Applications of AI in Manufacturing Processes
The Rise of AI
In recent years, AI or artificial intelligence has been one of the trends which have gained immense popularity, thanks to Hollywood sci-fi movies and the digital generation. As the population is increasing every day, their demands are also increasing. Companies and manufacturers are working tirelessly to keep up with the demands to maximize their profit but this is not enough. With the increasing demand, the cost of labour and manufacturing processes are also increasing. In such a situation, the optimization of the manufacturing or production process is necessary to reduce costs. This is the area where AI systems are gaining immense popularity along with machine learning concepts.
AI or artificial intelligence essentially means developing a synthetic intelligence with imbibed reasoning and decision-making capabilities or simply a system that can automatically reason and make decisions without any kind of external human intervention.
We are living in the digital age right now where most of the systems and processes have been automated for the sake of more efficient service. Data has become the new modern fuel as most of the automated systems need data to work on. Data analysis and data science have become a strategic weapon for most of the companies and enterprises looking to stay ahead in the competition.
Application of AI systems in the commercial manufacturing processes has become very common now with the majority of the big scale firms employing AI systems in their production process. Since AI systems are automated and fully digital, they eliminate the human error factor completely. The production is also very precise and faster as compared to their human counterparts. Also, since the cost of maintenance is lower than that of labour costs, AI systems are gaining more and more preference over prevalent production processes.
Applications in the Manufacturing Processes
So, we have seen that AI systems are finding many opportunities in the manufacturing processes in various industries. They are being incorporated for a wide range of applications. Listed below are some of the major applications of AI in the manufacturing process.
- For the Purpose of 'Predictive Maintenance- We all know that the machinery and systems involved in large scale productions require periodic maintenance. The process of maintenance has evolved with time. In the olden days, the system of reactive maintenance was prevalent which meant that the pieces of machinery were repaired if they broke down. With the advancement in technical knowledge, the concept of periodic maintenance was invented. That meant the pieces of machinery received a regular weekly or monthly maintenance service to prevent any major fault occurring in the future. However, now that we possess the weapon of AI, we can use the software to predict future failures and prevent them from damaging the machines at all. Since most of the systems are automated today, the data of asset utilization can be fed into an AI system which in turn can run some calculations and give us the precise limit and time of machine failure. We can utilize this data to take preventive measures and perform maintenance beforehand to keep the production process uninterrupted. Predictive maintenance is better than reactive and periodic maintenance as it maximizes the asset life and also gives us the precise data of which machine parts require maintenance so that no extra money is spent on repairing parts that work perfectly thereby making it more economically efficient.
- Creating a Generative Design- The most important part of a manufacturing process is choosing the design of the product and the production technique. Choosing a product design is critical because there are various factors which are needed to be taken into consideration while initiating a production process. These factors include deadlines, raw material constraints, quality constraints, budget and others like these. All these factors and constraints can be fed into an AI system that can implement an algorithm to generate the most suitable solution. This allows us to consider the maximum number of probable solutions before choosing the most suitable one. We can also program the maximum and minimum limits of the finished products to get the results within the specified limit. It must be remembered that the solutions obtained are only proposed solutions. Machine learning techniques can be applied to further refine the models and finally achieve the desired product design. This process is often termed as creating a generative design. The process can be repeated as many times as we like in a little time so the room of error reduces considerably, and it enhances the productivity of the whole manufacturing process exponentially.
- Applying Quality Control through a Digital System- The fundamental part of a manufacturing process is 'quality control'. If a production process is devoid of any kind of quality control method, then the whole production will be for nothing as the consumers will reject the finished product, so quality control is essential for any manufacturing process. Generally, some experts are employed for quality control measures, but even the judgment of the finest expert consists of some human errors and therefore the decision can never be fully impartial. AI has helped by providing a suitable replacement for this problem. Computer visions can be programmed to monitor the most minute details of a manufacturing process to ensure that the finished product is entirely error-free. Moreover, the computer vision can always recognize any microscopic error which is often not noticed by the naked eye. Computer visions are a lot better than human eyes and can also detect any kind of discrepancies between the layers or minute patterns. Also, the measurements of the finished product can be fed into the AI system to ensure the precise dimensions of the output, which is generally not possible for a human to do. The data about the production process can also be taught to the AI system so that it can identify the faults in the production process itself.
- Creating Digital Twins- We are living in the age of increasing competition and rapidly declining resources. Every large scale company or firm has to be very cautious before setting up a manufacturing process because any kind of mistake in the production process can lead to massive losses both in the terms of money and resources. Sometimes even the greatest of plans can get foiled due to some unforeseen circumstances. But what if we could prevent this from happening at all? That's where the concept of digital twins comes in. The concept of digital twins refers to the creation of a model of the proposed production design, which will work in real-time according to the real world constraints applied to it, thereby giving us an idea of how the actual manufacturing process will turn out in the real world. We can use this feature to identify any kind of error before going into actual production. This can also be used to run tests of experimental scenarios to check the conditions which can maximize the productivity of the process. The information or data which we obtain from digital twins can be used to improve the production process and also create enhanced and better manufacturing processes.
Will this Continue to be Popular?
In all the fields of industry, the manufacturing process is the most vital part of the production cycle and the firms are always on the lookout to optimize it any way they can and needless to say, AI has given them a wide range of options which were not available before. Although the stage of the application of AI systems in the manufacturing process is still new, we have seen a massive enhancement in the finished products due to their applications.
The usage of AI to optimize manufacturing processes has already resulted in a huge difference in quality in the finished products and has made the production process exponentially better. This has led to the concept of applications of AI in manufacturing to become very popular. Most of the large companies and enterprises are paying hefty amounts to employ data scientists in order to develop AI systems for their production processes.
From all the above information, we can be sure of one thing which is that AI has completely changed the way we used to implement our production processes. It has given us more precision and more effective control over the quality of the finished product. We can effectively manipulate the limitations of the product and various constraints under which it has to perform.
AI has considerably reduced the amount of time for decision-making and quality-checking processes involved in manufacturing, so more and more companies are looking for opportunities to employ AI systems. It has to be considered that the concept of artificial intelligence and machine learning is still at a very young stage and with the progress of time, it will only become better and better. With more innovations in the future, the demand for AI procedures will also increase exponentially and eventually the whole manufacturing process will become AI controlled. So, it is safe to say that the applications of AI in manufacturing processes is a rising trend and is here to stay for the foreseeable future.
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