Applications of Machine Learning & AI in Mechanical Engineering

This is part two of a two-part series on Machine Learning in mechanical engineering. You can find the first part here.

 

AI is at the core of the Industry 4.0 revolution. AI algorithms can optimize production floors, manufacturing supply chains; predict plant/unit failures, and much more. For instance, in 2018, AI helped in reducing supply chain forecasting errors by 50%. Moreover, using ML-based quality testing is increasing defect detection rates by 90%. 

 

 

In the near future, most human-intensive tasks will be accomplished by machines. Therefore, it becomes essential for mechanical engineers to up-skill themselves and get acquainted with the technology.

 

Applications of AI & ML in Manufacturing 

 

Manufacturers are always keen to adopt technology that improves product quality, reduces time-to-market, and is scalable across their units. Artificial Intelligence, Machine Learning, and Robotic Process Automation are helping manufacturers fine-tune product quality and optimize operation. 

 

These are the 3 most common applications of AI and ML. 

 

 

 

Predicting Mechanical Failure 

By continuously monitoring data (power plant, manufacturing unit operations) and providing them to smart decision support systems, manufacturers can predict the probability of failure. Predictive maintenance is an emerging field in industrial applications that helps in determining the condition of in-service equipment to estimate the optimum time of maintenance. 

 

ML-based predictive maintenance saves cost and time on routine or preventive maintenance. Apart from industrial applications, predicting mechanical failure is also beneficial for industries like the airline industry. Airlines need to be extremely efficient in operations and delays of even a few minutes can result in heavy penalties. Situations like delays in taxing will result in severe fines for airlines, the primary reason for taxing delays results from aeroplanes experiencing mechanical failures or environmental situations that result in cascading delays. This is directly related to sequential data. For making sense of sequential data, we can use machine learning models to predict such events. 

 

 

AI for Automatically Segmenting Brain Tumors 

Artificial Intelligence has a broad scope in healthcare devices and applications. It can make analysis, treatment, and monitoring of tumors more effective. For example, NVIDIA has developed a 3D MRI brain tumor segmentation using deep-learning and 3D magnetic resonance imaging technologies. 

 

 

Source: NVIDIA 

“Automated segmentation of 3D brain tumors can save physicians time and provide an accurate reproducible solution for further tumor analysis and monitoring.” 

Andriy Myronenko, Senior Research Scientist, NVIDIA

 

 

In the above image, the first row is the real-life data that contains the image of a tumor identified by an expert physician. The bottom row contains the images of the brain with tumors detected by a computer. 

Such applications of AI in healthcare can make good health facilities cost-effective and help them reach remote places where there is a lack of trained physicians or technicians.  

 

 

Reducing Test and Calibration Time 

Data science-based analytics can help manufacturers with the prediction of calibration and test results to reduce the testing time while production. 

For example – Bosch, a German multinational engineering and technology company used AI techniques like early prediction from process parameters, descriptive analytics for root-cause analysis, and component failures prediction to avoid unscheduled machine downtimes and achieved 35% reduction in test and calibration time. 

 

 

The increasing demand of AI Engineers 

Manufacturers have been using distributed and supervisory control systems to improve process efficiencies in their plants. However, it requires rigorous monitoring and relies on the experience, intuition, and judgment of the operator. 

AI is capable of improving and standardizing the knowledge and experience of experts to make decision support systems effective. Industries are keen on developing in-house AI capabilities and that’s why the demand for mechanical engineers with knowledge of AI is rapidly increasing. Currently, organizations are looking out for process and automation engineers, data scientists, IT & Data engineers and AI creation experts from mechanical and electronics background. 

 

 

Source: McKinsey & Company 

 

 

Students who are trained in mechanical engineering and have an understanding of Machine Learning are valued in companies across the world. These are students - employees who do not need to be trained to understand the intricacies of a Navier-stokes equation nor will they need to be given a crash course in supervised and unsupervised learning. The demand for such students is always high, and Skill-Lync ensures that our students meet the grueling demands of the industry. 

 

Course link - Machine Learning Fundamentals in Depth

 

 

 

Important Terminologies Related to AI and ML 

 

Types of Data 

Data is any relevant information that is available related to the application you’re building using ML. Usually, we categorize the data into two sets – one, which is used to train the ML model; and two, which we use to test if the algorithm (ML model) is working fine or not. 

1. Training Data: This data set is a sample data set that comprises input and/or output values for training the ML model. 

2. Validation Data: The validation data is the set of sample data kept aside to test the effectiveness of the algorithm/ML model. It gives an unbiased estimate of the model’s skills and is required for comparing/selecting between final models. 

3. Test Data: It is used to evaluate the final model without any biases. The terms- validation data and test data are often used interchangeably. 

 

 

Fundamental Techniques of Machine Learning 

There are three fundamental techniques of Machine learning – structured, unstructured, and reinforced learning. 

1. Structured: Structured learning is suitable when we are aware of both – inputs and outcomes. 

2. Unstructured: This type of learning is useful for complex problems where we don’t know what the right answer is. It tries to figure out what the input is by studying the input values. This ML model requires an enormous amount of input data before devising an algorithm to solve a given problem. 

3. Reinforcement learning: Whenever there are consequences to the inaccurate outcomes, reinforced learning is used. It penalizes the wrong outcome and rewards the correct solution. This type of machine learning is useful for designing driverless cars. 

 

 

Quality of Prediction 

After training a machine, we need to determine its effectiveness based on the quality of the predictions it makes.  

 

1. Overfitting: When the ML model tries to predict the outputs for a given set of inputs in a very vigorous way, in other words - it is biased to the input and gives incorrect output for even a slight variation in the input value, it is known as overfitting

2. Underfitting: It is a situation when an application can neither model the training data nor generalize to new data. It is mainly due to inefficient algorithms. The only remedy to underfitting is trying alternative machine learning algorithms. 

 

 

 

Frequently Asked AI and ML Questions from Mechanical Engineers 

 

How much RAM is required to run ML? 

We cannot answer this exactly without knowing the problem. But, usually, for some of the common applications of AI in Mechanical Engineering, 16 GB RAM should be sufficient. 

 

Is AI a threat to people working in Manufacturing? 

There is a hype surrounding AI and its potential threat to industrial and manufacturing jobs. AI is capable of automating routine tasks and making them more efficient and productive. It can take over some of the jobs at the grassroots level but will create new opportunities like research analysts, data scientists, AI engineers, ML engineers, Mechatronics, etc. 

 

How are ML and AI different from IoT? 

IoT (Internet of Things) corresponds to a system of interconnected devices (both mechanical and digital). The devices in IoT are capable of communicating and transferring data over a network without the need for human-computer interaction. 

AI and ML are the technologies that make machines capable of making human-like decisions. In the long run, AI and ML can add a layer (functionality) to make IoT devices more interactive and user-friendly. 

 

Does AI deal with MPI? 

MPI is basically a message passing interface. It is a library that helps to perform parallel calculations. For example, if you have a GPU with 100 cores, you can actually use those 100 cores to do computations using AI. 

 

Can we use AI and ML in CAE? 

CAE stands for Computer-aided Engineering. ML algorithms can be highly beneficial for predicting mid-surface problems in CAE applications. 

 

Is Python used in AI and ML? 

Python is a high-level, general-purpose programming language. It has an extensive library ecosystem, which makes it most popular for AI and ML applications. 

 

Why can't we use if/else or for loops instead of complex AI? 

For problems with a limited scope, coding logic works. But, for intensive data sets, real-time predictions, and designing solutions for complex problems, AI is beneficial. 

 

Upskilling with AI and ML can help Engineers gain a competitive advantage in this fast-changing digital world. Skill Lync has introduced AI and ML courses specifically designed for Mechanical Engineers to help them pursue careers as Data Scientists, AI & ML Engineers.  

 

 

Course link - https://skill-lync.com/courses/ml-ai-mechanical-engineers 

 

 

This blog has been written based on the webinar - Fundamentals of AI-ML conducted by Sarang, co-founder of Skill-Lync.
You can view both parts of the webinar here -