The revolution in Industry 4.0 is driven by AI. Production floor and supply chain optimization, failure prediction for plants and units, and other tasks are all made possible by AI algorithms. For example, AI assisted in a 50% reduction in supply chain forecasting errors in 2018. A further benefit of utilizing ML-based quality testing is the 90% increase in defect detection rates.
The majority of now labor-intensive tasks will soon be carried out by machines. Mechanical engineers must therefore upgrade their skills and familiarize themselves with technology.
Manufacturing AI and ML Applications
Technology that enhances product quality, shortens time to market, and is scalable across all of a manufacturer's units is constantly in high demand. Manufacturers are using robotic process automation, artificial intelligence, and machine learning to improve product quality and streamline processes.
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.
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.
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.
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.
- Training Data: This data set is a sample data set that comprises input and/or output values for training the ML model.
- 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.
- 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.
- Structured: Structured learning is suitable when we are aware of both – inputs and outcomes.
- 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.
- 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.
- 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
- 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.
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