A Machine Learning (ML) Model is an algorithm based on relevant data to identify patterns in the input data and determine its correlation with output data.This enables diverse applications in prediction, reasoning, and enabling human decisions in the manufacturing environment.
Machine Learning Models are chiefly classified into two categories – supervised learning and unsupervised learning. Both these learning models are further divided into different sub-categories. However, a preliminary understanding of the difference between the two is sufficient to further study them in detail.
Supervised learning is possible when the training data contains both the input and output values known as labeled data. This process is generally carried out under supervision. For instance, the data feed for an average “age-IQ” correlation. The inputs must contain values of both ages and respective IQs. The input (21,115), will instruct that the average IQ value of a 21-year-old is 115. This data is stored and analyzed. With an increasing number of inputs, the algorithm gradually tries to identify or formulate a pattern. The pattern is then used to predict the output for a given input. Supervised learning is further classified into two groups – regression and classification.
Unsupervised learning is where the machine works on its own by drawing patterns and inferences for data that only has input values. It handles unlabeled data. The two main methods of unsupervised learning are clustering and dimensionality reduction.
Machine Learning solutions are popular in the product development phase, largely because of the large amount of data that can be leveraged from the planning and improvement stages. These solutions can help gather customer data, analyze it, and even identify trends and patterns that can lead to substantial business opportunities. ML solutions help mitigate risks associated with product development. The insights extracted from the planning stage help make informed decisions along the cycle. ML combined with digital twins can also reduce the need for expensive prototyping, testing and validation investments. More importantly, it can crash the time-to-market delivering significant early-to-market advantages.
Sensor technologies are helping redefine benchmarks in manufacture. A study shows that when put to good use, ML solutions can help improve quality of a product by as much as 35%. There are primarily two ways in which this can be done. First is by reducing defects, and secondly, by improving the quality of the manufacturing process. Another study shows that ML can offer as much as 90% improvement in defect detection compared to conventional methods.
