My genuine question to the respected authors and scholars is, what is the use of the research work in building prediction models for defects (such as porosity) when there are powerful modelling software like Ansys, which already has the feature of defect prediction of additive manufacturing in them (like melt pool prediction and porosity prediction).
I have found this question as I have started working on a research topic regarding optimisation of process parameters of laser-based metal additive manufacturing, using machine learning to get improved melt pool characteristics and thus increase the quality of the product (reduced porosity). But I have also found that there are software that already does the same task, and it would not be useful to model ML algorithms that do the same task.
Are there any research gaps in the respective field?
Dear Vishnu Swaroop
These articles might be helpful, have a look:
1. https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/
2. https://www.pewresearch.org/internet/2018/12/10/improvements-ahead-how-humans-and-ai-might-evolve-together-in-the-next-decade/
3. https://hbr.org/2007/07/six-rules-for-effective-forecasting
4. https://amfg.ai/2019/08/21/10-predictions-on-the-future-of-3d-printing-expert-roundup/
5. Article Machine learning applications in cancer prognosis and prediction
6. https://www.sciencedirect.com/science/article/pii/S0040162517300276
Kind Regards
Qamar Ul Islam
Thank you, Qamar Ul Islam, for your answer. I'll have a look at the articles.
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