Introduction
The advancement and growth of computer technology have increased the intensity of worldwide competitiveness. With Artificial Intelligence (AI), many firms predict that the future of manufacturing operations will alter radically, from planning, scheduling, and optimization. Today, AI has effectively addressed fundamental manufacturing problems such as predicting and avoiding machine-related errors (Litvinova 7). Therefore, this essay explores an operation case, discussing the tools in AI, particularly TensorFlow and Theano, and their implementation issues. Operation technology is also discussed by exploring AI in machines, explicitly focusing on the current state of the technology and its economic impact on companies.
Operations Case
AI has aided in the processing of vast amounts of data and its use in business. With the advancement of AI and Machine Learning (ML), many tools and frameworks accessible to data scientists and engineers have grown. In this case, TensorFlow is utilized as an ML and AI software library. It is used in various applications with a specific focus on deep neural network training and prediction (Géron 33). TensorFlow is useful in environments that handle large amounts of data and require the prediction of the behavior of the systems. Theano is also implemented as a Python module that allows companies to assess arithmetic computations such as multi-dimensional arrays efficiently. It is widely utilized in the development of ML projects.
Operation Related Problem
For instance, Siemens relies on AI to address some of the more complicated quality-related errors that would directly interfere with the operation of the manufacturing process. Siemens has been employing smart boxes to digitalize motors and transmissions for the interconnection of over 65,000 nodes (Blackman 6). According to Blackman (7), sensors and telecommunication connections for data transmission are housed in boxes designed to detect and relay information to actuators. Siemens has employed such techniques to enhance productivity on the factory floor by automating repetitive quality control check operations.
Implementation Issues
TensorFlow and Theano are implemented on Graphics Processing Units (GPUs) for optimum ML performance. The intelligent systems help improve the monitoring process, addressing lead times and quantities consumed at every cycle stage. The AI systems are specifically designed to form judgments about a machine’s status and spot abnormalities by analyzing data, allowing predictive maintenance. The AI systems ensure a more accurate prediction of the behavior of the systems necessary for timely intervention. Utilizing AI technologies in quality control has since improved the overall efficiencies of operations at Siemens.
Similarly, AI technology is employed in coordinating predictive repair and maintenance of heavy-duty machines such as high-speed trains. For example, Siemens collaborates with Deutsche Bahn on a pilot scheme for high-speed train predictive repairs and maintenance (Kulawiak 8). Data engineers use AI to recognize trends in the operational data of automobiles. In such a setting, the program finds alternatives that meet all requirements, such as those for dependable operation, among the billions of conceivable hardware combinations for a switch tower. With such benefits, the current state of AI in machinery seems to be growing exponentially, with emerging technologies offering several benefits to companies utilizing the technology.
Operations Technology
Artificial Intelligence in Machinery
Siemens and Deutsche Bahn are some of the leading companies that employ AI in machinery. Such companies appreciate that AI systems assist engineers in forecasting when or whether functioning equipment will fail, allowing maintenance and repair to be arranged before the breakdown. Kulawiak (9) notes that manufacturers can enhance productivity while lowering the cost of equipment failure due to AI-powered predictive maintenance. AI technology is significant in addressing machine operation efficiencies because it allows the software to perform human capabilities such as thinking, judgment, and organization more accurately. According to Blackman (7), AI-powered machines can also perform collaboration on real-time adjustments and performance evaluation more effectively, efficiently, and at a lower cost.
The Current State of Artificial Intelligence in Machinery
Currently, AI in machine applications is transitioning from a concept to reality. Many modern applications use AI, and the breakthrough is set to revolutionize the face of automation in the near future. AI has already become a fundamental aspect of manufacturing and automation in most engineering, logistics, and maintenance processes. Many experts believe that AI will revolutionize everything that would otherwise need extensive human attention and interaction (Dolci 21). While many individuals struggle with AI, technical limits are becoming less of an issue overall, with strategic and managerial constraints emerging as the major roadblocks in emerging technologies.
Today, some of the emerging technologies are in operational simulation and optimization. Simulations in machine designs and optimization are significant application areas for AI in machines (Marcus 17). End-users may schedule their equipment usage more efficiently, arrange material flow and supply more dynamically, and predict potential shock events thanks to dynamic modeling and optimization of systems. The desire for end-users to reduce total operating costs and the advent of classical mechanics AI solutions are key driving factors in the category of the AI-powered machine. As additional production lines are connected to the supply chain and operations become more complicated, there will be greater demand for AI solutions that address operational modeling and improvement.
Emerging commercial players include robotics research companies like Tesla, Inc. and Boston Dynamics. The future direction of these companies is the application of AI is optimizing the operation of machines to enable self-sustaining models that can ensure energy efficiencies and high safety standards. Marcus (21) notes that these manufacturing businesses benefit from artificial intelligence because it improves with time. Machine learning models often grow increasingly accurate and can predict errors and abnormalities as they examine data particular to the company and manufacturing process. The efficiencies offer several economic opportunities that improve the economic prospects of the companies.
The Key Economical Aspect of Artificial Intelligence in Machinery
AI facilitates the execution of previously complicated activities without incurring substantial costs. AI also works without interruptions or pauses, and there is no downtime. Reduced downtime and faster processing mean that the economic value of the company’s pieces of equipment is realized. Such characteristics of AI give the technology a broad commercial appeal. In comparison, a typical human will work relatively slowly and labor for only six hours every day, excluding breaks (Aoun 17). Humans are designed to take some time off to renew themselves and prepare for the following workday. Human laborers also have weekly breaks to keep up with their work-life and home relationships. But, unlike humans, companies can use AI to make robots work faster without interruptions improving the overall economic gains of the company.
Conclusion
Applying AI-powered industry solutions improves the automation of processes, allowing firms to create intelligent workflows that cut costs and downtime. AI systems employ pattern recognition and combine it with general intelligence to estimate potential flows in machines. Siemens and Deutsche Bahn are leading companies that have successfully used AI in machinery for optimized operations. Other companies like Tesla, Inc. have advanced their research in AI to the extent of augmenting human capabilities in their machines. Such extensive application of AI in machines has several benefits, including improved efficiencies and lower operating costs.
Works Cited
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Dolci, Rob. IoT solutions for precision farming and food manufacturing: artificial intelligence applications in digital food. IEEE 41st Annual Computer Software and Applications Conference, 2017.
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Litvinova, Tatiana. Managing the development of infrastructural provision of AIC 4.0 on the basis of artificial intelligence: case study in the agricultural machinery market. Institute of Scientific Communications Conference. Springer, Cham, 2019.
Marcus, Gary. Innateness, alphazero, and artificial intelligence. arXiv Preprint, 2018.