Almost every industrial organization faces obstacles and demands in the development of quality assurance and the simplification of the manufacturing process.
How quality assurance is carried out differs from company to company. They are roughly classified into three categories:
- In the first category, the whole quality control procedure is still done by hand.
- In the second, work on automating various aspects of the quality assurance process has begun.
- The third group actively investigates and evaluates new options.
Automated mass processes designed to handle huge volumes currently employ a variety of automated solutions based on machine vision. However, with typical machine vision systems, the expert has already evaluated if a product is broken or accepted.
The data mass may be used more efficiently and the system can be trained to understand the attributes that characterize a high-quality product by introducing Artificial Intelligence into the machine vision solution. We may talk about Industrial Edge AI, which is the use of emerging technologies such as Artificial Intelligence, Machine Learning, Machine Vision, and sensor data to improve process efficiency and automation.
Artificial intelligence is an excellent remedy to quality assurance issues. It may considerably decrease or eliminate manual tasks, enhance and accelerate errors detection, discover flaws in the manufacturing process, and even forecast future failures.
Artificial intelligence solutions may now be applied in lower-volume industrial processes due to the advancement of new technology and cheaper pricing. We’ve included three examples of how AI Quality Assurance solutions function below.
1.) Analyze your manufacturing data to improve quality.
Quality assurance in the manufacturing and process sectors is still frequently relied on sampling or expert assessment of batch quality. Sample analysis and visual inspection may be automated using an Edge AI solution. The approach makes use of sensor data that has been evaluated by artificial intelligence and machine learning algorithms.
Furthermore, Edge AI systems offer faster detection of tiny alterations, which can at best prevent bigger batches from spoiling or halting the entire manufacturing process.
2.) Automatically detect flaws and abnormalities
Defective items are more than just an expense. They can endanger the company’s reputation or harm the end-user. Part of the manual inspection job can be abolished or, in certain situations, totally replaced with the aid of artificial intelligence.
With AI precision of mistake detection is enhanced, quality requirements can be ensured and standardized, and the overall process is sped up.
The solution is suited for evaluating a wide variety of surfaces or forms. Sensors of many types are available, such as those that can examine vibration or sound. Various cameras include infrared, stereo, and hyperspectral cameras.
3.) Recognize gradual changes in the manufacturing process
Over time, minor modifications in output may occur. You can understand how and when these deviations happened more efficiently with AI-based solutions.
Defective items may be found via manual quality control, but the fundamental reason is frequently unclear. In this case, the previously obtained production data aids in determining where the process variation occurred. Many factors in the manufacturing and supply chain might have an impact on a product’s life cycle. Analyzing this as a whole can lead to important discoveries as well as value creation for the end-user.
Cause-and-effect linkages can be established by reviewing manufacturing or component data if the product gets service or warranty requests. Things that generate systematic mistakes in completed goods can thus be improved in the manufacturing process. If you continue to improve the solution, you may be able to foresee future mistakes. What if you knew why the items were faulty before the production process was completed?
How can the power of artificial intelligence be unleashed?
Integrating artificial intelligence into the manufacturing environment and process, on the other hand, is a difficult task. The issue is not a lack of belief in artificial intelligence or the promise of new technology.
So, why isn’t artificial intelligence being employed more frequently? This is due to two factors:
- the verification of commercial advantages,
- lack of critical skills and knowledge.
- Justifying the decision to invest in new technology can be hard.
The cost savings and advantages of artificial intelligence can be difficult to understand, especially if the organization has never attempted this kind of solution before.
In the long run, consider tackling the subject strategically. Is there a place for artificial intelligence and solutions based on new technologies in the strategy? It is critical to establish specific goals and timetables. As new technology acquires a grip on strategy, the development will become more comprehensive.
- Because organizations do not have adequate internal knowledge, new technologies may not be given the attention they need in process development.
Commonly, you cannot locate all of the required knowledge in-house, so you must either purchase it elsewhere or begin developing it internally. It might be difficult to find a qualified solution partner that knows the rules of both technology and business.