edited by Gordon Pennock and Madhu Raghavan. This supplement is designed to combine the insights of leaders in mechanism theory and robotics with current research in this area of our mechanical design research community.The Journal has seen a growth in submissions from around the world, which must be attributed to the efficiency of the web submission process, and the ability of our associate editors and reviewers to respond promptly. During the period of December 2004 through November 2005, over 450 papers were submitted to the Journal, and approximately two-thirds have been closed in an average of three months. The remaining papers have been open for an average of four months.The acceptance rate among the closed papers is 28%, which is lower than in the recent past. This seems to be a result of a number of authors meeting our community for the first time, and learning the scope and depth of a contribution to mechanical design research. In this interaction I regularly see the profound benefit to the health and growth of our research community of detailed reviews for even those papers not deemed publishable. Few authors turn their back on careful though negative reviews, and most seem anxious to meet the challenge of generating a quality contribution as judged by their peers.The growth of the Journal continues to be supported by the ASME Publication Committee, which has agreed to expand the page count to 1400 pages. This allows us to publish approximately 150 journal articles, or about 33% of the papers submitted in the past year. There is also some discussion of moving to monthly issues, perhaps as early as January 2007.The success of the Journal relies on the hard work of Tara Smith and Cynthia Clark and their colleagues at ASME, and of Tanya Eberhard at UCI, as well as the daily efforts of our 22 Associate Editors, and their small army of reviewers. It is an honor to be part of this commitment to quality scholarship in mechanical design research.

Describing the design space of mechanical computational design synthesis problems

The effectiveness of current product design and manufacturing techniques is often reduced by the constriction of design spaces and the inclusion of various constraints based on the use of past examples, experience and intuition in the initial design stage. The expansion and transformation of design spaces that have been previously formulated and contracted is difficult. All too often, the original goal of creating the best possible design is replaced with a mechanical procedure to obtain an optimum solution based on a constrained design formulation. This paper proposes a methodology for breaking through current limitations by re-formulating previously formulated design spaces, using the conflicting preference information of each expert so that the satisfaction level of each decision-maker can be improved during collaborative optimization. A mechanical design example is offered to demonstrate the effectiveness of the proposed method.

Traditional mechanical design relies on iterative refinement cycles driven by expert heuristics and computationally expensive Finite Element Method (FEM) analyses to meet performance targets. While machine learning approaches have been developed to automate portions of this process, they typically demand large datasets, substantial computational resources, and remain narrowly tailored to specific domains, thereby limiting their generalizability across diverse design problems. We propose a framework coupling a pretrained Large Language Model (LLM) with an FEM module to autonomously generate, evaluate, and refine structural designs based on performance specifications and quantitative feedback. Without domain-specific fine-tuning, the LLM leverages general reasoning to iteratively propose candidate structures, interpret simulation results, and implement mechanically informed modifications. Demonstrated on 2D truss structures, the approach navigates complex design spaces, balances competing objectives, and identifies appropriate stopping points when optimization yields diminishing returns. Compared to traditional methods like NSGA-II, LLM-guided optimization achieves faster convergence with fewer FEM evaluations in highly discrete, multi-faceted design spaces involving dynamic node generation and discrete member sizing. Experiments across multiple temperature settings and model scales reveal that smaller models achieve slightly better constraint satisfaction with fewer iterations, while lower temperatures produce more consistent performance. These findings position LLM agents as a promising new class of natural language-based, reasoning-driven optimizers for engineering design.

During the design process, a designer transforms an abstract functional description for a device into a physical description that satisfies the functional requirements. In this sense, design is a transformation from the functional domain to the physical domain; however, this transformation process is not well characterized nor understood for mechanical systems. The difficulty arises, at least in part, because mechanical designs are often composed of highly-integrated, tightly-coupled components where the interactions among the components are essential to the behavior and economic execution of the design. This assertion runs counter to design methodologies in other engineering fields, such as software design and circuit design, that result in designs in which each component fulfills a single function with minimal interaction. Because of the geometry, weight, and cost of mechanical components, converting a single behavioral requirement into a single component is often both impractical and infeasible. Each component may contribute to several required behaviors, and a single required system behavior may involve many components. In fact, most mechanical components perform not only the desired behavior, but also many additional, unintended behaviors. In good mechanical designs, these additional behaviors often are exploited. The long term goal of our research is to create a transformational strategy in which the design specifications for a mechanical system can be transformed into a description of a collection of mechanical components. To realize this goal requires formal representations for the behavioral and the physical specifications of mechanical systems as well as formal representations for the behaviors and the physical characteristics of mechanical components. Because the interactions of components are important in our synthesis strategy, the representation of the behaviors of mechanical components must be linked to the representation of their physical characteristics; that is, we are concerned with modeling the relationship between form and function of components. Finally, we need a strategy that enables us to transform an abstract description of the desired behavior of a device into a description that corresponds to a collection of available physical components. In this paper, we present a graph-based language to describe both the behavioral specifications of a design as well as the behavior of the available physical components. We also briefly discuss a graph-based grammar for the representation of the physical characteristics of the components that enables us to guide the translation from specifications to components [Pinilla 89]. The transformation strategy is discussed in a companion paper [Hoover 89].

Nuclear energy provides enduring power to space vehicles, but special attention should be paid to radiation shielding during the development and use of nuclear energy systems. In this paper, novel composite materials containing poly-ether-ether-ketone (PEEK) as a substrate and different tungsten contents as a reinforcing agent were developed and tested as shielding for gamma-ray radiation. Shielding test bodies were quickly processed by fused deposition modeling (FDM) 3D printing, and their mechanical, shielding properties of composite materials were evaluated. The results revealed shielding materials with excellent mechanical properties which can further be improved by heat treatment. Under 0.45 MPa load, the heat deflection temperature of PEEK/tungsten (metal) composites was significantly lower than that of PEEK/boron carbide (non-metal) composites. The new shielding materials also demonstrated better shielding of low-energy 137Cs than high-energy 60Co. The gamma-ray shielding rates of test pieces shielding materials made of the same thickness changed exponentially with the tungsten content present in the composite materials. More tungsten led to a better shielding effect. At the same tungsten content, the gamma-ray shielding effect showed a proportional relationship with the thickness of the shielding test bodies, in which thicker test bodies induced better shielding effects. In sum, the integration of 3D printing in the mechanical design and manufacturing of shielding bodies is an effective and promising way for quick processing when considering diverse rays and complex environments. Lighter shielding bodies, at lower costs, can be achieved by structural design in limited space to maximize the material utilization rate and reduce waste.

This paper discusses implementation conditions based on the Bayesian equilibrium, in which different parameter spaces are affected by controlled variables chosen by mechanism designers and a theoretical foundation to discuss the change in design for parameter spaces under the bi-variable scenario is provided. Then we provide sufficient and necessary conditions of mechanism duality when the goal function remains unchanged which is applied in state-owned land auction mechanisms in China. The results show that when players strategically choose their action, the mechanisms of limited housing price auction and of limited land price auction are dual equivalent while the government’s revenue is maximized. The key difference between the efficiency of the market mechanism and that of the non-market mechanism lies in the tradeoff between the government’s interests and consumers’ benefits.

Design space is a common abstraction used in the investigation of design cognition. Characteristic properties of design spaces and how they change are underexplored. Design spaces can vary with the design task and its constraints, which are assumed to result in differences in neurocognitive processes. We review general cognition, creative cognition and design neurocognition EEG studies. We analyzed the brain activity of 32 professional mechanical engineers and industrial designers while performing constrained and open design tasks. The neurophysiological activations during reading the task, earliest reaction, and open externalization stages of constrained and open design are compared based on EEG frequency band power. Significant differences between constrained and open design for the beta bands were found in the earliest reaction stage. Significant differences between constrained and open design for alpha 2 and the beta bands were found in the open externalization stage. We discuss the results and relate the higher brain activity and significant differences in open design to cognitive functions of interest to design cognition. We show that EEG brain activation is sensitive to the level of constraints in designing, in particular alpha 2 and beta bands can act as proxies of the change and expansion of design spaces.

Natural nanomechanisms such as capillaries, neurotransmitters, and ion channels play a vital role in the living systems. But the design principles developed by nature through evolution are not well understood and, hence, not applicable to engineered nanomachines. Thus, the design of nanoscale mechanisms with prescribed functions remains a challenge. Here, we present a systematic approach based on established kinematics techniques to designing, analyzing, and controlling manufacturable nanomachines with prescribed mobility and function built from a finite but extendable number of available “molecular primitives.” Our framework allows the systematic exploration of the design space of irreducibly simple nanomachines, built with prescribed motion specification by combining available nanocomponents into systems having constrained, and consequently controllable motions. We show that the proposed framework has allowed us to discover and verify a molecule in the form of a seven link, seven revolute (7R) close loop spatial linkage with mobility (degree of freedom) of one. Furthermore, our experiments exhibit the type and range of motion predicted by our simulations. Enhancing such a structure into functional nanomechanisms by exploiting and controlling their motions individually or as part of an ensemble could galvanize development of the multitude of engineering, scientific, medical, and consumer applications that can benefit from engineered nanomachines.

Based on the feature requirements of mechanical product configuration design principle, this paper takes the passenger car product as an example to transform the nominal variable and numerical variable parameters of key component configuration design features into standard values, and extract the feature of functional requirements most concerned by customers from different customer orders as the feature of key component configuration design. From the perspective of demand, the principal component analysis method is used to analyze the customer order, and the configuration design characteristics of key parts of passenger car are determined according to the contribution degree of eigenvalue.

High-precision and large-stroke XY micropositioning stage based on serially arranged compliant mechanisms with flexure hinges

In most instances it is necessary to partition a product realization process into a set of design activities. Design decisions are an important type of design activity. In order to ensure that information flows among different design activities are achieved without difficulty, uniform representation of the information is necessary. A design activity template and a solution template are presented in this paper to support the information flow in a design process. Activity template provides an option to describe the design problem so that engineers know exactly what kind of design decision is preferable. Solution template provides an option to describe the design solution. The paper also introduces an approach to manage and deliver design freedom from one engineering team to the other. In mechanical design, it is essential to give engineers a feasible design space so that they can choose the design solutions that best satisfy the design problem. A chip design example is used to illustrate the different concepts presented in this paper for collaborative decision making.

Recent developments in design and manufacturing have greatly expanded the design space for functional part production by enabling control of structural details at small scales to inform behavior at the whole-structure level. This can be achieved with cellular materials, such as honeycombs, foams and lattices. Designing structures with cellular materials involves answering an important question: What is the optimum unit cell for the application of interest? There is currently no classification framework that describes the spectrum of cellular materials, and no methodology to guide the designer in selecting among the infinite list of possibilities. In this paper, we first review traditional engineering methods currently in use for selecting cellular materials in design. We then develop a classification scheme for the different types of cellular materials, dividing them into three levels of design decisions: tessellation, element type and connectivity. We demonstrate how a biomimetic approach helps a designer make decisions at all three levels. The scope of this paper is limited to the structural domain, but the methodology developed here can be extended to the design of components in thermal, fluid, optical and other areas. A deeper purpose of this paper is to demonstrate how traditional methods in design can be combined with a biomimetic approach.