Fast optimization of mechanical designs using computer aided design/computer aided engineering emulation: A case study

The ever increasing demands for improved performance in engineering design from an overall perspective requires a sophisticated automated design generation strategy that can effectively and efficiently incorporate concepts of uncertainty, quality and robustness into design. Engineering design optimization approaches that typically require modeling the problem in a single objective form become inadequate to address these multiple set of requirements. Instead, this research presents an alternative robust multiple-criteria based design approach. In this approach, decision models are formulated using utility functions that quantitatively represent designers’ subjective preferences and optimal solutions are then determined in a statistical exploration based iterative design of experiment set-up. The resulting Trade-off Based Robust Engineering Design (TRED) method is illustrated with the aid of an example design problem. The results indicate that the TRED approach can be expected to address some of the important issues associated with the engineering design of problems including tolerances, quality control and uncertainties.

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.

North American undergraduate mechanical engineering design education has failed to meet the needs of industry in educating students in effective design philosophies typified by the concurrent engineering design philosophy. Current programmes emphasize traditional engineering analysis courses, leaving little room for truly educating the students in the fundamentals of mechanical engineering design. This paper uses the concurrent engineering design paradigm to design a programme for the education of students in mechanical engineering design. The basics of concurrent engineering design are outlined, the failings of typical design education stated, and an exploration of the required features of a new design curriculum presented.

1 - Design

This paper presents a description of an Intelligent Design Environment (IDE) based on AI tools. Fundamentally an IDE is based on the coexistence of algorithmic procedures, of common usage in Mechanical Engineering Design methods, and knowledge based systems where non algorithmic knowledge is dealt with. This synthesis leads to the development of a powerful design environment where the two types of knowledge mutually enhance their capabilities. Starting from the results obtained during an EC funded research programme, aimed at the optimisation of gear transmissions using AI tools, the paper presents structures and particularities of an IDE that can be used in any domain of mechanical engineering design. The application to gear transmission design is then described in detail including the data acquisition module, the inference module, reasoning strategy and user interface. The system capabilities are critically discussed, particularly by the controversial point of view of the utility of AI methods and tools in Mechanical Engineering Design.

Thin films are widely used in many fields such as electronics, optics or materials science. For example, they find applications in thermal or mechanical sensors design. They are also very useful as protective or reinforcement layers for many structures. However, some coating defects such as thickness variations, microfissuring or poor adhesion are common problems. Therefore, nondestructive testing of these structures using acoustic waves generated and detected by lasers represents a major interest. Indeed, in comparison with conventional methods based on the use of piezoelectric transducers, laser ultrasonics leads to non-contact investigations with a large bandwidth. Usually, bulk acoustic waves are used and a pulse-echo technique is considered that needs high frequencies and implies local measurements. In order to avoid this limitation, we propose to use surface acoustic waves in a frequency range up to 45 MHz. The samples consist of a micrometric gold layer deposited on silicon substrates. In a first part, using dispersion analysis, theoretical and experimental results clearly reveal that the first Rayleigh mode allows the detection of film thickness variations and open cracks. In a second part, a localized adhesion defect is introduced in a similar sample. The effects of such a flaw on the Rayleigh modes dispersion curves are theoretically described. Finally, we experimentally show that the first Rayleigh mode allows the defect detection only under specific conditions.

Integrated Intelligent System for Manufacturing Process

The open arc welding robots have been used widely nowadays; however, the elastic deformations of every inner link inevitably affect the welding quality. In this paper, it proposes an effective method of service reliability assessment for the rigidity-flexibility coupling open arc welding robot. Firstly, a parametric virtual model of the robot is built in ADAMS/View-AutoFlex module. Secondly, the stochastic errors generated in the working process are simulated by a program created from pseudo-random numbers which have been written by multiplicative congruence method. Lastly, Monte Carlo sampling calculation is implemented for the virtual model, and large sample data of the dynamic characteristics in welding process are obtained and used to calculate the service reliability of the welding robot. This method includes simplicity (no need to solve complex mathematic model) and high accuracy which make it highly promising for application in mechanism design.

Constrained Majorization: Applications in Mechanism Design

In our research, we aim to introduce Game-based learning (GBL) activity as part of a holistic approach to supporting knowledge acquisition within a Mechanical Design module. Our case study evaluates Activity Based Learning (ABL) by use of GBL as a tool to drive collaborative student learning. The activity described targets students’ ability to engage in hands-on practical collaborative learning, utilising existing skills in order to collectively share and reinforce knowledge. It relies on knowledge acquired from several subject topics thus consolidating applications through a studio-based activity in the form of a game bringing about its own benefits in teaching and learning. Widely used in a range of subjects, the application of GBL in Engineering and Technology and its effectiveness is less explored and reported as a learning tool in Engineering education. We present an approach to underpinning engineering education as part of a studio-based activity for Mechanical Engineering Design. We explore the options and potential for collaborative learning whilst offering students the opportunity to compete with peer teams for ranked positions on a leader board. We report on the level of student engagement and the extent to which learning outcomes were met through the introduction of such an activity.

Project-based learning in an engineering-design course – developing mechanical- engineering graduates for the world of work

1. Introduction.The Cambridge University 600 kV high resolution electron microscope has been developed as a joint programme between the Department of Engineering and the Department of Physics. The shorter electron wavelength at 600 kV when compared to 100 kV gives a potential improvement in resolution that can only be realised in practice if the engineering design and construction are also correspondingly improved. Many aspects of this microscope have been developed specifically for this attempt at very high resolution. Some of these aspects were reported in September 1977 at the time when the microscope was first operated (Nixon et al. 1977). After three months operation the present resolution is better than 3 Å with no indication that any fundamental design limit has been approached.The following three items have been purchased outside. All other aspects of the microscope have been designed and constructed in Cambridge University. The high voltage generator and accelerator, as described by Reinhold and Gleyvod (1974), has been supplied by Haefely, Switzerland

The International Conference on Mechanical Engineering Design was held at Sri Sivasubramaniya Nadar College of Engineering, Chennai, TamilNadu, India, on 21–22 March 2024 [...]

Designers often gather information, for instance through stakeholder or domain expert meetings, to understand their design problems and develop effective solutions. However, few previous studies have provided in-depth descriptions of novice engineering designers’ approaches to conducting information gathering meetings. In this preliminary study, we analyzed data from six capstone mechanical engineering design teams to identify the types of individuals from whom teams gathered information, when these meetings occurred, and how teams solicited information during meetings. Teams in our study exhibited a range of information gathering behaviors that aligned with recommended practices, particularly in their early meetings. We also observed relatively few instances of teams exhibiting behaviors that were less similar to recommended practices during their meetings. However, our findings revealed two key trends across teams that represented specific opportunities for improvement and that may reflect characteristic novice approaches to conducting information gathering meetings. First, teams explored domain experts’ perspectives in depth during meetings and met with additional domain experts to inform their projects. Teams' meetings with project partners contained few instances of deep exploratory information gathering behaviors in comparison. In addition, teams seemed to finalize design decisions during early design meetings and were less likely to conduct information gathering meetings during later design phases. The comprehensive descriptions of novice mechanical engineering designers’ approaches provided in our preliminary study provide an entry point for further investigations that can inform engineering training, tools, and pedagogy for conducting effective meetings.

This paper reports on the application of computer aided technology to the design of mechanical products within a UK-based heavy/ medium, batch-manufacturing company operating in the marine engineering sector. An engineering design methodology is outlined which judiciously exploits particular software techniques associated with the two kinds of technology available in the company concerned. These software techniques range from ‘custom-built’ user-extension programs (developed for specific design applications) for use with a minicomputer-based computer aided design (CAD) system, through to enhanced ‘off-the-shelf ‘ applications packages for use on microcomputer hardware. The authors attempt to demonstrate that effective application of the available technology has essentially involved synergizing that technology with the design engineer's knowledge and abilities throughout the design process, and not relying on locking into the use of computer aided methods as a ‘technological fix’ for design problems.