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Vaibhav D.
Senior Data Scientist at DMI Finance
Vaibhav Devdhe is a seasoned professional with expertise in software engineering, product management, and business analysis. With a degree in Computer Science from Pune University, Vaibhav started his career as a software engineer and quickly rose through the ranks to become a team leader. Over the years, he has worked with top companies such as Accenture, Symantec, and Infosys, gaining valuable experience in the areas of software development, project management, and client relationship management.
Vaibhav's passion for innovation and entrepreneurship led him to launch his own startup, where he has been responsible for product ideation, design, and development. He has also been involved in fundraising and managing investor relationships, which has given him a unique perspective on the start-up ecosystem.
As a thought leader in the technology space, Vaibhav is an active contributor to developer communities and has spoken at industry conferences. His strong analytical skills and ability to translate complex technical concepts into business outcomes make him a valuable asset to any organization.
Highlights
GDG Pune started.. #GDG_Pune
Last week, got an opportunity to present my #research work in International #conferences on 'Manufacturing Excellence'.
The research is all about to optimizing process parameters of CNC Turning center during machining operation for max. tool life along with min. the #production time to reduce the production cost.
One of the renowned industry has approached us with this problem and objective of reduction in cost.
For this research, we have used #Taguchi #optimization technique and Analysis of Variance is used for validations.
We had implemented L9 Orthogonal array with 3 parameters and 3 levels of each parameters. So with just 9 experiments we had received significant results.
When we implemented the finding of experimentation, we were amazed by outcomes. Now with the setting of optimized parameters, industry observed 66% reduction in tool cost and each part to be machined requires less time. It means if earlier industry was investing 1000 bucks for tools, now they had to invest approx. 330 bucks.
It was completely different problem that we see in normal Data science problems, where we have huge data and we approximate the solutions, but here we just had 9 experiments and we had significant results.
Guys, please explore other domains and be sure you were amazed.
