In manufacturing, quality control is a collaborative effort and a vital reflection of all production factors.
However, companies inevitably face the problem of standardizing quality control procedures. Plus, it can be challenging to standardize responses, either in the presence or absence of issues.
In cases when subjects of quality control have different or even unique features, it gets especially hard to identify and eliminate defects. Keeping these issues in mind, we offer our computer vision solution aimed at streamlining this process cost-effectively and with high precision.
Efficient quality control is a vital concern in pharmaceutical and chemical industries, transportation, electronics, industrial equipment, and a range of other industries.
Issues associated with Quality Control contribute to the struggle of attaining industrial-scale production or upscaling in some other way. Product differences and factors for Quality Control outnumber the methods to monitor them. Moreover, human operators that oversee the manufacturing process have limited capabilities.
Also, failure to automate key components of quality control may have a more significant impact than many factories give thought to. Lack of automation and general complexities of QC interfere with the implementation of a company’s strategic intent even in established and well-functioning businesses.
As we have said, control of produced goods is particularly complex in some industries. For example, it applies to unique medical products. Human bodies differ by many accounts – everything from our fingerprints to entire organs. And when producing, let’s say, orthokeratology (ortho-k) contact lenses, companies need to make necessary adjustments that are not clearly visible to the naked eye.
Companies dealing with confidential data and parameter management in unique technologies are especially interested in computer vision capabilities.
The first step in maximizing the output would be to automate the process of recognizing a unique ID of the product. It is carried out using computer vision technology.
By developing our solution based on neural networks, we can achieve 99% accuracy of optical character recognition on irregular and transparent surfaces. The solution is designed with regard to a broad range of text applications. For example, if a company is willing to make adjustments, the technology will take that into account.
Then, there is a challenge to verify whether the product complies with regulatory standards. We address this issue by applying computer vision, which in theory reaches up to 99.95% accuracy. An object under inspection can be irregular and transparent as well.
The solution factors in up to 30 parameters within one processing cycle. The product features and conditions of input data can include lighting, different angles, equipment configuration, functional calibration, and further optimization after calibration. After initial implementation, we can make additions to the parameter selection. For example, when coming across cases in dispute, we can develop a complementary method for evaluating quality using computer vision.
It should also be mentioned that after adding new features and conditions, the existing parameters can be merged with the new ones. Once you make appropriate changes to the detection and classification processes, you can proceed to ramp up production volumes.
Due to the automation through computer vision, the costs associated with quality control could be considerably reduced. It means that enterprises will be able to upscale their production processes by introducing new data to neural networks.
Current capabilities of computer vision make it possible to automate the majority of existing processes in manufacturing. Ease of implementation ensures further industrial application and an increase in capitalization within a short period.
The graph above depicts leading industries in terms of computer vision utilization.
Potential total annual value of AI and analytics in Retail (Source: McKinsey Analytics)
However, there are other industries and fields of business that can effectively utilize this technology. Even though it might not be evident at first glance, you can take advantage of CV automation in your specific niche.