AI will impact every industry and occupation; the question is, how will mechanical engineering fare in the world of the future? Some subfields of mechanical engineering are on the cusp of experiencing a paradigm shift thanks to AI.
The Contribution of AI to Complicated CAD
Artificial intelligence in computer-aided design (CAD) typically operates on knowledge-based systems. In CAD, design artefacts, rules, and problems are saved to provide CAD designers with assistance later. Model-Based Reasoning is the method that enables the combination of CAD and AI (MBR). Knowledge-based systems are used in a significant number of recently released software packages.
Generative design is one of the most critical areas in which AI can be applied. The design requirements are the input the productive design tool receives, and the possible designs are the output. With the release of SolidWorks 2018, the software company introduced a new topology optimisation feature that uses a different algorithm based on generative design.
Artificial Neural Networks in CFD Computational Fluid Dynamics have attracted much attention from researchers in various fields, including mathematics, engineering, and computer science.
Academics are becoming increasingly interested in Artificial Neural Networks (ANN) because of their potential to provide flow approximations while requiring less computing power, time, and dimensional reduction of problems. In addition to this, they are demonstrating a good agreement with conventional CFD models. The difficult task at hand is to train ANN using many example simulations. In addition, using neural networks does not allow one to understand the flow mechanism.
IoT and Data Analysis
The fourth industrial revolution will connect all of the machinery in a production plant and consumer products. This will allow engineers to analyse, improve, and guarantee the product’s quality. Engineers capable of reading between the lines of sensor data will be required to manage such technical data. It would be necessary to have mechanical engineers who are also skilled in artificial intelligence to work on software that can handle the data provided by sensors embedded in various components of a power plant, production facility, or consumer product. One application of data science can be found in the optimisation of power plants.
The data gathered from Supervisory Control And Data Acquisition (SCADA) systems can be used to help predict failures, thereby preventing any financial or personal loss.
How Should One Get Ready for This Emerging Trend?
In the context of learning AI with the end goal of applying it to mechanical engineering, which languages and skills should receive the most attention?
Python is by far the most popular choice for use in machine learning for mechanical engineers when it comes to programming languages (ML). Python for mechanical engineers and R are both helpful programming languages for analysing data. The majority of mechanical engineers will use artificial intelligence and machine learning either as a component of a tool (such as CAD/CAM or FEA software) or as support for data analysis and decision-making.
The applications of AI and ML that involve designing and analysing things will be the first to experience the benefits of these technologies, and they are already being used in commercial settings. AI and machine learning can disrupt various domains that call for optimisation, estimation, or evaluation of potential solutions.