AI and Automation in Manufacturing: Applications, Benefits, Challenges, and Future Scope

Artificial Intelligence (AI) and automation are becoming tools for making manufacturing better and helping to build smart factories. This paper looked at how AI and automations used in mechanical engineering. Some examples include predicting when machines need repair by learning from data, Industrial Internet of Things (IoT) CNC machining, robots moving materials, automatic quality checks, planning production watching energy use and digital twins. The review shows that AI and automation can help increase how much work gets done keep product quality high monitor machine condition and support decision-making using data. Predictive maintenance can catch when a machine is acting strangely. Machine vision can find defects that might be missed. Robots can handle materials. Do repeated tasks with consistency. Smart production planning can help use resources and schedule tasks efficiently. The amount of benefit from these tools depends on what they're used for, how good the data's how well they are put into practice. With these opportunities many challenges remain. High upfront costs, data quality, problems connecting with machines, security risks, lack of worker skills and doubts about how reliable the models are can stop companies from adopting these tools. Safety rules and regulations also need attention when adding automated systems to factories. So, companies should test if the technology works and if it’s worth the money before rolling it out. To address these problems the paper suggests a step-, by-step plan. It starts with identifying the problem measuring how well the current system works, collecting data choosing the technology testing the model running a small-scale trial linking the system into operations checking costs and continuously monitoring performance. The plan focuses on performance measures, human oversight and regular checks to make sure the system stays reliable. Future studies should look into AI, manufacturing, digital twins, edge computing people working alongside robots and green industrial practices. More real-world experiments and case studies are needed to see how better the work gets to figure out costs and to find when these tools are most helpful. In short AI and automation can make manufacturing more efficient, flexible and dependable. Using them well means combining technology with engineering practices, training workers protecting against cyber threats ensuring safety and constantly checking performance. Since this paper is a review of existing work the ideas it presents should be tested in factories through hands-on experiments.

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