Artificial Intelligence in Design Engineering
The advantages of artificial intelligence (AI) systems and the direction of machine design are covered in this blog. AI advancements are changing what, where, and how products are designed, manufactured, assembled, distributed, serviced, and upgraded. These technologies include cognitive computing, Internet of Things, 3D (or even 4D) printing, advanced robotics, virtual and mixed reality, and human-machine interfaces. Development of self-repairing, self-healing, self-adaptive, and self-reconfiguring systems - as well as goods that “operationally improve” themselves - may be the end result of the research and related activities.
Such goods might become more valuable and capable through time as opposed to declining. In the future, the human engineer’s position might change from one of producer to one of director. Like one need not be able to use a slide rule or finish an isometric drawing to be a successful engineer today, much of the technical part of engineering will be pushed to the machine-based design system.
AI is applied in a variety of fields within mechanical engineering, including fracture mechanics, predictive maintenance, and many more. The diagnosis of revolving, reciprocating, and hot forging pressing machinery is one use of AI in fracture mechanics. For failure analysis in crash tests, it is also utilized. Some of the AI techniques utilized in the field of fracture mechanics include Bayesian network (BN), deep learning, support vector machine (SVM), ANN, and case-based reasoning (CBR).
Huge amounts of information about the condition of the machines are now available as a result of the growing usage of sensors in machinery. This information can be utilized to correctly estimate how quickly machines will deteriorate and when they would need maintenance. AI tools like machine learning and deep learning are utilized for predictive maintenance. As the amount of data collected grows over time, AI-based systems can produce more accurate findings, extending component life and lowering overall maintenance costs. AI is currently employed in the machine-building industry to design and create intelligent machine tools for health evaluation. AI is employed in sectors related to nano-fluidics because it can manage nonlinear situations and learn from patterns.
Over the past few years, the engineering field has experienced a tremendous evolution. To demonstrate the finished product of a design model, physical models are frequently made. Today, software and machine tools are used by engineers to accomplish this. The following methods are employed by engineers during the design process.
Computer-assisted design -
Design and technical documents employ this technology. It entails building computer models under the direction of geometrical parameters.
Computational fluid dynamics -
It is used in design to examine structural integrity and compliance with requirements.
Finite-element analysis -
It is a tool used in the creation of new products to conduct development analyses and guarantee the speedy application of analysis findings.
Engineers are still in charge of the design process despite the use of these methods. Further system automation is being facilitated by new trends that are emerging with artificial intelligence and other breakthroughs. This article will demonstrate the value of engineers in the design process as well as the potential career paths for engineers.
Creative Generating -
A program generates some outputs as part of the generative design process in order to satisfy predetermined requirements. To investigate design options, designers or engineers enter design goals and parameters into generative design tools, such as materials, manufacturing processes, and cost limitations. Machine learning techniques are used in the solution to determine what functions well and poorly after each iteration.
Quality Control -
The need for meticulous attention to detail in manufacturing is made even more pressing in the electronics industry. In the past, checking the quality of the manufacture of electronics and microprocessors was a laborious task that called for a highly qualified engineer. Its circuits were all set up properly.
Today’s image processing algorithms can check whether a product was manufactured appropriately automatically. This sorting can be automated and done in real-time by placing cameras at key locations on the production floor.
Mechanical behavior forecasting -
The ML models are intended to provide quick and accurate predictions of target mechanical characteristics or behaviors or to identify compositions or structures that perform better than the training data in the design space using datasets comprising materials information.
Large databases often come from experiments or simulations concentrating on composition-property connections, and they are typically obtained for materials with complex and disordered microstructures, such as glasses and alloys. As a result, the chosen features, such as component concentrations, are typically organized as feature vectors, and ML techniques adept at handling input vectors are particularly suited for problems involving the prediction of these materials’ properties.
Topological architecture -
Due to the intimidatingly large design spaces, designing topological structures of multi-phase materials like composites or architected materials is intractable for conventional optimization methods in some respects. However, ML-based models have the ability to explore the design spaces more quickly and find novel designs that outperform the structures in training sets.
Pixel photographs of 2-D material formations can be used as input for image processing algorithms like CNNs and GANs. By including suitable optimization techniques into the workflow, these models can greatly expand the design areas that must be investigated in order to create the best design.
Autodesk Design Graph
Ongoing research into tools like Autodesk Design Graph is well financed. In order to classify and categorize components as well as their relationships and produce a live catalogue, this system uses algorithms to extract significant amounts of data from 3D designs. The system can efficiently pick and categorize the large number of files given to it while compiling them. For the time being, Design Graph can extract searches for specific components, like joints or bolted assemblies, and provide the various alternatives. Contrary to popular belief, Design Graph does not utilize tags or titles to identify sections. To recognize the part, one must be able to recognize its shape and structure.
Will AI replaces Engineers?
NO is the answer. In the future, the role of the human engineer will change from producer to director. Although humans may not be performing the tasks, we will choose the course we want the system to take and give the most crucial feedback: if we are happy with the performance.
In the same way that a successful engineer today doesn’t need to be able to use a slide rule or finish an isometric drawing, the majority of the technical aspects of engineering will be pushed to the machine-based design technique. In some ways, the programmer will develop into someone skilled at deciphering the incipient human desires for goods with a more elegant shape, using fewer resources, or operating more efficiently in a collaborative relationship with an artificial intelligence that can find the solution as long as it is aware of the problem.
Engineering will change until computers can manufacture and even design things on their own, but engineers will still need to be highly skilled. They can benefit from cognitive, mental, and perceptual enhancements from AI technologies. As a result, they will only need to improve their skills, which may include showing the AI systems how to create and work effectively with humans in future human-AI enterprises.
Promoting the effectiveness of material designs through simulations and tests is an obvious advantage that ML gives to research in mechanics and materials. Exploring a vast design space of innovative materials is frequently too difficult to accomplish using physical intuition and intractable for brute force methods. Instead, ML-based design approaches can take into account mechanical and material characteristics during the preprocessing of input data, discover the connection between mechanical and material properties during training, and then produce specific designs utilizing the learned models. It should be noted that ML algorithms may not always be advantageous when handling practical issues if the overall cost of training and design procedures is higher than conventional approaches.
References -
