Artificial Intelligence vs. Machine Learning - Which is More Effective in Quality Control

October 18, 2021

Artificial Intelligence vs. Machine Learning - Which is More Effective in Quality Control

Quality control is an essential process in the manufacturing industry. The traditional quality control method requires human knowledge and expertise, but with the advancement of technology, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as important tools for quality control in industrial automation. Both AI and ML have powerful abilities to analyze, learn, and improve quality control systems. However, the question is, which is more effective? Let's find out.

Artificial Intelligence

AI is a technology that enables machines to mimic human intelligence and perform tasks that require human-like decision-making. For quality control, AI is used to analyze data patterns and detect defects, which can be used to improve the manufacturing process. AI uses a combination of algorithms and statistical models to identify anomalies and determine their root causes. The advantage of AI is that it can analyze large amounts of data and identify patterns that may not be visible to the human eye.

Machine Learning

ML, on the other hand, is a subset of AI that enables machines to learn from data without being explicitly programmed. In quality control, ML algorithms can help predict defects and improve the manufacturing process by learning from the data. Unlike AI, ML requires an initial training phase, where the algorithm is trained on a set of data to identify patterns, and then it can be used to analyze new data.

Which is More Effective in Quality Control?

Both AI and ML have their pros and cons when it comes to quality control in industrial automation. AI is better suited for applications where data patterns are well-defined and predictable, while ML is better suited for applications where data patterns are complex and irregular. For instance, AI can be used to identify defects in a simple manufacturing process with a limited number of possible defects, but ML may better identify issues where data patterns are constantly changing.

According to a recent study by the International Journal of Advanced Research in Computer Science, AI outperformed ML in detecting defects in the manufacturing process, with an accuracy rate of 96% compared to 91% for ML. However, ML is better suited for predicting future defects based on historical data, which can help prevent quality issues before they occur.

Conclusion

In conclusion, both AI and ML have their place in quality control for industrial automation. AI is better suited for applications with well-defined patterns and is better at detecting current issues. ML is better suited for situations with complex data patterns and can help predict future quality issues. Ultimately, the choice between AI and ML depends on the specific needs of the manufacturing process.

References:

Abbas, A., Kim, K. J., & Ahmad, I. (2019). Comparison of Machine Learning and Artificial Intelligence Techniques for Quality Control in Computer Numerical Controlled Milling Machines. International Journal of Advanced Research in Computer Science, 10(2), 346-352. https://doi.org/10.26483/ijarcs.v10i2.6337