Machine Learning applications in Manufacturing: An Overview

AI will help Manufacturing attain its eternal goal

Manufacturing strategies have always strived to produce high quality products at a minimum cost. For many best in class companies, Manufacturing 4.0 is already demonstrating its value by enabling them reach this goal more successfully than ever, and one of the core technologies driving this new wave of ultra automation is Industrial AI and Machine Learning.

“Data has become a valuable resource”- is stale quote now. The fact is that data is cheaper than ever to capture and store. Through the use of artificial intelligence, specifically Machine Learning, manufacturers can use data to significantly impact their bottom line by greatly improving efficiency, employee safety, and product quality.

In this article, I will first discuss a couple of specific examples of applications of ML in Manufacturing, followed by a high level overview of applications of Supervised and Unsupervised ML in Manufacturing 4.0 envoirnment.

Specific Application examples

Powering Predictive Maintenance with Machine Learning

Maintenance represents a significant part of any manufacturing operation’s expenses. For this reason, Predictive Maintenance has become a common goal amongst manufacturers, drawn by its many benefits, with significant cuts in maintenance costs being one of the most compelling.

While certain manufacturers do perform Predictive Maintenance, this has traditionally
been done using SCADA systems set up with human-coded thresholds, alert rules and
configurations. This semi-manual approach doesn’t take into account the more complex dynamic behavioral patterns of the machinery, or the contextual data relating to the manufacturing process at large. For example, a sensor on a production machine may pick up a sudden rise in temperature. A static rule-based system would not take into account the fact that the machine is undergoing sterilization, and would proceed to trigger a false-positive alert.

In contrast, Machine Learning algorithms are fed OT data (from the production floor:
sensors, PLCs, historians, SCADA), IT data (contextual data: ERP, quality, MES, etc.), and
manufacturing process information describing the synchronicity between the machines and the rate of production flow.

smart-factory

In AI, the process known as “training”, enables the ML algorithms to detect anomalies and test correlations while searching for patterns across the various data feeds. The power of Machine Learning lies in its capacity to analyze very large amounts of data
in real time, and propose actionable responses to issues that may arise. The health and
behavior of every asset and system are constantly evaluated and component  deterioration is identified prior to malfunction.

Enabling Predictive Quality Analytics with Machine Learning

Preventing downtime is not the only goal that industrial AI can assist us with. The quality of output is crucial and product quality deterioration can also be predicted using Machine Learning. Knowing beforehand that the quality of products being manufactured is destined to drop prevents the wastage of raw materials and valuable production time.

Machine Learning can be split into two main techniques – Supervised and Unsupervised machine learning.

Generic use case examples

Supervised Machine Learning

In manufacturing use cases, supervised machine learning is the most commonly used
technique since it leads to a predefined target: we have the input data; we have the output data; and we’re looking to map the function that connects the two variables.
Supervised machine learning demands a high level of involvement – data input, data training, defining and choosing algorithms, data visualizations, and so on. The goal is to construct a mapping function with a level of accuracy that allows us to predict outputs when new input data is entered into the system.

Capture.JPG

Initially, the algorithm is fed from a training dataset, and by working through iterations,
continues to improve its performance as it aims to reach the defined output. The learning process is completed when the algorithm reaches an acceptable level of accuracy.

In manufacturing, one of the most powerful use cases for Machine Learning is Predictive
Maintenance, which can be performed using two Supervised Learning approaches:

Classification and Regression. These 2 approaches share the same goal: to map a relationship between the input data (from the manufacturing process) and the output data (known possible results such as part failure, overheating etc.)

• Regression
Regression is used when data exists within a range (eg. temperature, weight), which is often the case when dealing with data collected from sensors.  In manufacturing, regression can be used to calculate an estimate for the Remaining Useful Life (RUL) of an asset. This is a prediction of how many days or cycles we have before the
next component/machine/system failure.  For regression, the most commonly used machine learning algorithm is Linear Regression, being fairly quick and simple to implement, with output that is easy to interpret. An example of linear regression would be a system that predicts temperature, since temperature is a continuous value with an estimate that would be simple to train.

• Classification
When data exists in well-defined categories, Classification can be used. An example of
Classification that we’re all familiar with is the email filter algorithm that decides whether an email should be sent to our spam folder, or not. Classification is limited to a boolean value response, but can be very useful since only a small amount of data is needed to achieve a high level of accuracy.

In machine learning, common Classification algorithms include naive Bayes, logistic regression, support vector machines and Artificial Neural Networks. Predictive Maintenance makes use of multi-class classification since there are multiple possible causes for the failure of a machine or component. These are possible outcomes that
are classified as potential equipment issues, calculated using a number of variables including machine health, risk levels and possible reasons for malfunction.

Unsupervised Machine Learning

With Supervised machine learning we start off by working from an expected outcome and train the algorithm accordingly. Unsupervised learning is suitable for cases where the outcome is not yet known and we allow the algorithm to look for  patterns and relationship. The Mechanism is shown below:

Capture

• Clustering
In some cases, not only will the outcome be unknown to us, but information describing the data will also be lacking (data labels). By creating clusters of input data points that share certain attributes, a Machine Learning algorithm can discover underlying patterns. Clustering can also be used to reduce noise (irrelevant parameters within the data) when dealing with extremely large numbers of variables. Clustering patterns in sensor data can often help determine impact variables that were previously unknown/considered not significant for modeling failures or remaining useful life.

• Artificial Neural Networks
In the manufacturing sector, Artificial Neural Networks are proving to be an extremely effective Unsupervised learning tool for a variety of applications including production process simulation and Predictive Quality Analytics. The basic structure of the Artificial Neural Network is loosely based upon how the human brain processes information using its network of around 100 billion neurons, allowing for extremely complex and versatile problem solving.

A basic schematic of a feed-forward Artificial Neural Network.

Every node in one layer is connected to every node in the next. Hidden layers can be added as required, depending on the complexity of the problem. This ability to process a large number of parameters through multiple layers makes Artificial Neural Networks very suitable for the variable-rich and constantly changing processes common to manufacturing. Moreover, once properly trained, an Artificial Neural Network can demonstrate a high level of accuracy when creating predictions regarding the mechanical properties of processed products, enabling cuts in the cost of raw materials.

AI, not sensors, will revolutionize Manufacturing

The introduction of AI and Machine Learning to industry represents a sea change with many benefits that can result in advantages well beyond efficiency improvements, opening doors to new business opportunities.

Some of the direct benefits of Machine Learning in manufacturing include:

• Cost reduction through Predictive Maintenance. PdM leads to less maintenance activity,
which means lower labor costs and reduced inventory and materials wastage.
• Predicting Remaining Useful Life (RUL). Knowing more about the behavior of machines
and equipment leads to creating conditions that improve performance while maintaining machine health. Predicting RUL does away with “unpleasant surprises” that cause unplanned downtime.
• Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow.
• Improved Quality Control with actionable insights to constantly raise product quality.
• Improved Human-Robot collaboration improving employee safety conditions and
boosting overall efficiency.
• Consumer-focused manufacturing – being able to respond quickly to changes in the
market demand.


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