Manufacturers regularly face various obstacles — from unexpected machinery breakdowns to poor product delivery. However, this can be easily fixed with modern technology in place. Companies may increase operational efficiency, launch new products, and customize product designs by leveraging AI solutions. How does that work? Let’s figure that out!
AI Applications in the Manufacturing Industry
AI use in industrial facilities is gaining popularity among businesses. According to Capgemini’s research, more than half of European companies (51%) are deploying AI solutions, with Japan (30%) and the United States (28%) coming in second and third.
Hundreds of factors influence the manufacturing process. While these are difficult for humans to detect, machine learning algorithms can accurately forecast the influence of specific factors in such complicated circumstances. Machines still function below human skills in other areas involving language or emotions, limiting their acceptance. But where exactly is AI used?
1. Building Digital Twins
A digital twin is a virtual replica of a real manufacturing system. In the manufacturing industry, there are digital twins of certain equipment assets, full machinery systems, or specific system components. The most popular applications for digital twins include real-time diagnosis, tic and evaluation of manufacturing processes, prediction and visualization of product performance, and so on.
Data science engineers use supervised and unsupervised machine learning methods to educate digital twin models to improve the physical system by analyzing historical and unlabeled data from continuous real-time monitoring. These algorithms aid in the optimization of production scheduling, quality enhancements, and maintenance.
2. Generative Design
Generative design is a method where software generates some outputs to fulfill certain requirements. Designers or engineers use generative design software to investigate AI product design options by entering design goals and factors like materials, production processes, and cost limitations. The approach employs machine learning techniques to understand what works and what doesn’t with each iteration.
The program finds numerous methods to create a simple object, such as a chair. You must enter the specifications such as four legs, elevated seat, weight requirements, minimal materials, etc. Based on the input data, the solution generates a number of design possibilities and features.
3. Predictive Maintenance
Manufacturers use AI technology to analyze sensor data to identify future downtime and accidents. AI systems assist manufacturers in forecasting when or whether functioning equipment will fail, allowing maintenance and repair to be arranged prior to the breakdown. Manufacturers can enhance productivity while lowering the cost of equipment failure thanks to AI applications for predictive maintenance.
4. Assembly Line Optimization
Furthermore, by incorporating Artificial Intelligence into your IoT environment, you may generate many automation opportunities. Supervisors, for example, may be notified when equipment operators exhibit indications of weariness. When a piece of equipment fails, the system might initiate contingency planning or other reorganization actions.
5. Quality Assurance
Traditionally, quality assurance was a manual procedure that required a highly qualified engineer to ensure that electronics and microprocessors were made correctly. All of its circuits were properly set up. Modern processing techniques may now automatically assess whether an object was manufactured appropriately. This sorting may be done automatically and in real-time by putting cameras at important places around the production floor.
Optimize Your Product Development!
AI and machine learning (ML) are making significant contributions to expediting new product development — from startups to companies rushing to introduce new products. Today, Indeed, LinkedIn, and Monster have 15,400 job openings for DevOps and product development engineers using AI and machine intelligence. According to Capgemini, the connected goods industry will be between $519 billion and $685 billion this year as revenue models based on AI and machine learning become more popular. And the above are just some of the AI applications. More to come!