These are the Latest AI Trends in Manufacturing
AI is the ‘go to’ for most of manufacturing companies these days. It's such a game-changer that it boasts the potential to digitally transform both manufacturing operations as well as business processes within a manufacturing organization. No wonder the buzz words like 'Industry 4.0' and 'Digital Transformation', is trending in the industry. According to a research by ‘MarketsandMarkets Research Private Ltd’, the value of AI in the manufacturing market was estimated to be around $1.1 billion last year and is most likely to hit $16.7 billion by 2026. We are talking about a whopping CAGR of 57.2 percent in the forecast period.
In terms of manufacturers making use of AI, a study by 'Capegemini' points towards Europe taking the front seat of AI implementations at 51 percent, Japan falling second at 30 percent, and the US falling third at 28 percent. As the innovations of AI are ever dynamic, some of its applications have become trendy in this current era and speaking of trends, here are some of the latest AI trends that most manufacturers prefer.
Deep Learning- Defect Detention:
In manufacturing, defects are a recurring phenomenon. But with the help of deep learning, such cases can be avoided in a smart, efficient manner. Through deep neural network integration, a computerized system takes notice of surface cracks, leaks, scratches and similar damages. Data scientists spot those defects through image, classification, object detection and instance segmentation algorithms. In comparison to the traditional computer vision, deep-learning powered detention devices draw far better outcomes. An example of such usage of methods can be seen in the 'AI-based visual inspection app' by Coca-Cola, where the app examines the facility system and identifies issues and would then notify technical experts about the detected issue.
Digital Twin- Digital Replica:
Like the term suggests, it is simply a virtual replica of the original or real-time products and assets. These allow manufacturers to gain a better understanding of the product and also allow experimentation of the product to enhance performance. Here, engineers through supervised and unsupervised Machine-Learning (ML) algorithms make digital twin models to comprehend to optimize the physical systems. This trend is taking the spotlight as Deloitte’s study indicates its growth at 38 percent CAGR to reach $16 billion by 2023. Also, its presence comes quite handy in production development, design customization, predictive maintenance and so on. Kaeser, a leading manufacturer of compressed air goods, is one example that used ‘Digital Twins’ to allow the company to move from selling products to selling services.
Generative Design- Smart Manufacturing:
This makes for a smart manufacturing that mimics an engineer’s approach to design through ML algorithms. So all that engineers or designers have to do is, select parameters like weight, size, strength, material, operating, costs and manufacturing conditions in generative design software, where the software produces all possible outcomes in accordance to those selected parameters. Similar to the concept of multiple solutions to a problem, likewise, manufacturers can generate thousands of design options for one product. Manufacturers can ease a little with designing ideas, since the computer becomes the designer here. For instance, Bugatti, A French automobile manufacturer, had put Siemens NX to test using generative design software; they produced a concept for the 1,500 horsepower Chiron supercar's wing control mechanism. The component was printed in titanium and carbon fiber, and the entire process resulted in an integrated assembly with a weight savings of over 50 percent.
ML-Based Energy Consumption Forecasting:
The concept behind using machine learning to control energy use is to spot patterns and trends. Machine learning models can forecast future energy consumption by analyzing historical data on energy consumption in the past. The most popular machine learning method for predicting energy consumption is to use sequential data measurements. Data scientists use autoregressive models and deep neural networks to achieve this. Economic, practical and technical among others are benefits that come with this forecasting.
Industrial robots, also known as manufacturing robots, automate routine activities, reducing or eliminating human error, and allowing humans to concentrate on more efficient aspects of the process. Robots can be used in a variety of ways in plants. Assembly, welding, painting, product testing, picking and putting, die casting, drilling, glass making, and grinding are some of the applications. An industrial robot can track its own accuracy and efficiency and train itself to improve with the addition of artificial intelligence. Machine vision is a feature on some manufacturing robots that allows them to move precisely in complex and random environments. Robotic welding, assembly, painting, material removal are few examples of ‘Robotics’ in the automation manufacturing industries.
The emergence of service-based revenue models to supplement the current product-based models has resulted from the digitalization of manufacturing across industries. In both the B2C and B2B segments, digital innovations have started to usher in an age of customization at a much lower cost. Global brands like Adidas and Nike have begun to shift their manufacturing centers away from low-cost countries and closer to the market in recent years.
Furthermore, utilizing modernized systems that can be reconfigured quickly; a focus is now being put on developing an agile and scalable production process. Lower costs, enhanced efficiencies, increased yield, mass customization, and, most significantly, new sales and business models are now possible aspects that companies can experience. Digital technologies are increasingly being customized to serve needs across multiple industries with their application being witnessed most often in the automotive, healthcare, aerospace, and defense, chemical, and consumer goods sectors.