The Present and Future of Industrial AI in Manufacturing
Overview
By 2035, AI technologies are expected to boost production by 40%1 or more. This will further economic growth by an average of 1.7% across different industries. The significant effect that AI has and will continue to have on manufacturing goes without saying. Industrial Automation is a result of the effect of AI in manufacturing. Creating new ways to automate tasks, AI rebuilds the way humans and machines live, interact, and collaborate, leading to a more robust digital economy. Manufacturers who know how to empower their workforce via AI applications create the most significant value in the future. Besides, AI applications in manufacturing plants enable businesses to transform their transactions fully.
Explore how AI is revolutionizing the manufacturing industry and emerging as a sought-after technology for the present and the future.
How is Industrial AI Used in Manufacturing?
Supply Chain Optimization
Supply chain optimization may be defined as the tools and processes that help to improve manufacturing and distribution supply chain performance and efficiency, considering all constraints. As supply chains emerge to be super complex networks to manage, AI in manufacturing has become an indispensable tool to obtain products from production and deliver them to customers within no time. AI can help you answer questions like “What’s the best shipping route for product B?”
Through Machine Learning (ML) algorithms, manufacturers can define the optimized supply chain solution for all their products. Supply chain network optimization technologies utilize complex algorithms and analytics to balance demand and supply so that enough raw materials are acquired for manufacturing and distribution, thus fulfilling customer expectations at the highest cost-efficiency.
Manufacturers are also faced with another crucial challenge with in-house inventory management. The production line depends a lot on inventory to keep the lines fed and create products. AI and ML in manufacturing can quickly help manage the challenge of keeping the factory floor stocked with all requisite inventory. Artificial intelligence can look at component quantities and expiration dates throughout the factory floor, thus optimizing the distribution.
Predictive Maintenance
Predictive maintenance may be defined as a technique that utilizes data analysis tools and techniques to recognize anomalies in your operation and probable defects in processes and equipment so that they can be fixed before they lead to a possible failure. With the cost of machine downtime going high, AI uses data in the best possible manner, ensuring optimum operational efficiency.
AI and ML in manufacturing thus enable manufacturers to process large amounts of sensor data at the maximum speed. Manufacturers thus get an unparalleled opportunity to improve on current maintenance operations and even add predictive maintenance to ensure uninterrupted operations.
Predictive maintenance considers vast amounts of data and uses AI and predictive maintenance software to convert that data into meaningful insights and data points, thus preventing data overload. As you upgrade your current maintenance systems, be rest assured your staff has the required knowledge and tools to maintain your mission-critical assets running to deliver the highest performance. AI in predictive maintenance helps you understand what questions you need answers to, like, “Do you need to know how to avoid unplanned downtime, expedite repairs, and lower costs?”
Predictive Maintenance
Predictive maintenance may be defined as a technique that utilizes data analysis tools and techniques to recognize anomalies in your operation and probable defects in processes and equipment so that they can be fixed before they lead to a possible failure. With the cost of machine downtime going high, AI uses data in the best possible manner, ensuring optimum operational efficiency.
AI and ML in manufacturing thus enable manufacturers to process large amounts of sensor data at the maximum speed. Manufacturers thus get an unparalleled opportunity to improve on current maintenance operations and even add predictive maintenance to ensure uninterrupted operations.
Predictive maintenance considers vast amounts of data and uses AI and predictive maintenance software to convert that data into meaningful insights and data points, thus preventing data overload. As you upgrade your current maintenance systems, be rest assured your staff has the required knowledge and tools to maintain your mission-critical assets running to deliver the highest performance. AI in predictive maintenance helps you understand what questions you need answers to, like, “Do you need to know how to avoid unplanned downtime, expedite repairs, and lower costs?”
Industrial and Collaborative Robotics
Industrial Robotics may be defined as a technology that automates industrial processes. The robotics arms and industrial automatisms can operate with pieces of greater weight than collaborative robotics. Besides, their size ranges from small sizes to several meters in height. AI and ML in manufacturing have been quick at making their way into industrial robotics technology.
As AI integrates with industrial robotics technology, robots can supervise their performance and precision, indicating when maintenance is needed to prevent expensive downtime. One of such examples of AI in manufacturing is a Japanese company that executes AI to make robots smarter. Fanuc is known to be a leader in industrial robotics, integrating deep learning into robots. Fanuc’s partnership with NVIDIA led to the company using its AI chips for the Smart Factories.
Due to the application of deep reinforcement learning, industrial robots could train themselves. The companies aim to enable multiple robots to learn at the same time. AI, ML, and industrial robotics technologies in manufacturing lead to increased uptime, higher productivity, and decreased time required for programming.
Collaborative robots, employing AI and ML technology, can simultaneously be programmed for diverse tasks, learning to operate safely alongside humans. Collaborative robotics is a technology that permits an operator – repetitive, dangerous, or little ergonomic processes with no security systems and enclosures available in traditional robotics facilities. A few collaborative robots can be programmed by displaying to the robot how to do a task. The robot continues to repeat the task until it successfully performs it with a high degree of repeatability.
The generative design may be defined as putting a comprehensive brief created by humans into an AI algorithm. The information in this brief can consist of various parameters, such as present production resources, time, and budget. The algorithm analyzes all possible variations and produces a few optimal solutions. Pre-trained Deep Learning models can examine this set of solutions, thus adding more valuable insights before settling on the ideal solution. Structural optimization is the most typical use of generative design algorithms today.
Also Read : Predictive Analytics in Healthcare is the Future: Top Use Cases
This is how AI plays a crucial role in manufacturing through product designing—for instance, designing parts that offer enough strength, fatigue resistance, and rigidity with the minimum of material. Such applications are typical wherever weight is a key consideration. In addition to this, where the material is an important determinant, higher structural efficiency can significantly save from cost and sustainability perspectives.
Smart generative algorithms thus not only lead to optimal product designs and improved products but also specify how product development is done. Generative algorithms offer value at multiple points in a product’s journey from initial concept to product improvement and procurement.
Energy Management
Broadly, energy management may be defined as the proactive, systematic, and organized management of energy use in an organization or a building to meet economic and environmental needs. AI/ML in Manufacturing helps to cater to the often-ignored area of energy management. It can cut energy waste, mitigate energy costs, alleviate and speed up the use of clean, renewable energy in power grids across the globe. Your engineers can hone the planning, control, and operation of power systems leveraging AI. With artificial intelligence reducing operational costs, you can allocate more funds towards process improvement resources, resulting in higher yield and productivity.
Fault prediction has been one of the critical applications of AI in the energy sector, besides real-time maintenance and recognition of ideal maintenance schedules. The energy industry is an industry where equipment failure is common, with possibly significant consequences. AI thus, combined with the right sensors, can be helpful to monitor equipment and identify failures before they occur, saving resources, time, money, and lives. Remember, AI technologies help to offer clean and cheap energy, vital to development.
One of the AI use cases in manufacturing is Siemens. The largest industrial manufacturing company in Europe uses a neural network-based AI in their gas turbines. More than 500 sensors monitor several parameters within the system, learning and making decisions on adjusting fuel values for highly efficient performance.
AI in Manufacturing: What Lies Ahead
By 2025, smart factories will contribute $3.7 Trillion2, implying that new-age technologies such as advanced analytics, robotics, and automation will dominate the manufacturing industry. The increasing scale of AI adoption in manufacturing is no less than an evolution. You will need AI and ML experts by your side to harness the optimal benefits from a human-machine collaboration. From Natural Language Processing (NLP) to Speech Recognition and Forecasting and Optimization to Deep Learning Solutions, our AI and Machine Learning Services enable you to derive consistent value from artificial intelligence.
