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August 23, 2022
Knowledge-based work is being automated in the Fourth Industrial Revolution; by developing new methods of automating jobs in manufacturing, we may redesign how humans and machines live, work, and interact to construct a more robust digital economy.
The AspenTech 2020 Industrial AI Research found that 83% of big industrial firms think AI generates superior outcomes, but just 20% have used it.
Artificial intelligence will not be widely used in the industrial sector without significant investment in acquiring domain knowledge. Together, they make up what is known as "Industrial AI," which is the application of machine learning techniques to traditionally non-academic settings.
Artificial Intelligence (AI) in production is the ability of computers to think and behave like humans, including recognizing and reacting to external and internal stimuli and even forecasting future occurrences. The machinery can identify when a tool is worn out, when something unexpected occurs, or even when something anticipated occurs, and then respond appropriately.
Historians classify periods of human history from the Stone Age through the Iron Age and beyond depending on the degree to which their populations mastered new tools, materials, and technologies. This era of human history is often referred to as the Information Age or the Silicon Age. Nowadays, thanks to computers and other electronic devices, people can work together to exert significant control over the natural world and achieve results that would have been unthinkable even a few decades ago.
AI has emerged as a logical consequence of the increasing capability of computers to do tasks that people have traditionally performed for themselves. It is up to individuals to decide how best to use AI and machine learning. AI's strength lies in its ability to free up people's imaginations to pursue other goals. In manufacturing, for example, this may mean creating a component in the factory or developing a product or part that no machine could ever hope to replicate. However, this technology does not replace humans; the best applications assist people in accomplishing what they are particularly excellent at.
It's becoming more and more about teamwork between people and robots. Most industrial robots need a lot of human oversight, despite the widespread belief that they are self-sufficient and "smart." However, advancements in AI are making them smarter, leading to human-robot teams that are both safer and more productive.
Most current applications of AI in manufacturing are in measuring and nondestructive testing (NDT). While AI is already helping with product design, its use in manufacturing is in its infancy. However, machine intelligence has not progressed much. Even while automated shop tooling is making headlines, many of the world's manufacturers still use outdated machinery that can only be operated manually or with a restricted digital interface.
The displays, human-computer interfaces, and electronic sensors in modern manufacturing systems provide back information on a wide range of aspects, including the availability of raw materials, the health of the system, and the amount of energy used. In any case, the operator may picture the outcome of their actions on the screen or in the physical machine. The future is becoming more transparent, as are the many potential applications of artificial intelligence in manufacturing.
Monitoring the machining process in real-time and tracking status inputs like tool wear are two examples of more immediate use cases. Predictive maintenance is a broad category that includes several kinds of programs. It's a natural fit for AI: Algorithms that take in data streams in real-time from sensors may use analytics to foresee issues and notify maintenance crews in time to prevent catastrophic failure. Internal machine sensors may detect activity. It may be an auditory sensor that detects when the belts or gears are beginning to wear out, or it may be a sensor that keeps tabs on how well the tool is holding up. An analytical model tied to this data might then estimate the remaining useful life of the instrument.
By 2035, AI technologies are expected to boost production by 40%. This will further economic growth by an average of 1.7% across different industries.
Artificial intelligence (AI) enables far more accurate manufacturing process design, as well as issue diagnostics and resolution when problems emerge throughout the production process utilizing a digital twin. Simply put, a digital twin is a digital representation of a physical component, machine tool, or manufactured item. It's not just a CAD model by any means. It's a digital duplicate of the physical element, complete with information about how it will react in certain situations. All components are flawed and eventually break down because of this. The use of a digital twin in the development and upkeep of a manufacturing process requires the use of AI.
Businesses with a significant employee base and a healthy budget have the most to gain from using AI solutions. In contrast, SMEs, such as contract designers or manufacturers servicing technology-intensive sectors like aerospace, has sponsored some of the most innovative applications.
Many small and medium-sized enterprises (SMEs) seek to get ahead of the competition by adopting cutting-edge technology and equipment more quickly than their bigger counterparts. However, in certain situations, companies may deploy new tools and processes without the appropriate expertise or experience, even though offering these services is a distinguishing factor in the fabrication market. This may be true from a design or production perspective, making it difficult to get into additive manufacturing. In this case, small and medium-sized businesses (SMBs) may have more to gain from embracing AI than huge corporations: The use of intelligent systems that can offer feedback and aid in setting up and operationalizing might enable a tiny startup to establish a disruptive footing in the market.
Essentially, a manufacturing process may include end-to-end engineering knowledge. That is to say, the hardware equipped with AI may be sent together with instructions for setting it up, getting everyone to use it, providing it with sensors, and running analytics to spot problems before they become catastrophic. (Among these analytics is probably the use of so-called "unsupervised models," which are conditioned to seek for weird or "wrong" portions of the sensor data to identify potential issues.
In this case, there is a chance to pitch a manufacturer on the benefits of an integrated work process. Anything from the software to the hardware in the factory to the digital twin of the machinery to the ordering system that communicates with the factory's supply-chain systems to the analytics used to keep tabs on production methods and gather data as inputs move through the system could be a part of this. Methods that amount to a "factory in a box" are developed.
A system like this would let a factory check the quality of a component manufactured today against the quality of a part created yesterday, do quality assurance checks on the products being made, and evaluate the NDT performed at each production stage. The data from the sensors would show the manufacturer precisely where the problems exist, and the feedback would help them learn exactly what parameters were utilized to build those components.
The capacity of self-organizing systems like machine learning, neural networks, deep learning, and others to automatically learn from their own experience is a significant source of AI's efficacy. These computers can find meaningful patterns in large datasets far more quickly than humans. However, human professionals are still significantly in charge of driving the development of AI applications in the industrial sector today, imprinting their knowledge from the systems they have previously developed. Expert opinions on what went wrong or well with a situation are presented.
The use of artificial intelligence extends well beyond the manufacturing stage. Consider this from a production line layout point of view. Many elements, such as worker safety and efficient process flow, influence the design of a facility's layout. The facility may need to be flexible to host a variety of short-term projects or processes with frequent iterations.
Constant shifts raise the risk of inefficient or unsafe material and spatial problems. However, sensors can monitor and quantify such disagreements, and AI has a place in designing efficient production layouts.
Using NDT after the component has been created is crucial when adopting new technologies with a lot of uncertainty, such as additive manufacturing. Capital equipment like CT scanners, often employed in nondestructive testing, adds up to a hefty price tag (used to analyze the structural integrity of manufactured parts). In-machine sensors may communicate with models constructed from a big data set gleaned throughout the production process. When sensor data is made accessible, a machine-learning model may be built to help link, for instance, a defect shown in a CT scan with the corresponding sensor data. Parts that the analytical model predicts are malfunctioning may be identified using sensor data without resorting to invasive techniques like CT scanning. Instead of scanning everything that comes off the line, just those pieces would be checked.
In addition, the operation can track who is utilizing which pieces of machinery and for what purposes. When designing manufacturing equipment, engineers must predict how it will be used. Human analysis has the potential for error because of the possibility of performing unnecessary steps or skipping necessary ones. The data is readily available for AI analysis, thanks to sensors.
Artificial intelligence will be used in manufacturing for various purposes, including design, process optimization, minimizing equipment wear, and optimizing energy use. That change has already started happening.
As time passes, robots improve in intelligence and become increasingly interconnected, both among themselves and with other areas of business automation such as the supply chain. Everything would go smoothly if materials went in and pieces came out, and sensors tracked every process step. Humans continue to oversee operations but are not required to physically do any tasks. By eliminating the need for humans to perform easily automated tasks, manufacturers may redirect their attention and resources toward innovation, such as developing novel approaches to the design and production of individual components.
There has been some pushback to using AI, as there would be with any radical change. The resources and expertise in artificial intelligence are sometimes challenging to come by, so many factories cannot produce AI products. Because they are confident in their abilities in certain areas, they may be wary of taking risks when expanding production capacity and need extensive evidence to support the decision to invest in developing something new or altering an existing process.
That's good news for businesses looking at "manufacturing in a box" solutions. More companies, particularly SMEs, may confidently embrace an end-to-end packaged process with sensors and analytics. Including digital twin functionality, where engineers may test out a new manufacturing process in a virtual environment, further reduces the risk associated with the choice.
Predictive maintenance is another critical area of emphasis for AI in manufacturing. Engineers may now integrate production machinery with artificial intelligence models that have already been trained using this accumulated information. The models may pick up on new patterns of cause and effect identified on-site based on data from the equipment.
Quality inspection is another area where AI might be useful; it produces a lot of data and is thus well-suited to machine learning. Instances of Additive Manufacturing A single build may yield as much as a terabyte of data on the machine's production process, environmental factors, and problems encountered during construction. The examination of such a large dataset was impossible for humans, but modern AI systems have made it possible. What works for additive tools may readily be adapted for use in a variety of other manufacturing processes, including but not limited to subtractive manufacturing, casting, injection molding, and many more.
In additive manufacturing, AI has the most significant potential for immediate impact. Due to the higher cost and lower output volume, additive techniques are easy pickings. As AI is developed and refined by humans, it will undoubtedly play a more significant role in the whole industrial value chain in the future. More data may be acquired from Industrial Internet of Things devices, which can be utilized by AI platforms to enhance different jobs in manufacturing as their usage, popularity, and efficiency expand.
But if the development of AI applications continues, fully automated factories, product designs created automatically with little to no human oversight, and other such things may become increasingly commonplace. To get there, though, we must maintain our pace of invention. A notion is all that is required. This may include combining technologies or finding a novel use for an existing one. Manufacturing companies may differentiate themselves from the competition by adopting new technologies.
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