The mechanical design process

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.

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.

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.

<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>

Communication plays a pivotal role in design teams and can impact team dynamics and design outcomes. Understanding the relationships between design communication and design trajectory can significantly improve our ability to track and optimize design processes. While prior work has investigated design convergence, there is limited work on how team climate and communicative patterns impact the design process. This study investigates the interactions between team climate, communication, and design actions. We estimate team climate as an individual’s perception of their team’s psychological safety, or how comfortable they feel sharing thoughts and ideas freely. Design actions were measured using a behavioral coding schema and audio recordings of design team interactions. In this work, 28 participants and 5 design thinking experts engaging in a design thinking workshop were studied to understand the relationship between design communication, process, and team climate. Contrary to expectations, this mixed-methods study found no significant correlation between perceived psychological safety and design behaviors in engineering design teams, challenging prior research suggesting a positive link. This finding highlights the complexity of team dynamics and underscores the need for further exploration to understand how psychological safety, conversational behaviors, and design actions interact to shape collaborative processes and innovation in design teams.

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.

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.

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.

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.

Chapter 25 - Developing a Design Specification

This paper is presented to participate in a symposium under the theme of “Human Factors in Design”. The title “Human Factors in Design” refers to the research on machine/device designs and studies based on considerations given to the people who use these machines/devices.

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.

1 - Introduction