How Artificial Intelligence is Revolutionizing the Manufacturing World
By Devashish Shrestha
Manufacturing has undergone major transformations in style, scale, technique and efficiency in recent years. Despite significant progress however, much remains to be done to streamline operations, enhance production efficiency, reduce downtime and resource consumption, grow customer satisfaction, and increase the bottom line. Artificial Intelligence (AI) may be the solution. How?
Industry 4.0
One of the biggest transformations in manufacturing is the rise of Industry 4.0, named for the fourth industrial revolution. A key component of Industry 4.0 is the smart factory: a highly interconnected, optimized, and autonomous system that takes automation to the next level and requires little to no human interference. Another component is the digital twin — a visual model of any physical process or entity that provides real time data about its physical counterpart. Digital twins make manufacturing more dynamic by giving manufacturers the ability to “experiment with parameters and explore ideas for further optimization, without the risk of harming performance or damaging equipment” (“How Factory 4.0 is Transforming Production”).
Generative Design
AI systems are also used to optimize the design process. Specifically generative design incorporates input from engineers — for instance, material parameters, cost limitations, and methods — into its software to develop design alternatives. The system then chooses the optimal design using machine learning (ML) mechanisms. The process resembles a natural selection of designs and has applications in industries such as automotive, aerospace, and architecture, to name just a few.
Predictive Maintenance
AI is also used to reduce downtime using an ML application called predictive maintenance. According to the International Society of Automation, downtime results in a 5–20% loss of manufacturing capacity. Predictive maintenance works by evaluating the possibility of an equipment failure, preventing disruption of operations and wasted time and money. The article “Predictive Maintenance: Fixing a Machinery Before Breakdown with AI” says that predictive maintenance: “eliminates (the) guesswork as the machines (continuously) report their conditions” in real time, a task that can be cumbersome even for experienced engineers. It also increases productivity and reduces cost: according to McKinsey, “AI-driven predictive maintenance can increase asset productivity by up to 20 percent and reduce maintenance costs by up to 10 percent.” These systems also allow manufacturers to adjust process parameters. If a malfunctioning product is detected, the machine automatically removes it from production. This has a significant impact on productivity: the article “Artificial Intelligence is Transforming Manufacturing” states “quality testing can increase productivity by up to 50 percent and increase defect detection rates by up to 90 percent in comparison to processes based on human inspection.”
Supply Chain Management
AI assists supply chain management by predicting demand on resources. Such data includes historical and, as mentioned in the article, “Supply Chain Optimization: Finding the Best Mix Across the Supply Chain,” “environmental data and recent trends to predict optimal resource needs at each stage of production.” Complex supply chains consist “of thousands of diverse components” where “delays, breakdowns or mistakes can shut down a product assembly point.” AI allows manufacturers to “better predict the complex interactions between … production units and automate requests for parts, labor, tools and repairs.”
The models mentioned above are capable of performing numerous important tasks: spot irregularities in resource use, identify domains in need of additional inspection, “determine desirable inventory levels … update resource plans, reroute inventory … streamline resource requirements to reduce downtime, reduce costs, increase production speed and increase profits from manufacturing operations” as mentioned by the article above. Statistics from McKinsey suggests that using AI for supply chain optimization helps “companies reduce forecasting errors by 20 to 50 percent to optimize stock replenishment … reduce lost sales due to stock‑outs by up to 65 percent and reduce inventory by as much as 20 to 50 percent.”
Robots
Robots are a longtime friend of manufacturing and are used in assembly lines to perform manual and repetitive tasks. These are known as industrial robots and require huge financial capita. Another subgroup of robots are cobots or collaborative robots. These robots work with humans and are able to adapt to environmental changes without prior programming. In addition to their flexibility, cobots are small and lightweight. Compared to industrial robots, they are easier to program and less expensive, making them a cost effective option for companies whose budget doesn’t allow for an army of industrial robots. Additionally, robots perform jobs that may be dangerous for humans. Because most manufacturing tasks tend to be repetitive, they often induce “boredom and could lead to injuries because of the inattentiveness of (…) workers.” Robots are immune to boredom.
Product Optimization
Production optimization aims to optimize parameters that control production in order to determine the combination that will lead to maximum efficiency. It is achieved through operators whose skill and experience level determine how well it is performed. While operators might be able to do their job when the number of process parameters is limited, their task becomes impossible when the number goes beyond a certain limit. Machine learning algorithms learn by analyzing vast quantities of historical data from sensors and form “complex relations between various parameters and their effect on production.” These models predict production rates based on parameters that can be adjusted during the process. ML algorithms suggest ways of achieving optimum production rates by determining the control variables that need to be adjusted and by what amount. Such algorithms “run on real-time data streaming from the production facility (and) provid(e) recommendations to the operators when it identifies a potential for improved production.”
Articles from the piece:
How AI Builds A Better Manufacturing Process
Artificial intelligence in manufacturing: Optimization of additives consumption
Predictive Maintenance: Fixing a Machinery Before Breakdown with AI
Artificial Intelligence is Transforming Manufacturing,
Supply Chain Optimization: Finding the Best Mix Across the Supply Chain
4 Ways Artificial Intelligence Will Impact Manufacturing
7 Ways Artificial Intelligence is Positively Impacting Manufacturing
Robots in Manufacturing Applications.
Advantages of Artificial Intelligence on Manufacturing Industry
How to Use Machine Learning for Production Optimization
How Factory 4.0 is Transforming Production
Keep reading:
What Generative Design Is and Why It’s the Future of Manufacturing
Machine Learning Techniques for Predictive Maintenance
What is Industry 4.0? Here’s A Super Easy Explanation For Anyone
