Impact of AI on Manufacturing efficiency and quality
Artificial intelligence (AI) is increasingly being adopted by companies.
With artificial intelligence in manufacturing, factories will become smarter and more efficient, lowering manufacturing costs and addressing supply chain and product quality issues.
Industries can benefit from the amount of data that has been collected during the manufacturing process.
As data is analyzed with machine learning, critical insights are gained, and manufacturing is greatly affected. Advanced AI analytics delivers ongoing monitoring & optimization of yield and is powered by automatic alerts in case of issues, prediction reports and more.
Leveraging AI and Machine learning to optimize quality for electrical and electronics products
Since the electronics goods and the automotive markets are growing, manufacturers cannot afford to waste time on faulty units or manufacturing errors that result in product recalls. Customer quality expectations must be met and deliveries must be on time and on budget. Artificial intelligence can have a significant impact here.
Manufacturers have been impacted by artificial intelligence in electronics products in the following ways:
- Enhance the quality control and inspection of electronic products
Keeping your customers happy requires meeting the highest standards. Artificial intelligence can help manufacturers identify quality issues in their products or services.
As data becomes available, manufacturers can also gather it to make better future decisions about their products. Artificial intelligence can address all types of errors in product design during the early stages of development.
QualityLine AI analytics delivers insights on:
- Product testability.
- Test limits optimization.
- Smart correlation and cross coefficient between product parameters.
- Implement preventative maintenance
The manufacturing industry relies increasingly on machines, and companies must boost productivity and uptime to succeed. In predictive maintenance, machine performance data is analyzed to forecast when a machine will fail, limit the time it will be out of service, and identify its root cause. By utilizing AI analytics, the machines can be made to operate as efficiently as possible, helping to increase yields, throughput, and energy efficiency.
The higher the yield is, the more consistent and reliable the process.
In order to improve production yields and efficiency, it is important to gain full visibility into all stages of the manufacturing process.
Using AI analytics tools to determine the proportion of finished units that pass inspection during product testing is the most effective way to accomplish this.