Implementing AI Based Quality Inspection System to Improve Quality Management System Performance
Patel, Romil (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024120933980
https://urn.fi/URN:NBN:fi:amk-2024120933980
Tiivistelmä
The aim and objectives of this thesis paper are to improve the performance of quality management systems by implementing AI-based quality control systems in manufacturing systems.
Conveyors are among the primary indoor transportation systems used throughout the production process. Conveyors improve efficiency and effectiveness by shortening process cycle times. In this situation, the quality inspection consists of several quality inspection processes at intervals. This quality measure employs various techniques, including batch testing, sampling, and random quality inspection.
This quality inspection technique requires more cycle time and resources still, the highest quality performance is not achieved.
This research intends to provide quality control throughout manufacturing by putting an AI-powered quality control system implemented over the conveyor system, allowing materials to be conveniently inspected during in-house movement at the same time.
This reduces cycle time, saves extra costs, and reduces the quality issue reported at the end, which can be detected occasionally, saving even more process costs.
AI is finding use in a variety of manufacturing applications. The ability to identify patterns and propose actions in periodic occurrences is a valuable advantage for both large and small businesses. Solutions based on artificial intelligence can solve a wide range of business difficulties in industrial environments. While traditional methods of visual assessment and automation are complemented with AI, stakeholders see considerable productivity increases and reduced operational costs. AI-based quality inspection systems will increase the quality of manufactured products while lowering costs.
Conveyors are among the primary indoor transportation systems used throughout the production process. Conveyors improve efficiency and effectiveness by shortening process cycle times. In this situation, the quality inspection consists of several quality inspection processes at intervals. This quality measure employs various techniques, including batch testing, sampling, and random quality inspection.
This quality inspection technique requires more cycle time and resources still, the highest quality performance is not achieved.
This research intends to provide quality control throughout manufacturing by putting an AI-powered quality control system implemented over the conveyor system, allowing materials to be conveniently inspected during in-house movement at the same time.
This reduces cycle time, saves extra costs, and reduces the quality issue reported at the end, which can be detected occasionally, saving even more process costs.
AI is finding use in a variety of manufacturing applications. The ability to identify patterns and propose actions in periodic occurrences is a valuable advantage for both large and small businesses. Solutions based on artificial intelligence can solve a wide range of business difficulties in industrial environments. While traditional methods of visual assessment and automation are complemented with AI, stakeholders see considerable productivity increases and reduced operational costs. AI-based quality inspection systems will increase the quality of manufactured products while lowering costs.