HOW MACHINE LEARNING HELPS OPERATORS PROACTIVELY MAINTAIN TRIM QUALITY.

For years, Edge Tech has been helping strip processing facilities to see the quality of their trim while a coil is still in production. Today, the solution has experienced a revival through the implementation of Machine Learning. The Edge Tech of today requires little to no attention from an operator when quality goals are satisfied.

This technology pivot comes from the ability to teach Edge Tech which defects are critical, resulting in reduced response time, greater control over quality, and improved consistency.

In manufacturing, where Machine Learning has the greatest benefit, the term is still a nuanced concept. To understand how Edge Tech can reduce operator intervention, we must first identify how the term is applied.

Categories of Machine learning. From Wikipedia:

Early classifications for machine learning approaches sometimes divided them into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system. These were: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.

Edge Tech uses the method of Supervised learning to identify defects. From Wikipedia:

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Types of supervised learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

Edge Tech takes training data to develop the algorithms to give our machine learning better output. We supply Edge Tech with a basic set of algorithms that will allow each plant location to collect a set of samples to use as training data for the system. As new defects are identified and samples are collected, Supervised Machine Learning is used to add the defect to the detection algorithm. With a system in place to identify defects as they develop, operators are able to be more proactive in their approach to quality control. As an added benefit, safety is improved by limiting operator intervention to only when necessary.