Artificial intelligence (AI) has the power to utterly transform manufacturing. There are many potential benefits, including boosted productivity, lower costs, improved quality, and less downtime. Large factories stand to gain a lot, but small businesses should also realize that high-value, affordable AI is within reach.
There are numerous ways AI can be applied in manufacturing. Complex image processing enables AI to automatically detect defects across diverse industrial objects.
What is AI in Manufacturing?
With industrial IoT and smart factories generating huge amounts of data daily, AI has many uses in manufacturing. More and more, manufacturers are leveraging AI solutions like machine learning (ML) and deep neural networks to better analyze data and make decisions.
Predictive maintenance is often mentioned as one AI application in manufacturing. Analyzing production data with AI improves failure prediction and maintenance planning, resulting in less costly upkeep.
Many more applications and advantages of AI in manufacturing exist, like more precise demand forecasting and less material waste. AI and manufacturing are a natural fit since close human-machine collaboration is crucial in industrial environments.
Types of automation in digital manufacturing
Fixed Automation
Fixed automation, also known as hard automation, refers to a system that performs a single specific task and is difficult to reconfigure for different product styles. It is characterized by high production rates and a high initial investment cost. Programming is built into machines using cams, gears, wiring, and other hardware.
Fixed automation is well-suited for mass-produced products, such as the assembly lines used in automotive manufacturing, automatic assembly machines, and certain chemical processes. While changeovers are possible, they require shutting down the production line and manually changing out the tooling.
Programmable Automation
Programmable automation allows manufacturers to create products in varying quantities, from a small number to a large number of units per batch. This type of automation necessitates reprogramming and altering equipment for each new batch, resulting in downtime.
Programmable automation typically operates at a slower production rate compared to fixed automation due to its focus on enabling easy product changeovers. The high cost of downtime has led to the development of flexible automation, which builds on programmable automation.
Flexible Automation
The key drawback of programmable automation is downtime during equipment changeover for new products. Flexible automation addresses this issue by automating changeovers. However, it can limit equipment to running parts that share similar tools or require additional devices for changeovers. This means that flexible automation supports a much smaller variety of products to ensure quick automatic changeovers.
Flexible automation is frequently linked to a network, offering benefits such as the ability to remotely operate production and the elimination of batch production, thereby enabling the simultaneous production of various products.
Key AI Segments That Impact Manufacturing
AI refers collectively to learning system capabilities that are perceived as representing intelligence, including image and video recognition, predictive modeling, smart automation, advanced simulation, and complex analytics, among others, according to Cap Gemini. In manufacturing processes, AI use cases focus on the following technologies:
- Machine learning: Using algorithms and data to automatically learn from underlying patterns without explicit programming.
- Deep learning: A subset of machine learning that utilizes neural networks to analyze things like images and videos.
- Autonomous objects: AI agents that manage tasks independently, such as collaborative robots or connected vehicles.
AI for manufacturing is expected to grow from $1.1 billion in 2020 to $16.7 billion by 2026, an astonishing compound annual growth rate of 57 percent. The increase can be mainly credited to the presence of large amounts of data, rising levels of industrial automation, enhanced computational capabilities, and greater financial investments.
What are the Applications of AI in the Manufacturing Sector?
Artificial intelligence (AI) has many applications in manufacturing. To start, AI can be trained by observing how humans perform tasks. With enough practice, it can learn to independently execute a wide range of duties without constant oversight.
Next, crowdsourcing can be used to amass data from the public to train AI. It can quickly process this information and compare it to existing knowledge. This results in an AI with access to collective wisdom and the ability to utilize shared knowledge.
Additionally, unsupervised learning allows AI to gain knowledge without explicit instruction. It does this through reinforcement learning, acquiring new information through experience.
AI has several roles in manufacturing
It can predict when equipment might fail so maintenance can be scheduled before breakdowns occur. Generative design uses machine learning to mimic engineers’ design process, rapidly generating numerous options. AI software can forecast commodity prices more accurately than humans, improving over time. Edge analytics utilizes sensor data for quick, decentralized insights.
For quality control, AI systems can identify abnormalities using machine vision since flaws are often visually obvious. Industrial robots automate repetitive tasks, minimizing human error and freeing up workers. AI optimization improves processes to achieve consistent production. Digital twins track the production cycle to identify potential quality issues or performance shortfalls.
In summary, AI can be trained to perform tasks, utilize collective knowledge, and learn independently. It plays roles in predicting failures, creative design, forecasting, analytics, quality control, robotics, process improvements, and shop floor monitoring. AI enhances manufacturing through this wide range of applications.
AI in Manufacturing
Artificial intelligence is having a major impact on manufacturing. AI is improving the production process in factories in various ways.
AI in Logistics
Keeping the right amount of inventory is an ongoing challenge. Having too much stock leads to waste and lower profits. Having too little means missing out on potential sales and revenue. AI can help optimize inventory levels.
AI Robots
Also called “industrial robots”, robotics in manufacturing enables automating repetitive tasks, reducing human error, and freeing up workers for higher-value activities. Robots have many uses in factories. Some incorporate machine vision to navigate chaotic environments.
AI for Supply Chain Management
AI helps factories manage their entire supply chain, from forecasting capacity to taking stock. By building a real-time, predictive model of suppliers, issues can be spotted instantly and impact assessed.
AI Self-Driving Vehicles
Self-driving vehicles can automate transport inside and outside factories. Autonomous trucks and ships enable 24/7 deliveries. Connected vehicles with sensors that track traffic and road conditions can optimize routes, cut accidents, and alert authorities. This improves delivery speed and safety.
AI for Factory Automation
Factory operators manually adjust equipment while monitoring indicators – a complex, skill-based task. Operators must now handle their regular work plus troubleshooting. This discourages optimizing returns on investment.
AI for IT operations (AIOps) is key here. AIOps leverages big data and machine learning to automate the management of IT operations. It is especially useful for extensive data management. Other applications include IT service management, event correlation, performance analysis, anomaly detection, and root cause analysis.
AI for Design and Production
AI software can generate multiple design iterations that improve on the original. It asks for inputs like: key ingredients, measurements, production techniques, budget limitations. Then algorithms generate various feasible layouts.
AI and the Internet of Things
Internet of Things (IoT) devices in factories produce huge real-time operational data. Combining AI and IoT can greatly enhance manufacturing precision and output.
AI for Warehouse Management
AI can automate various warehouse tasks – tracking stock, optimizing logistics. With continuous data, AI oversees inventory and quality control. This cuts costs, boosts productivity and reduces staff needs. Ultimately manufacturers can increase revenue and profits.
AI Process Automation
AI-powered process mining tools can find and eliminate inefficiencies to sustain high production rates. In manufacturing, meeting customer needs often requires prompt, accurate delivery.
AI for Predictive Maintenance
AI analyzes sensor data to anticipate equipment failures and accidents. This allows scheduling preventive repairs and maintenance in advance, improving productivity and reducing costs.
AI for Product Development
Using AR and VR, manufacturers can test product models before production with AI. This should simplify maintenance and debugging. AI-enabled product development can enhance and accelerate innovation, leading to new, more advanced products that beat competitors to market.
AI for Connected Factories
The future of manufacturing lies in sensor-equipped, cloud-connected “smart” factories. Adopting smart manufacturing helps: monitor the shop floor in real-time; track resource utilization; enable remote, hands-free systems; and allow timely interventions.
AI for Quality Inspections
AI-driven flaw detection uses high-res cameras to observe every production step. This can catch issues missed by the human eye and trigger corrective actions, reducing waste and product recalls, while improving safety.
AI for Procurement Costs
Input price changes significantly impact manufacturer profits. Estimating raw material costs and selecting vendors are key challenges. AI simplifies managing all procurement data in one place and tracking purchases across vendors.
AI Order Management
Effective order management requires flexibility as markets, demand, consumer expectations and manufacturing strategies shift. AI-based systems/robots can: generate purchase orders instantly based on inventory sensors; handle many different order types from multiple sales channels; streamline and clarify order and inventory management.
AI for Cybersecurity
Manufacturers lose the most to cyberattacks as even brief downtime can be disastrous. With proliferating IoT devices, risks will rise exponentially. AI-driven cybersecurity systems and risk detection algorithms make securing industrial facilities easier.
Select the Optimal Program
Realize the potential of AI and ML through Simplilearn’s all-inclusive programs. Choose the appropriate AI ML program to become an expert in cutting-edge technologies and propel your profession forward.
Conclusion
To benefit from ai in manufacturing, integrating AI promptly is vital. However, doing so necessitates a huge investment of time, hard work, and resources, plus upskilling your employees. Completing pilot projects to rapidly scale up and transition out of the pilot stage is essential. For those who still need to do so, the window of opportunity to incorporate AI into production processes is closing.
AI is now central to manufacturing, and it’s expanding annually. Skillsets are still scarce, so there is value in educating AI engineers who can build practical applications utilizing a variety of intelligent agents; machine learning experts trained in supervised and unsupervised learning, mathematical and heuristic techniques and hands-on modeling; and deep learning specialists who learn to master TensorFlow, the open-source software library intended to conduct machine learning and deep neural network research.
I’m Krishanth Sam, and I have 2 years of experience in digital marketing. Here, I’m sharing about Artificial Intelligence. You are get some of information about this interesting field here. Also, I will helps you to learn the Artificial Intelligence, deep learning, and machine learning.