Machine Learning Algorithms with Python - Engineering Assignment Help

Assignment Task

Thermal analysis is an essential feature of modern design (Hughes, 2019). In order to test materials, thermal simulations are used to see if they can perform at different temperatures or with new manufacturing methods (Hughes, 2019).

Finite element analysis is the simulation of a physical phenomenon using a numerical approach, known as Finite element method (FEM) (English, 2019). Whilst, generally the accuracy of the solution improves as the number of elements increases, nonetheless the computational time and costs also increases (Fish and Belytschko, 2007). Moreover, meshing consumes a large amount of engineering time and creates many delays in the design process (Fish and Belytschko, 2007). The increase in computing time is a disadvantage that most users suffer from, since it converts the work of hours to days. The recent advancement in machine learning allows us to re-evaluate how we design mechanical parts (Kerns, 2019). Machine learning is a branch of artificial intelligence (AI), characterised by collecting a dataset and algorithmically constructing a mathematical model based on that dataset as the method of solving practical problems (Burkov, 2019). Figure 1 demonstrates the wide range of methods that can be used to train a model:

The objective of this project is to test whether the use of machine learning could predict the thermal performance of a two-dimensional design by training an algorithm with FEA big data. These leads to the second hypotheses being tested: (H2) “The algorithm will be able to output the thermal performance of the design without doing a finite element analysis”. The algorithm will use Python code to solve the computational problem. Whilst computational time has been a problem that has affected design cycle time for decades, nonetheless this new path where artificial intelligence meets design tools could be the shift that reduces
computational time. Furthermore, it could provide designers with a deeper insight of interpreting and modelling finite element problems independently of their experience with finite element analysis.

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