Data Science/AI for Mechanical Engineering

The Indian Education System's lack of emphasis on practical work is the main cause of career failure. One thing we may observe in Tier-3 colleges in India is that students who perform poorly in lab or practical exams, such as the welding lab in the mechanical or electrical lab, are occasionally disregarded for that subject and are granted passing scores. All the practical experiments are outdated. Instead of watching that student measure correctly with a micrometer,more emphasis is placed on whether he or she uses a black pen for giving titles in the practical notebooks.

At the final point, In reality, in the academics of Btech,students don't have skills that are really needed in Industry. Students are focusing on outdated projects like GO-karts,CNG bikes, electric cars,self-balancing bikes, etc. After pursuing graduation in Mechanical engineering, when students try to get jobs, they are mostly underrated because of competition. At last, many graduate engineers are only able to get jobs like CNC operators,  service engineers, etc. The real meaning of BTECH is to research and invent, which is why the real places for BTECH guys are: Research and Development department
Planning and Control department

One thing to keep in mind is that "a company" is not established to provide jobs; it is only there to earn profit. If a student seeks a better position in industry,that firm will only ask, "What can you do for us?". What are those skills that the industry prefers when selecting candidates for good posts?What are those skills that industry prefers while selecting candidates for good post?
1- Cost cutting Skills
2- Human Effort reduction

Believe it or not, if a person is able to reduce manufacturing costs for a company even by 2–5 rupees, he is a real hero for it. But the thing is, to reduce manufacturing costs, much higher knowledge of material science is required. That's why IIT students are selected at Higher departments of Firms such as TATA,Maruti Suzuki, Bosch, etc. because they start research work in the middle of their course.

In Industry 4.0, Advancement means less manpower in factories and less human effort from customers.

Lets see two types of Students giving CV's to HR:

What do you think will be selected? Obviously, Jatinder Yadav, because his skills and projects match the industry's requirements. The meaning of technology is less human effort. That's why the mechanical industry demands predictive analysis of faults,Automation and robotics,data-driven automatic decision-making systems,Flow pattern recognition in Fluid dynamics, and so on. So finally, students should learn these types of skills while pursuing graduation. What is the key technology behind such types of tasks?

Artificial Intlelligence

Yes Whatever your engineering branch is, AI is an essential subject to learn to grow and succeed in the upcoming Industrial 5.0 era.

Artificial Intelligence simply is What people are doing is done by machines. It may be a Robot chatting on a website,Tesla Auto Mode,Alexa as your girlfriend, a robotic arm in manufacturing, etc.

What makes the machine think like humans? That is Machine learning.
Machine learning is a tool by which machine processors are able to learn something from images,raw data, voice,voice etc. There are thousands of mathematical algorithms in ML by which machines grasp hidden patterns in data and predict something. Example: We have a dataset from Carwale.com in the form of an Excel sheet that includes details of cars (engine cc,bhp, number of owners,km driven) and selling price. We will run a ML algorithm by which the computer will start seeing correlations between different columns and their overall relation to the selling price by using a prewritten mathematical formula. This is known as training." After training is done, we will provide new data on a used car, and the computer will predict its selling price.

Data science is simply a process to solve a task that depends on data.

Example:Again taking Second hand car price task.Let's understand what will be the steps.

Step1:We will collect data on all cars that were sold on Carwale.com (in Excel).
Step2: After collection, we will clean the data, which means if there is a missing or corrupt value, we will either delete it or try to find it using statistics or monthly.
Step3:Now we will perform Feature Engineering. As we know the columns of our data, i.e., Car name, km driven, number of owners, power, and finally the main column, which is selling price, We know car prices depend on the rest of the columns, so we will delete unusual columns that do not affect the selling price; convert alphabetic columns (car names) to integer form because machines only understand numbers; scale down the data; plot graphs of columns to see more ways to simplify the data; and many more steps. These are all steps known as feature Engineering. In Feature engineering, we tend to convert data into a more simplified form that will be easily understandable by machines.
Step4: Split the data to train and test (just like our math teacher solves some numericals in class and a few questions are kept for the homework test).
Step5: Here comes the role of ML. An algorithm will be selected from machine learning by which the machine will understand the patterns of the data. (Don't focus on how to select; how the machine finds patterns will be discussed later.) We have different methods to learn different subjects; we don't use the same method for Social study that was used for math. Like us, there are different algorithms by which machines learn data.
Step6: After training is done, we will perform testing. In testing, we will give car details, and the machine will predict the price. If the difference between the predicted price and the original price is greater, then we have to repeat the above steps in a different way or change the algorithm.
Step7: Finally, we will save the learning. (Again, don't focus on what the learning form is; just assume that our brain stores information in complex form, just as machines do.) We will discuss it later), and that learning may be used by customers to predict their car prices on the website.
ALL the above steps are Data Science.
Steps will vary according to the task. If there is image data, the steps will be different, but the overall task is the same.
Important point:ML is only used to perform prediction. In data science, there are many tasks that do not require ML. So ML is just a tool that may be used or not.

Let's go back to the topic of Data science in Mechanical engineering.
In the manufacturing,metallurgy, oil and gas, automotive, biomedical, and defense industries, From designing to selling, about 90% of tasks can be automated using Data science. Data science is able to predict machine faults before downtime,make designs suitably, and track the performance of factories. That's why companies are reducing manpower to decrease manufacturing costs. This is the reason of recessions.

There are some articles or news from forbes and companies orignal websites that support these things:

So these posts are evidence that data science is also important for mechanical perspective also.

How to learn?

Following are the topics which have to learn step wise:

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