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Artificial intelligence is a technological concept that allows digital solutions to go far beyond the execution of predetermined algorithms. As for AI in the manufacturing industry, this business niche is mostly standardized and based on a completely transparent logic. So how can AI development services be useful here? Let’s find out about AI in the manufacturing market below.
Until recently, the main line of development in manufacturing was the use of end-to-end automation systems. Companies massively implemented solutions based on powerful and distributed computing facilities that controlled the entire production cycle. Thus, the integration of production and computing systems ensured the flexibility of technological processes and the possibility of quickly changing the types of products. However, the rapid development of artificial intelligence systems has opened up new horizons for the development of manufacturing and created perfect opportunities for building automation systems of a fundamentally new level.
For example, “non-intelligent” solutions use predefined business logic and precise calculations in their work, based on a given model of production processes. As for AI, this concept adapts systems to real conditions and performs the assigned tasks even when the control goals change, and unforeseen changes in the properties of the controlled object or environmental parameters appear. Thus, AI in manufacturing can independently change the control algorithm and look for optimal and efficient solutions after analyzing the conditions.
Formally, we can distinguish the following characteristics of intelligent systems that distinguish them from non-intelligent ones:
AI in manufacturing can be applied in almost all business areas and at all levels:
To learn more about the usage of artificial intelligence in the manufacturing industry, read our article about AI in automotive.
AI in the manufacturing market covers many more narrowly focused intelligent technologies. This includes machine learning, robotic machines, computer vision, etc. Depending on the purpose, each of these methods can be used in manufacturing. Let’s look at the use cases for AI in more detail.
The robotics used in manufacturing is focused on performing highly specialized tasks. While many modern robots can handle their tasks without the implementation of these complex technologies, the introduction of artificial intelligence significantly boosts the level of automation and makes them even more versatile. Thus, artificial intelligence in this context helps to reduce the cost of production, increase the speed of its manufacturing, and reduce the risks associated with the human factor.
In particular, such robots are already being used today in some factories in Japan for conveyor processing of materials, assembling parts, and testing already assembled models.
Another option for AI-assisted robotization is pipelined software deployment. Here we talk about software, which, like robots, automates the processes associated with the implementation of industrial software on local equipment.
Although this technology concept is less specialized for manufacturing, it is still important in optimizing business processes. For instance, the use of such robots eliminates the incorrect transfer or leakage of data, as well as the need to hire local specialists for individual branches of the enterprise.
Machine learning is an artificial intelligence technique where an algorithm learns from training data to make decisions and recognize patterns in real collected data. It can be used for the optimization of pipelines.
As one AI in manufacturing examples, let’s consider the case of predicting the output characteristics for the current conditions when smelting metals. This production process requires the accumulation of previously obtained data. To achieve the desired quality, it may also be needed to determine the initial composition of the alloy and the melting parameters. Machine learning in such situations provides continuous production, allowing companies to reduce raw material costs, optimize the composition of elements, ensure the quality of the output product, and more efficiently manage the smelting process.
Neural networks can also be used here. Using artificial “neurons,” they receive input data in the input layer. This input is passed to the hidden layer, which assigns a weight to the input and sends it to the output layer.
Thus, the machine accepts a sufficiently large number of input data sets, each of which is associated with certain scenarios. The ML-based machine finds patterns in the input information, thanks to which, in the future, it gets the opportunity to predict the consequences of certain events, evaluate various hypothetical scenarios, and make optimal decisions after analyzing alternative options.
The main difference between non-intelligent systems and the ones based on neural networks is the absence of the need for manual entry of processing rules. After processing the training samples, such systems become digital experts in their subject area. At the same time, such systems need the means of tight knowledge control that can resolve possible contradictions, eliminate redundancy, and generalize concepts. This is where operator assistance may be needed.
Computer vision is a set of technologies that allows machines to not process images as a data array but to interpret them in a human-like way. Computer vision becomes more and more popular in manufacturing, allowing companies to automate and significantly improve processes that require visual control.
As one of the use cases, let’s consider a conveyor with auto parts, where it is necessary to quickly detect visual defects during product quality control. The main task is to localize and classify these defects using deep learning, one of the machine learning techniques where the software emulates the human brain just like a neural network. In this case, information is transferred from one level to another for more accurate processing. To train such neural networks, ready-made training samples are needed, which are characterized by the completeness and consistency of the input data.
Thus, based on general ideas about product quality, over and over again, such AI solutions generate new output data suitable for defect analysis.
You can find out more about AI in manufacturing and other business sectors (such as healthcare, banking, and retail) in this article.
Artificial intelligence can also be used in natural language processing (NLP) chatbots to optimize the processing of requests from enterprise employees. This concept lies at the intersection of machine learning and mathematical linguistics and is aimed at studying the methods of analysis and synthesis of natural language.
When implemented correctly, enterprise employees can use such chatbots to report problems, manage hardware, and generate reports. Thus, imitating human speech, AI chatbots occupy an intermediate link between managers, equipment operators, and machines, making their communication and interaction more productive and fast.
Artificial intelligence enables companies to reduce equipment uptime, minimize excess inventory, predict wear and tear on equipment, manage excess waste, and address energy waste through environmental awareness.
To solve these problems, experts very often implement solutions based on soft computing—a set of tools and methods for tasks of high complexity, designed to process incomplete information, for which there are no rigorous approaches to obtain an accurate result in an acceptable time. In particular, they work well in situations that cannot be optimally resolved by traditional conventional approaches. Thus, soft computing makes it possible to find a non-optimal, but good enough solution in manufacturing.
Most companies that have used machine learning and AI in manufacturing face a lack of data sooner or later. Therefore, they need to supplement the available information with the results of real or virtual experiments using engineering analysis technologies based on the simulation of physical processes. At the same time, the created model must correspond to the actual operating conditions and be constantly updated with information about the operating object. This is what digital twins are for.
A digital twin is a complex dynamic model that reproduces in real-time and with high accuracy the state and parameters of equipment and process operation under existing conditions.
Since it’s impossible to immediately identify all failures on a working model, it’s necessary to correctly model what-if conditions to predict and identify the most critical aspects of security and business as a whole. Therefore, such use cases provide end-to-end decision support and recommendations for operators using machine learning algorithms based on both historical and simulated data.
You can also read about AI use cases in cybersecurity and healthcare.
Manufacturing is a well-established business sector where cost reduction and process optimization are among the top priorities. Of course, AI provides new opportunities to achieve these goals, working much more efficiently than many other digital concepts, but it also has its drawbacks.
The first challenge is the rather high cost of implementing and maintaining AI solutions. As a rule, this requires almost a complete replacement of equipment, which a priori cannot be cheap.
Another key problem with using AI in manufacturing is the cost of error. In particular, the incorrect operation of the machine or the incorrect assessment of the situation can cost the life and health of people. That’s why the introduction of AI into enterprises must be gradual, with the involvement of industry experts who are equally well versed in their niche and in the technologies that are used to implement AI solutions. Usually, finding such personnel is a rather difficult task, so sometimes companies have to hire experts from other countries.
The next challenge is the insufficient quantity and quality of data. Unlike many other industries, historical data is rarely stored in manufacturing and is not used for analytics, so creating training samples can also take a lot of time and money. Thus, the global digitalization of manufacturing companies is always accompanied by huge investments, especially when it comes to heavy industry.
And finally, another problem lies in solving common problems related to the optimization of multi-stage pipelines, since to build a logical chain of communication between several separate algorithms, an intelligent machine can not only spend a lot of time but also make errors.
Fully autonomous production is yet to come. However, artificial intelligence systems can be an auxiliary process control solution for operators, technologists, and engineers, helping them make more informed decisions and maximize their productivity.
Also, artificial intelligence helps to reduce equipment downtime with early warning of potential failures and problems. For example, an engine breakdown prediction system accurately detects breakdowns nine out of ten times, allowing companies to reduce downtime and maintenance time for their equipment.
What’s more, the ability to remotely monitor and speed up the troubleshooting process improves workforce efficiency, while condition-based repairs optimize maintenance schedules and increase profits.
Thus, artificial intelligence ensures the improvement of work processes in all aspects of manufacturing activity.
We hope you now know a little bit more about the use of AI in manufacturing.
As you can see, AI in manufacturing can solve problems that people cannot cope with due to their physiology. This may be working in hard-to-reach places, in chemical production, in permafrost conditions, or with high levels of radiation. Secondly, such solutions are optimal where human intelligence is subjective or unable to cope with colossal flows of unstructured data—for example, in predicting critical faults, preventing sudden equipment failure, maintenance, etc. Thus, this technological concept has enormous prospects for further development in this industry due to its accuracy, high efficiency, and narrow focus.
If you are interested in implementing your business idea based on AI in manufacturing, please contact us. We always provide the best conditions for partnership and excellent quality of created digital solutions.
An AI Solutions Consultant with more than 10 years of experience in business consulting for the software development industry. He always follows tech trends and applies the most efficient ones in the software production process. Finding himself in the Data Science world, Evgeniy realized that this is exactly where the cutting-edge AI solutions are being adopted and optimized for business issues solving. In his work, he mostly focuses on the process of business automation and software products development, business analysis and consulting.
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