by Product development
Manufacturers can use digital twins before its physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.
Design customization
Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.
Shop floor performance improvement
A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.
Logistics optimization
Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.
Generative design
Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.
Predictive maintenance
Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing the sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.
Quality assurance
Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.
Edge analytics
Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyzed data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:
- Improving production quality and yield
- Detecting early signs of deteriorating performance and risk of failure
- Tracking worker health and safety by using wearables.
Robotics
Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.
Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.
Price forecasting of raw material
The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI powered software like Kantify can predict materials prices more accurately than humans and it learn from its mistakes.
Quality Checks
Internal defects of equipment cannot be detected easily. Sometimes experts are also unable to detect the flaws in products by observing their functionality. But, AI and ML technologies can do this efficiently. Minor flaws in machinery are detected with AI.
AI in manufacturing processes improves quality control. Smart AI solutions monitor the productivity of machinery. That’s why most of the manufacturing companies using Ai automation in their manufacturing routines. AI-based tools detect defects of products on the production line.
Forecast Product Demand
Artificial intelligence systems using predictive analytics can also forecast the product demand efficiently. AI tools for manufacturing collects data from various sources. Later, based on data, tools can accurately predict the product demand.
Price Forecasts
By analyzing historical data of product prices, machine learning algorithms can forecast the price of a product. Competitive prices always offer more profits to the companies.
Predicts Equipment Failure
Manufacturers face challenges with machinery failures. A product might look perfect from the outside, but it offers low performance when we use it. It affects productivity.
It is the second most reason behind the increased demand for AI in manufacturing. Manufacturing companies are deploying AI get information of equipment damages for ensuring excellent performance.
