AI in Manufacturing Industry
Published on : Thursday 10-02-2022
The manufacturing business now has access to previously unthinkable amounts of sensory data in a variety of formats, says Ravi Saini.
Manufacturing is one of the primary industries that makes full use of Artificial intelligence and Machine Learning technology. Smart Factories, also known as Smart Factories 4.0, have significantly reduced unplanned downtime and enhanced product design, as well as increased efficiency and transition times, overall product quality, and worker safety. Artificial intelligence (AI) is at the heart of Industry 4.0, delivering increased production while remaining environmentally conscious.
Manufacturing AI is already being heavily invested in by industrial heavyweights such as Siemens, GE, Fanuc, Kuka, Bosch, Microsoft, and NVIDIA, among others. Smart Manufacturing (a combination of industrial AI and IoT) is expected to grow dramatically in the next three to five years, according to TrendForce. The global smart manufacturing market will be worth more than USD320 billion by 2020, with a compound annual growth rate of 12.5 per cent.
Intelligent maintenance
Maintenance of equipment is one of the largest expenses in the manufacturing industry, costing plants and factories about USD50 billion in unplanned downtime, 42 per cent of which was due to asset failure.
As a result, predictive maintenance has emerged as a critical option that can assist save a significant amount of money. Complex AI algorithms such as neural networks and machine learning are producing reliable forecasts about the condition of assets and machines. The equipment's Remaining Useful Life (RUL) extends dramatically. If something has to be repaired or changed, experts will know ahead of time and will even know which procedures to utilise.
Optimised product development
The approach of putting a detailed brief developed by humans into an AI algorithm is known as generative design. Different elements, such as available production resources, budget, and time, can be included in the brief. The algorithm looks at all of the possibilities and comes up with a few best options. Pre-trained deep learning models can analyse this set of solutions, adding extra insights and selecting specific possibilities. You can repeat this process as many times as necessary until you find the ideal one. Unlike humans, artificial intelligence is fully impartial, with no unproven assumptions.
Improving the quality
In today's environment of tight deadlines and increasing product complexity, meeting the highest quality standards and regulations becomes even more difficult. Customers demand flawless goods. Furthermore, product faults might result in recalls, which can severely harm the company's and brand's reputation. Companies can use AI to detect faults in the production process that could lead to quality difficulties. These flaws could be significant or minor, but they all have an impact on overall performance and can be removed early on.
Computer vision, for example, is an AI solution that uses high-resolution cameras to detect faults far more effectively than a human. It could be used in conjunction with a Cloud-based data processing platform to produce an automated response. Manufacturers can also collect data on their products' performance when they first hit the market, allowing them to make better strategic decisions in the future.
The German firm Siemens believes that its prior experience with industrial AI for manufacturing has already aided the technology's development and deployment. They've been using neural networks to monitor and improve the operation of steel paints for decades. They've spent more than USD10 billion on software acquisitions in the last ten years.
Siemens' gas turbines incorporate artificial intelligence based on neural networks. Over 500 sensors track a variety of characteristics, and the system learns and adjusts fuel values for the most economical operation.
General Electric, one of the world’s largest corporations, manufactures everything from household products to vast industrial machines. They have over 500 plants around the world, but they are only now starting to make them intelligent.
Brilliant Manufacturing Suite is GE's attempt to track and process everything in every step of manufacturing in order to identify any potential problems or failures. Thanks to this innovation, their first Brilliant Factory in India attracted USD200 million in investments and increased the facility's effectiveness rate by 18%. The Brilliant Production Suite from GE intends to integrate all aspects of manufacturing, such as design, engineering, and distribution, into a scalable worldwide smart system. It even has its own Industrial Internet of Things platform, called Predix.
Japanese firm Fanuc uses artificial intelligence to make robots smarter. In fact, by incorporating deep learning into robots, it has become a leader in industrial robotics. The FANUC Intelligent Edge Link and Drive (FIELD), an IoT platform for the industrial industry, was developed in collaboration with Rockwell and Cisco. The collaboration with NVIDIA resulted in the use of Fanuc's AI processors in future plants. Some industrial robots can now teach themselves thanks to the use of deep reinforcement learning. FANUC and NVIDIA want to make it possible for several robots to learn at the same time. It will be faster for each robot to learn if they can study together.
Smart facilities are creating industrial intelligence that benefits an entire enterprise, thanks to cutting-edge technology like Big Data and IoT in manufacturing. The manufacturing business now has access to previously unthinkable amounts of sensory data in a variety of formats, structures, and semantics. Deep learning garnered a lot of attention as the major advance in computational intelligence on the road from sensory data to practical manufacturing intelligence. Deep learning techniques allow humans to learn from data, discover patterns, and make judgments automatically. Predictive analytics, prescriptive analytics, diagnostic analytics, and descriptive analytics are examples of different degrees of data analytics.
Ravi Saini is a Senior AI Engineer at Ignitarium Technology Solutions Private Limited. With a Masters in Robotics and Artificial Intelligence and close to 7 years of experience under his belt, Ravi has developed and build various prototypes and products for military, medical, retail, gaming, etc. His professional experience includes proficiency in data science, machine learning and deep learning skills for multiple applications including Computer Vision and Natural Language Processing. Ravi has worked in China for 3 years followed by client side working in Hong Kong and Vietnam, before returning home due to Covid-19.
