Chapter 25 - Developing a Design Specification

1 Why Study the Design Process? 2 Describing Mechanical Design Problems and Process 3 Designers and Design Teams 4 The Design Process 5 Project Definition and Planning 6 Understanding the Problem and the Development of Engineering Specifications 7 Concept Generation 8 Concept Evaluation 9 The Product Design Phase 10 Product Generation 11 Product Evaluation for Performance and the Effects of Variation 12 Product Evaluation for Cost, Manufacture, Assembly, and other Measures 13 Launching and Supporting the Product Appendixes A Properties of 25 Materials Most Commonly Used in Mechanical Design B Normal Probability C The Factor of Safety as a Design Variable D Human Factors in Design E TRIZ F Belief Map Masters

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.

Application of the quality function deployment method in the mechanical structure design of subsea power devices

With the complexity of mechanical manufacturing system design increasing, the traditional single-objective optimization method can no longer meet the needs of multi-objective optimization, such as lightweight, high efficiency, low energy consumption and long life. Multi-objective optimization algorithm is widely used in mechanical design and manufacturing process planning because of its good convergence and diversity. Taking robot motion planning as an example, this paper analyzes the application methods of multi-objective optimization algorithm in mechanical structure design in detail, including problem description, multi-objective optimization model construction, algorithm application and performance analysis. Through the application of Genetic Algorithm (GA), this paper shows how the multi-objective optimization algorithm can balance multiple objectives such as path length, energy consumption and obstacle avoidance effect, and realize more intelligent and efficient solutions in robot motion planning. Performance analysis shows that GA is superior to single-objective optimization and random search algorithm in optimization effect, computational efficiency and stability. In addition, this paper also discusses the influence of the change of target weight and algorithm parameters on the optimization results, which provides valuable reference for future algorithm optimization and practical application. Multi-objective optimization algorithm shows obvious advantages in mechanical manufacturing system, which provides new ideas and methods for realizing more efficient, more environmentally friendly and more intelligent mechanical manufacturing design.

The best control performance in mechatronic systems cannot be obtained by only control algorithms, because the control is effective only within the performance range realizable by an actuator system in practice. To obtain the best control performance, therefore, the mechanical system should be designed considering the control performance first. It is, however, difficult to expect the control performance in the mechanical design process, since the control performance is dependent on various unexpectable factors, such as input and output saturations of an actuator, heat problems, sensor limitations and so on, as well as the mechanical design parameters. Therefore, in this paper a recursive mechanical design process based on control experiments, called Iterative Learning Mechatronics (ILM), is proposed. The proposed method seeks a better mechanical design parameter based on a set of control signals, such as a control input and a tracking error, and suggests an update of the design parameter. For verification of the proposed method, the ILM is applied to obtain the best leg stiffness of a robotic leg bearing a load.

In order to solve the problems of complicated design process, insufficient optimization of design parameters and low reuse of design knowledge during traditional mechanical product detailed design process, this paper proposes a rule-driven product configuration design method. Traditional mechanical product design process is divided into a number of design nodes and partially complex functions of traditional design process are defined in nodes. Design nodes are configured and connected after forming rule-driven nodes ,which sharply simplifies the configuration process of traditional design process and can realize the optimization of design parameters. The basic database that adapt to the design node is developed and the design knowledge is integrated into the basic database and node rules, which improves the configurability of the design process and better supports the parameter optimization of the design process and reuse of design knowledge. Finally, the system software is developed and the design process of Marine diesel turbocharger is taken as an example to verify the effectiveness of the method.

This paper investigates a computer environment approach for the exploration of design behavior in the mechanical design process. Generic component types and behavior modelers are developed based on the needs of mechanical designers and are represented in a computer environment. Built-in component types and physical behaviors are also developed. Extension can then occur as needed during design refinement. The resulting system can support exploration and knowledge refinement during design.

Integration of materials and manufacturing engineering technology directly into the mechanical design process through physics-based computational tools is revolutionizing the way components are being design and manufactured within the turbine engine industry. Linkage of mechanical design and solid models with simulation tools that provide manufacturing and mechanical property information is providing design engineers with greater capabilities to conduct holistic design optimization. In the paper presented, examples of materials and manufacturing process simulations along with the overarching approach within ICME (integrated computational materials engineering) to link these simulation capabilities to design methods will be discussed.

<p>This study investigates the innovative application of neural networks algorithms in the aviation industry's mechanical design process, motivated by the pursuit of creating a more accurate and efficient method for performance prediction. Traditional approaches, such as computational fluid dynamics (CFD) simulations based on solving Navier-Stokes’s equations, demand substantial computational power and often exhibit limited accuracy, particularly when compared with complex geometries. The state-of-the-art review unveils a growing research trend advocating for data-driven methodologies to revolutionize design practices, addressing the limitations of conventional techniques. The primary objective of this study is to explore how neural network algorithms can overcome the drawbacks of CFD simulations, offering a more effective alternative for predicting the performance of airfoils. To achieve this objective, we conducted a performance analysis of airfoils using neural network algorithms. The results presented a promising avenue for a more accurate and efficient performance prediction method through digital twinning. The study highlights the advantageous features of neural network methods in unmanned aircraft vehicles (UAV) component mechanical design, showcasing their potential to outperform traditional methods and offering practical recommendations for integration into the design process.</p>

Computational support for concept exploration has been a research development in the last two decades, and the ascent of AI tools such as Chat GPT and generative design is further expanding it. Mechanical engineering tends to focus on more stringent functional requirements and compliance with regulation, and while the potential of AI developments to support it exists, it also likely requires significant oversight by the human. The levels of helpfulness/need for oversight and checks by humans are explored in terms of where AI could be beneficial in the mechanical engineering design process, how this could be supported, and how reliable can it be in that context. Exploring the use of AI in mechanical engineering design and the level of trust placed on its outcomes is an important question for future engineering design development, especially since AI and machine learning are improving exponentially. Exploration of students’ perception of AI and its objective usefulness will be contrasted, by performing a comparison between designs conducted with and without AI support. AI tools used will focus on product design specification generation, concept image generation and generative design. Then recommendations will be given on tools that are currently considered to be helpful for mechanical design development, highlighting the positives and negatives of the approach using AI and potential for adoption of AI in engineering design education.

The mechanical design and manufacturing process of a high-torque, outer rotor PMSM with tooth-coil winding and solid rotor yoke is presented. Throughout the design process the principle of modularisation is applied for efficient construction of this rather large machine in the context of university equipment and workshops.

Muon tomography is a technique based on the observation of the absorption of muons in matter, as the ordinary radiography does using X-rays. The interaction of cosmic rays with the atmosphere produce Extensive Air Showers, which provide an abundant source of muons. These particles can be used for multiple applications on muon tomography, in particular, to study the internal structure of different volcanoes edifices. It is necessary in any muon tomography experiment a particle detector that has the capability to be sensitive to muon interactions. Here we report a brief description of the first steps towards a complete design of a prototype particle detector to perform muon tomography in harsh conditions encountered in the surroundings of the Galeras Volcano. The mechanical design and fabrication processes of the supporting structure of the muon detector prototype, and first steps towards a future detector simulations on GEANT4 are described in this work.

Based on the basic process of mechanical product design and a modular design idea, the module includes three parts, i.e. design calculation, computer-aided drawing and foundation database. The design calculation mainly includes the commonly used design calculation program in mechanical product design, and the computer-aided drawing mainly includes the parametric drawing programs about the drawing environment and the commonly used machine parts, the foundation database mainly stores the data related to the mechanical product design. Then a computer-aided design module for mechanical product will be constructed by Autolisp and DCL. And the proposed computer-aided design module is developed on the friendly interface, easy to use, and can provide mechanical engineers for product design efficiency greatly.

Among mobility issues of tracked vehicles, obstacle climbing is one of the most important problems which should be addressed. In this paper, we present a theoretical method of deriving the maximum height which a tracked robot (with and without sub-tracks) is capable of climbing. This can be employed as a useful factor in mechanical design process, judgment of obstacle climbing, optimization, and autonomous train traversal algorithms for tracked vehicles. Geometrical dimensions and position of the Center of Gravity (COG) are the parameters which this method depends on. Finally, the validation of the theory is done using two heterogeneous tracked vehicles.

Traditional mechanical design process faces three major challenges: (1) the need for rapid and repeated numerous time-consuming simulations, such as finite element analysis and computational fluid dynamics; (2) the need for dataset management; (3) the lack of an integrated and easy-to-use collaborative mechanical design environment. This paper presents a grid-enabled solution for mechanical engineering design, which integrates three traditional design phases (preliminary design, structure verification, and performance evaluation) into one workflow, utilizing grid resources for calculations. This solution aims to accelerate each phase in design process, accommodate more models, and prevent the best results being discarded during the early design stages as it may happen in the conventional design procedures. The applicability of this solutions shown by a case study.