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AI has the potential to revolutionise mechanical engineering by enhancing productivity, efficiency, and innovation. Its applications are diverse, ranging from design optimisation and predictive maintenance to autonomous systems and robotics.
Gain an intuitive understanding of AI and learn how to scope, plan, and develop AI tools to enhance productivity and cut costs with the Institution’s recently launched AI for engineers course.
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Why engineers can unlock economic potential
To effectively build AI models and systems, engineers need a range of skills and knowledge, including digital skills, math, and domain/business expertise.
Traditional research in any field applies techniques from mathematics and statistics to a certain domain or problem. Machine learning in its simplest form is statistical and numerical problems solved iteratively with computational mathematics. Software Engineering is about solving a domain or business problem with the help of computers and custom developed tools. However, by combining these skills, engineers can work with data in ways that have given rise to the field of Data Science - which also includes AI model development
Data science is a multidisciplinary field that involves extracting insights and knowledge from data using various techniques and methodologies. It combines elements of mathematics, statistics, computer science, and domain expertise to analyse and interpret large and complex datasets.
The engineers of tomorrow are also data scientists
Engineers of tomorrow can also be data scientists without having to leave the engineering field behind. Most engineering problems involve working with data in spreadsheet tools like Excel. This often involves running mathematical calculations on this data such that certain values are calculated for solving design, maintenance, or operational problems.
What sets engineers with data science skills apart is their deep domain expertise and strong mathematical background. This gives them an advantage, allowing them to create AI tools that don't exist yet or work with engineering datasets that data scientists from another field may not be able to use. However, to unlock this potential, engineers need to learn new skills and work with more tools.
The need for digital skills and programming knowledge
While engineers can certainly use tools like Excel for various tasks, it is not the most suitable tool for building complex AI models. Excel has limited computational and data processing power and does not provide any tools for building AI models. In these cases, programming languages like Python are necessary for engineers to handle complex or large datasets.
The main point to takeaway is engineers need a diverse skill set that includes digital proficiency, mathematical aptitude, and domain expertise. By combining these skills, engineers can delve into the field of Data Science, extracting valuable insights from complex datasets.
By embracing AI and honing their digital and programming skills, engineers can position themselves at the forefront of innovation, shaping the future of their field. The engineers of tomorrow have a unique opportunity to leverage the power of AI, driving advancements and making a significant impact in the increasingly technology-driven world.
Ali Parandeh CEng
IMechE trainer
The Institution recognises the critical role AI will play in shaping the future of engineering. That’s why we have collaborated with industry experts to develop three new AI-focused courses, ensuring you receive the most relevant and practical training available.
AI for engineers
Foundation Python for mechanical engineers
Building AI models with engineering datasets
For more information, or to discuss specific training requirements, contact us at training@imeche.org or by calling + 44 (0)20 7304 6907.