Deep Learning-Powered Defects Detection
In manufacturing, the process of defect detection in production lines is getting smarter. Deep neural network integration allows a computerized system to recognize such surface defects like scratches, cracks, leaks, and others.
By applying image classification, object detection, and instance segmentation algorithms, data scientists train visual inspection systems to detect defects depending on the given task. In conjunction with a high optical resolution camera and GPU, a deep learning-powered detection system has better perception than traditional machine vision.
For example, Coca-Cola built the AI-based visual inspection app. The app diagnoses the facility system and detects issues. Technical specialists receive notifications about detected problems and take further actions.
Predictive Maintenance With ML
Instead of fixing failure when it happens or scheduling equipment inspections, it is better to predict problems before they occur. By utilizing time-series data, machine learning algorithms fine-tunes the Predictive Maintenance system to analyze failure patterns and predict possible issues.
When sensors track such parameters like moisture, temperature, or density, these data are collected and processed by a machine learning algorithm. There are several machine learning models that are able to predict equipment failure.
Depending on a goal of prediction: remaining time before failure, getting failure probabilities or anomalies, there are several machine learning development approaches:
Regression models for the prediction of the Remaining Useful Life (RUL).
By utilizing historical and static data, this method allows predicting how many days left before a failure.
Classification models for prediction of a failure within a predefined time span.
In order to define how soon the machine will fail, we can develop a model that will forecast failures within a defined number of days.
Anomaly detection models to flag devices. This approach allows predicting failures by identifying differences between normal system behavior and failure events.
Key benefits given by machine learning-based predictive maintenance is accuracy and promptness. By revealing anomalies in production appliances, analyzing their nature and frequency, it’s possible to optimize performance before the failure happens.
ML-Powered Digital Twins
A digital twin is a virtual copy of a physical production system. In the manufacturing area, there are digital twins of specific machinery assets, entire machinery systems, or particular system components. The most common uses of digital twins are real-time diagnostic and evaluation of production process, prediction and visualization of product performance, and others.
In order to teach digital twin models to understand how to optimize the physical system, data science engineers utilize supervised and unsupervised machine learning algorithms. By processing historical and unlabeled data gathered from continuous real-time monitoring, machine learning algorithms look for behavior patterns and find anomalies. These algorithms help to optimize production scheduling, quality improvements, and maintenance.
Moreover, the utilization of NLP techniques gives a possibility to process external data from research, industry reports, social networks, and mass media. It enhances digital twins’ functionality not only for designing a future product but also for simulation of its performance.
Generative Design for Smart Manufacturing
The idea of generative design is a machine learning-based generation of all possible design options for a given product. By selecting such parameters as weight, size, materials, operating, and manufacturing conditions in generative design software, engineers can generate many design solutions. Then, they can select the most suitable design for a future product and put it into production.
Here it is how General Motors use this technology:
The utilization of advanced deep learning algorithms is what makes generative design software smart. One of the new AI trends is the utilization of the Generative Adversarial Networks (GAN). GANs, in turn, use two networks: generator and discriminator. The generator network generates new designs for given products. The discriminator network classifies and differentiates between the designs of a real product and generated ones.
Thus, data scientists develop and teach deep learning models to define all possible design variations. The computer becomes a so-called “design partner” which generates unique design ideas according to those constraints given by a product designer.
ML-Based Energy Consumption Forecasting
The growth of the Industrial Internet of Things (IIoT) allows not only to automate most production processes but also to make them thrifty. By collecting historical data about temperature, humidity, lighting usage, and activity levels of the facility, it’s possible to forecast energy consumption. And that’s when machine learning and artificial intelligence assume the bulk of implementation tasks.
The idea of machine learning utilization for energy consumption management is the detection of patterns and trends. By processing historical data about consumed energy in the past, machine learning models can predict energy consumption in the future.
The most common ML approach to make a prediction for energy consumption is based on sequential data measurements. In order to do that, data scientists use autoregressive models and deep neural networks.
Autoregressive models are good for defining trends, cyclicity, irregularity, and seasonality. However, it is not always enough to apply just a single autoregression-based approach. In order to enhance forecast accuracy, data scientists use several approaches. The most common complementary approach is feature engineering which helps to transform raw data into features, thus, specifying the task for prediction algorithms.
Deep neural networks are good for processing large datasets and quick finding of patterns. They can be trained to autonomously extract features from the input data, without the necessity of feature engineering.
In order to store information of previously inputted data using the internal memory, data scientists utilize recurrent neural networks (RNN), which are good at spanning patterns over long sequences. Having loops, RNNs can read the input data and simultaneously transmit it across neurons. This helps to understand time dependencies, define patterns in past observations, and link them to a future forecast. Moreover, RNN can dynamically learn to define what input information is valuable and rapidly change the context if necessary.
Thus, by utilizing machine learning and artificial intelligence, manufacturers can estimate energy bills, understand how energy is being consumed, and make the optimization process more data-driven.
AI and ML-Driven Cognitive Supply Chain
When realizing how rapidly the volume of data is growing along with the Internet of Things, it’s clear that smart supply chains are just a matter of choosing the right solution.
Artificial intelligence and machine learning make supply chain management not only automated but cognitive. Supply chain management systems based on machine learning algorithms can automatically analyze such data as material inventory, inbound shipments, work-in-processes, market trends, consumer sentiments, and weather forecasts. Therefore, they are able to define optimal solutions and make data-driven decisions.
The whole cognitive supply chain management system may involve the following functions:
Demand forecasting. By applying time series analysis, feature engineering, and NLP techniques, machine learning forecasting models analyze customer behavior patterns and trends. Thus, manufacturers can design new products, optimize logistic and manufacturing processes, relying on a data-driven forecast.
The demand forecasting system used by Adidas is a good example of how machine learning algorithms can influence customer experience. By analyzing trends in buying behavior and involving consumers in product design, the company optimized manufacturing and delivery processes significantly.
Transportation optimization. Utilization of machine learning and deep learning algorithms allows to assess shipments and deliverables and determine what impacts their performance.
Logistics route optimization. Generic ML algorithms review all possible routes and define the fastest one.
Warehouse control. A deep learning-based computer vision system detects shortages and excesses of stock, therefore, optimizing timely replenishment.
The example of a smart inventory management system is computer vision-based tracking technology integrated by Tyson Foods company. By utilizing edge computing, cameras, and machine learning algorithms, the system tracks the quantity of chicken passing through the supply chain.
Human resources planning. When a machine learning algorithm gathers and processes production data, it can show how many employees would be required to perform certain tasks.
Supply chain security. Machine learning algorithms analyze data about requested information: who, where, and what information was needed and assess risk factors. Thus, the cognitive supply chain ensures data privacy and prevents hacks.
End-to-end transparency. Advanced analytics based on machine learning processes data received from IoT devices. ML algorithm finds hidden interconnections between multiple processes within the supply chain and identifies weaknesses that require an immediate response. Thus, everyone involved in supply chain operations can request the required information if necessary.
PwC created a report about the future of ML technologies in the manufacturing sector. Here it is how machine learning-driven technologies are expected to grow over the next five years:
Source: Digital Factories 2020: Shaping the future of manufacturing
In conclusion, I would highlight that AI and machine learning won’t bring immediate success once integrated. The main point is that any innovative technology should solve existing business problems, but not imaginary ones.