Top 5 Use Cases of Artificial Intelligence in Manufacturing
Top 5 Use Cases of Artificial Intelligence in Manufacturing
In this article, let's take a look at various use cases of computer vision at different stages of manufacturing.
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The manufacturing industry has been going undergoing a major digital transformation. The traditional model is evolving to be known as Industry 4.0. With rapid developments in different areas including imaging techniques; CMOS sensors; embedded vision; machine and deep learning; robot interfaces; data transmission standards and image processing capabilities, computer vision technology can benefit the manufacturing industry at different levels. New imaging techniques have provided new application opportunities. Developments in computer vision technology has led to enhanced performance, integration and automation in manufacturing industry. Let's deep dive to understand various use cases of computer vision at different stages of manufacturing, but before that let's understand what is a computer vision?
It is a field of Artificial Intelligence and Computer Science that aims to provide computers a visual understanding of the world. The goal of Computer Vision is to emulate human vision using digital images through three main processing components:
1. Image acquisition
2. Image processing
3. Image analysis
Now, let's understand its top 5 applications in the manufacturing industry.
Predictive maintenance is a method of preventing the failure of expensive manufacturing equipment, by analysing data throughout production to pinpoint unusual behaviour ahead of time, to ensure appropriate measures can be taken to avoid extended periods of production downtime.
Better predictive maintenance using IoT can reduce equipment downtime by up to 50 percent and reduce equipment capital investment by 3 to 5 percent…In manufacturing, these savings have a potential economic impact of nearly $630 billion per year in 2025.- McKinsey
Ever imagined if you can get an alert from a mobile app ahead of a fault which is going to occur. Sound interesting right? Thanks to predictive maintenance, it tells us when to replace the part, reducing planned downtime and keeping the product running for an optimum amount of time. This helps to eliminate unnecessary repair costs.
Predictive Maintenance Technologies
There are various monitoring devices and techniques which can be employed for effectively predicting failures, as well as providing advanced warning for maintenance on the horizon. Let’s understand the types:
- Vibration Analysis — It allows to monitor a machine’s vibrations by using a handheld analyser or real-time sensors built into the equipment, its ability to predict potential failures makes it a useful tool to plan maintenance, boost asset performance, which helps to prevent unscheduled downtime.
- Infrared Thermography — It helps in detecting high temperature (Hot spots) with the use of IR cameras. By identifying hotspots infrared can help avoid costly repairs and downtime. The Federal Energy Management Program (FEMP) says a savings of 30% to 40% is possible for equipment that is only served by a reactive maintenance program.
- Ultrasonic Analysis — This technique uses sound to identify failing assets. It can be used for leak detection, mechanical inspection, electrical Inspection, electric arc flash detection, steam trap maintenance, and valve testing.
- Acoustic Monitoring — this technology helps to detect gas, liquid or vacuum leaks in equipment on a sonic or ultrasonic level. These are comparatively less expensive than the ultrasonic.
Reading Barcodes: A barcode is a machine-readable pattern applied to products, packages, or parts. They are used for informational and for tracking products throughout their lifecycle. Identifying and processing thousands of barcodes is one tedious task to be performed by humans, It requires placing the scanner operator near the barcode in order to obtain correct result. However, with computer vision scanning, product passing on a conveyor belt does not necessarily need to be aligned for the camera-based scanner to correctly detect the barcode. Smart industries are incorporating OCR (optical character recognition) technology to make the information in an image machine-readable and usable. Several technologies, like barcode recognition (OBR), intelligent character recognition (ICR), and optical mark recognition (OMR) to expand the functionalities of existing.
- OCR is used to recognize text from scanned documents or screenshots.
- ICR is used to read text from hand-written forms, e.g. questionnaires
- OMR is used to recognize check boxes in surveys or forms
- OBR is used to recognize traditional 1D and 2D barcodes for automatically routing parts through the production line
Defect Identification: It is quite a cumbersome task for any manufacturing company to physically count the massive number of goods & products. Computer vision provides real-time analysis of information derived from captured images to perform complex inspection tasks. It features a counting mechanism system which helps to validate, if each container contains the correct number of items in it. If the total number of items isn’t correct, or if a single container has been flagged as defective and if the container reaches the end of the production line, any containers that contains any defective pieces are rejected. This helps to remove the risk of packing and shipping any defective product.
Assembling products and components: Computer vision ensures that the product that assembly of product and components are strictly adhering to standards. The stringent assessment criteria reduce instances of product recalls as well as improves productivity. Foe ex: A dairy products manufacturing company which produces tonnes of dairy products can leverage computer vision techniques to ensure correct packaging. It also helps to examine other critical features of packaged bottles like cap seal, position, label, and much more.
Machine and Deep Learning: There are an unimaginable amount of sensory data which contains multiple formats, structures, and semantics. Deep learning techniques enables people to automatically learn from these data, detect patterns, and make decisions accordingly. It enables to distinguish different levels of data analytics, including predictive analytics, prescriptive analytics, diagnostic analytics, and descriptive. Here’s how they are being used in the manufacturing industries-
- Predictive analytics uses statistical models to make forecasts about the possibilities of future production and equipment degradation.
- Prescriptive analytics offers multiple scenarios to perform any action.
- Diagnostic analytics is aimed at reporting the reason for the equipment failure.
- Descriptive analytics helps to analyse operational parameters, environment, and conditions of the product.
Onwards to Industry 4.0
Clearly, computer vision is set to take the manufacturing industry by storm. The changing face of manufacturing and distribution has led to the emergence of smart products and innovative manufacturing models. Automation in the form of image and voice recognition is set to increase levels of productivity and accuracy. Smart Factories are experiencing major cuts in unexpected downtime and better design of products, improved efficiency, transition times, overall product quality, and worker safety.
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