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The importance of AI industrial in the manufacturing

The importance of AI industrial in the manufacturing

How will AI industrial in manufacturing  be used?

The factory of the future is intuitive, smart, and full of sensors. All because of AI industrial in manufacturing. Learn how manufacturing AI industrial is being used and why it is important to the factories of the future.

A fully automated factory was like a fantasy that can be seen a lot in sci-fi movies. It's almost unmanned and powered only by AI industrial  that directs robotic production lines. This is not the only way to use AI industrial  in manufacturing within a realistic plan.

The practical use of AI industrial in manufacturing seems closer to a collection of applications for small discrete systems that manage specific manufacturing processes. Operating with some degree of autonomy, it will respond to external events in an increasingly intelligent and human-like way, from worn out tools to natural disasters such as system outages and fires.

What is AI industrial  in Manufacturing ?

In manufacturing, AI industrial refers to the intelligence that allows machines to perform human-like tasks. It responds to events that occur inside and outside, and even anticipates the events that will occur. Machines can detect worn out tools, unexpected events, or even events that were expected to occur, and react and solve problems.

Historians track human progress by measuring evolutionary progress based on human proficiency in natural environments, materials, tools, and techniques, from the Stone Age to the Bronze Age and Iron Age. Humanity is currently in the Information Age , also called the Silicon Age . In this age of electronic technology, humans have the synergistic ability to use computers to collectively improve, wield unprecedented powers over nature, and accomplish things that were unimaginable a few generations ago.

As computers increasingly move toward doing things that only humans could traditionally do, AI industrial  has evolved naturally. People have options about how to apply machine learning and AI industrial. Helping creative people do more is one of the things AI industrial is good at. You don't have to replace people. Helping people with ideal applications to do what they are particularly good at. In manufacturing, this can be an operation such as making a part in a factory or designing a product or part.

Gradually, this becomes a human-robot collaboration. Despite the widespread public image of autonomous and “smart” industrial robots, most robots require a lot of maintenance. But robots are getting smarter through AI industrial  innovation, which is also making human-robot collaboration safer and more efficient.

How has AI industrial  evolved in manufacturing ?

Most of the AI industrial  ​​in manufacturing today is related to surveying, nondestructive testing (NDT) and other process tools. AI industrial  assists in product design, but adoption of AI industrial  in manufacturing is still in its infancy. Machine tools are still relatively simple. Automated workshop tooling is on the news, but many factories around the world continue to rely on outdated equipment, often mechanical and with limited digital interfaces.

Modern manufacturing systems utilize screens. Human-computer interfaces and electronic sensors provide feedback on raw material supply, system status, power consumption and various other factors. Work in progress can be visualized on a computer screen or machine. Future developments are becoming evident, as are the various scenarios for how AI industrial  can be used in manufacturing.

Short-term scenarios include monitoring the machining process in real time and monitoring status inputs such as tool wear. These applications fall under the category of 'predictive maintenance'. This is a clear opportunity for AI industrial . It's an algorithm that uses a continuous stream of data from sensors to find meaningful patterns, applies analytics to predict problems, and informs maintenance teams to fix problems before they occur. Sensors inside the machine can monitor what is happening. This could be an acoustic sensor that can hear the belt or equipment start to wear, or it could be a sensor that monitors the wear of tools. Such information is then linked to an analytic model that can predict how much of a tool's life is left.

Additive manufacturing on the manufacturing floor is becoming important, and it allows the addition of many new types of sensors to systems, monitoring new conditions affecting materials and manufacturing techniques that have been introduced in large numbers over the past decade.

Manufacturing AI industrial Status

AI industrial uses digital twins to diagnose and solve problems when defects occur in the manufacturing process, as well as enable much more accurate manufacturing process design. A digital twin is a virtual model identical to a real part, a machine tool, or a part being manufactured, surpassing a CAD model. An accurate digital representation of a part, showing how it behaves when, for example, a part fails. (All parts have defects, and therefore fail.) AI industrial  is essential for applying digital twins to manufacturing process design and maintenance.

Large enterprises can reap many benefits from adopting AI industrial , and they have the financial strength to invest in these innovations. But small businesses, such as contract designers and manufacturers that supply to technology-intensive industries like aerospace, have applied and invested in only some of the most creative.

Many small and medium-sized enterprises (SMEs) are trying to outperform larger competitors by rapidly introducing new machines or technologies. Providing these services differentiates in the manufacturing space, but in some cases introduces new tools or processes without the necessary knowledge or experience. It may be true from a design-oriented perspective or a manufacturing-oriented perspective, but it is difficult to jump into additive manufacturing. In this situation, small and medium-sized enterprises (SMEs) can benefit more from AI industrial  adoption than large enterprises. This is because smart systems that can provide feedback and assist with installation and operation can give emerging companies a foothold in innovation in the marketplace.

Fundamentally, it is possible to integrate the engineering expertise of the entire process into the manufacturing process. In other words, it is possible to implement tooling using on-board AI industrial  with the knowledge to guide installation, adoption, sensors, and analysis that can detect operational and maintenance issues. (These analyzes are likely to include so-called 'unsupervised models' that are trained to find the feedback patterns of sensors for unknown problems, looking for strange or 'wrong' aspects that need investigation .)

A real-life example of this concept is the Digital Reconfigurable Additive Manufacturing facilities for Aerospace ( DRAMA ), a £14.3 million ($19.4 million) collaborative research project launched in November 2017. Autodesk is one of a consortium of companies working with the Manufacturing Technology Center (MTC) to build a prototype of a “digital learning factory.” The entire additive manufacturing process chain is digitally connected. The facility will be reconfigurable to meet the needs of different users and to test different hardware and software. Developers are building an additive manufacturing “knowledge base” to support technology and process adoption.

Autodesk plays a key role in the design, simulation and optimization of DRAMA, taking full account of the downstream processes that occur in manufacturing. Understanding how the manufacturing process affects each part is critical information that enables humans to automate digital designs so that they can be performed closer to real parts and bring them into the design process through generative design .

The future of manufacturing AI industrial 

This scenario presents an opportunity to effectively package all stages of the work process for sale to manufacturers. This includes everything from software to real-world machines, digital twins of machines, ordering systems that exchange data with a factory's supply chain systems, and analytics that monitor processes and gather data as input moves through the system. Basically, it is creating a “ factory in a box” system.

Factory Mermaid Box

These systems allow manufacturers to analyze the non-destructive testing performed on each process line, allowing manufacturers to look at the parts manufactured today and compare them to those made yesterday, and verify that quality assurance has been achieved. The feedback will help you understand exactly the parameters used to make the part and then identify where the defect is in the sensor data.

The ideal vision for this process would be to put material at one end and the part to come out at the other end. After all, robots will only need humans to maintain systems that can perform many tasks. But in the current concept, people still design and make decisions, oversee manufacturing and work on multiple lines. The system helps them understand the real impact of the decisions they make.

Machine Learning and Autonomous AI

Much of the power of AI industrial comes from machine learning, neural networks, deep learning, and other self-organizing systems capabilities that come from their own experiences without human intervention. These systems can quickly discover specific patterns in vast amounts of data that exceed human analytical capabilities. However, in today's manufacturing, much of the development of AI industrial  applications is still overseen by human experts, encoding the expertise of the previous systems they designed. Human experts bring an idea of ​​what happened, what went wrong and what went well.

Ultimately, autonomous AI industrial  builds on this expertise, so if, for example, AI industrial  is analyzing data from on-board sensors for preventive maintenance and process improvement, new hires in additive manufacturing will benefit from operational feedback. can This is an intermediate step towards innovations such as automatic straightening machines. As tools wear, the system recommends replacement of worn components while adjusting to maintain performance.

Factory planning and layout optimization

The application of AI industrial  is not limited to the manufacturing process. From a plant planning perspective, equipment layout is determined by many factors, from operator safety to process flow efficiency. Equipment may need to be reconfigurable to accommodate continuous short-term project operations or frequently changing processes.

Frequent changes can lead to unexpected space and material collisions, and can cause problems of efficiency or safety. But sensors can be used to track and measure these collisions, and AI industrial  can play a role in optimizing factory layouts.

Capture data from sensors for real-time AI analysis

When applying a new technology with high uncertainty, such as additive manufacturing, an important step is to conduct non-destructive testing after the part is completed. Non-destructive testing can be expensive, especially if it incorporates capital equipment CT scanners (used to analyze the structural integrity of manufactured parts). A machine's sensors can be linked to models built from large data sets learned during the manufacturing of specific parts. When sensor data becomes available, it can be used to build machine learning models. For example, it can correlate with defects found on CT scans. Sensor data can mark parts that analytical models suggest to be defective without the need for a CT scan of the part. Instead of routinely scanning every part coming out of a production line, only those parts will be scanned.

Operations can also monitor how people use the equipment. When manufacturing engineers design equipment, they make assumptions about how the machine will be used. In the case of human analysis, additional steps may occur or steps may be omitted. On the other hand, sensors can accurately capture this information for AI industrial analysis.

AI industrial  also serves to introduce manufacturing processes and tooling that can be adapted to different environmental conditions. An example is humidity. Additive manufacturing technology developers have discovered that some equipment does not work as designed in certain countries. Humidity sensors in the factory were used to monitor conditions, sometimes making unexpected discoveries. In one case, humidity problems occurred in an environment where moisture had to be controlled because someone left the door open and went out to smoke.

In order to efficiently utilize sensor data, it is necessary to develop an efficient AI industrial  model. The model must be trained to understand what the data presents: what could be causing the problem, how to detect it, and what action to take. Today's machine learning models can use sensor data to predict when a problem will occur and notify a human solver. Ultimately, AI industrial  systems will be able to predict and react to problems in real time. AI industrial  models will soon be tasked with creating proactive ways to isolate problems and improve manufacturing processes.

generative design

AI industrial plays an important role in generative design, the process by which design engineers enter a set of conditions into a project and then design software generates multiple iterations . Recently, Autodesk has collected vast amounts of material data from additive manufacturing and is using it to drive generative design models. This prototype provides an “understanding” of how material properties change as the manufacturing process affects individual functions and structures.

Generative design is a tunable optimization technique. Many existing optimization techniques look at part optimization as a more general approach. Generative design algorithms can be much more specific as they focus on individual functions by applying an understanding of the mechanics of that function based on material testing and collaboration with universities. Unlike design in an ideal world, the manufacturing process takes place in the real world, so conditions may not be constant. An effective generative design algorithm includes this level of understanding.

Generative design can generate optimal designs and specifications in software and distribute those designs to multiple facilities with compatible tooling. That means more parts can be produced in smaller, geographically dispersed facilities. These facilities can be close to where they are needed. One facility can make parts for aerospace one day, and parts for other essential products the next day, reducing distribution and shipping costs. This is becoming an important concept in fields such as the automobile industry.

Flexible and reconfigurable process and shop floor

AI industrial  can also be leveraged to optimize manufacturing processes and make these processes more fluid and reconfigurable . Recent demands can determine the shop floor layout and create processes, and do the same for future demands. And you can use these models to compare and contrast. This analysis then determines whether it is better to have fewer large stacking machines or many smaller machines, which will cut costs and be diverted to other projects if demand slows. “What-if” analysis is a common application of AI industrial .

Models are used to optimize plant layouts and process sequences. For example, a 3D printer may heat-treat a laminated part directly, which may require the material to be pre-tempered or another heat cycle to be re-forged. Engineers can run a variety of hypothetical scenarios to determine what equipment a facility should have. It may make more sense to subcontract parts of the process to other nearby companies.

This application of AI industrial  could change the way factories do business, deciding whether they focus on one in-house process or work on multiple products or projects. The latter case makes the plant more resilient. For aerospace, an industry that is experiencing a downturn, manufacturing operations may be geared towards making medical components.

Manufacturing and AI industrial , Applications and Benefits

Designing, improving processes, reducing machine wear and optimizing energy consumption are all areas where AI industrial  will be applied in manufacturing. This change has already begun.

Machines are getting smarter, integrating with each other as well as with supply chains and other industrial automation. An ideal situation would be to have sensors monitor the connections of all chains of processes, from material entering to component being built. People retain control of this process, but are not required to work in the environment. This frees up valuable manufacturing resources and people to focus on innovations that create new component designs and manufacturing methods instead of repetitive tasks that can be automated.

As with any fundamental change, there has been resistance to AI industrial   adoption. The knowledge and capabilities required for AI industrial  can be expensive or lacking, and many manufacturers don't have their own. They believe they have strengths in their specialization, so they need exhaustive evidence to justify an investment to create something new or improve a process, and they may be afraid to improve their plant.

Therefore, the concept of 'factory in a box' is more attractive to the company. More enterprises, especially small and medium-sized enterprises (SMEs), can confidently adopt full-length integration processes where software works seamlessly with tooling and enhances them with sensors and analytics. Adding a digital twin that allows engineers to simulate new manufacturing processes also reduces the risk of such decisions.

Another key area of ​​focus in AI industrial  manufacturing is predictive maintenance . This allows engineers to equip factory machines with pre-trained AI industrial models that incorporate their cumulative knowledge of tooling. Based on the machine's data, the model can learn new cause and effect patterns found in the field to prevent problems.

AI industrial  also plays a role in quality inspection , a process that is inherently suitable for machine learning by generating large amounts of data . In Additive Manufacturing, for example, one build generates terabytes of data about how a machine made a part, the field conditions, and any issues found during the build operation. This amount of data is beyond the scope of human analysis, but it is now possible in AI industrial systems. Suitable for additive tools can be readily used in cutting, casting, injection molding and a variety of other manufacturing processes.

When complementary technologies such as virtual reality (VR) and augmented reality (AR) are added, AI industrial  solutions will reduce design time and optimize assembly line processes. Production line workers are already equipped with VR and AR systems that visualize the assembly process and provide visual guidance to increase the speed and precision of the job. Operators can also wear AR glasses that project diagrams showing how the parts are assembled. The system can monitor the operation and also provide prompts such as 'this spanner turned enough', 'not turned enough', 'brake not pulled', etc.

SMEs and large enterprises focus on different areas of AI industrial  adoption. Small businesses tend to manufacture many parts, while large corporations often assemble parts procured from other sources. There are exceptions. Automakers do a lot of spot welding of chassis, but they buy and assemble other parts, such as bearings and plastic parts.

A new trend in the component itself is the use of smart components that monitor the component's own state, stress, torque, etc. with built-in sensors . This is particularly groundbreaking for automobile manufacturing, as this factor has more to do with how the car drives than how far it travels. If you drive a lot on bumps every day, it will require more maintenance.

A smart component can tell you if a part has reached the end of its life or if it is time to inspect it. Instead of externally monitoring these data items, the part itself occasionally registers with the AI industrial  ​​system to report normal conditions and when anomalies occur and the part needs attention. This approach reduces the amount of data traffic within the system, which can significantly degrade the analytical processing power.

The biggest and most immediate opportunity for AI industrial to add value is in additive manufacturing. Lamination processes are a prime target because the products are more expensive and have a smaller volume. As humans grow and mature AI industrial, in the future, AI industrial  will become important across all areas of manufacturing value.