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In recent years, several companies in the industrial sector have transformed their business operations with artificial intelligence technologies, including through quality control tools in the industry. Most of these business conglomerates are from the automotive, electronics, and transport and logistics operations industries. In all cases related to these respective sectors, artificial intelligence has the potential to face the demanding challenges related to quality control in the industry. In this post, our goal will be to describe the main learnings that arise through projects like these. ALSO READ: • Product counting
• Inventory tracking through technology
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How to perform quality control in industry?
Among the various quality control tools in industry, the one we will mention in this article is computer vision. One of the applications made possible thanks to the advancement of artificial intelligence has contributed to the solution of countless problems in the production sector. However, its large-scale implementation in the industrial sector requires significant effort and specific knowledge, not to mention qualified labor. Furthermore, creating real value with computer vision has been a challenge for many companies. Today we have easier access to data, greater computational processing power, and versatile artificial intelligence (AI) tools to build applications at a reasonable cost for specific cases. A better understanding of what can be achieved has encouraged customers to pursue innovation with new applications. Often, advanced video analysis can provide a cost-effective way to automate simple monitoring tasks and free up human labor for more productive activities, helping above all with quality control in the industry. At Pix Force, we continuously monitor the development of computer vision technologies. State-of-the-art deep learning advancements, including self-supervised pre-training and active learning, have been successfully utilized in our production-level solutions. As open-source tools are improving and major platform providers are also offering more evolved development environments, we align ourselves to create commercial value by selecting the most suitable technology stack for a given problem and building our solutions on top of it.
How to perform industrial quality control?
The best results in AI projects are achieved when business problems are clearly defined. Having a clear set of expected outputs helps to build algorithms for specific problems. The following examples describe some real-world problems in different business cases. In the automotive industry, manufacturers need to perform rigorous testing activities as part of their automotive industry quality control processes. This testing is often a time-consuming and human-resource-intensive activity, delaying and increasing the cost of the process. The use of quality control tools would focus on automating the analysis of weld seams, reducing the time spent on the quality inspection of each part. The solution would allow for improvements in productivity and quality. By providing easy-to-use tools for daily routine tasks, one can emphasize and facilitate the importance of supporting actual workers to do their jobs better, with the use of technologies that allow the optimization and facilitation of factory construction. The human aspect is very important to gain people's commitment and support when deploying AI in practice. In electronics and the heavy machinery industry, we need to take into account complex, multi-stage assembly processes. Computer vision can be used to monitor work progress and notify users in real-time during the process if something is incorrect in the assembly, such as missing screws, or to inform if potential critical components are not in their proper places. By providing solutions like the Pix Counter to our customers, for example, they can save costs and improve the yield and quality of their industrial assemblies.
Choosing the right approach and the right tools – Insight and experience needed
In the world of AI, it is crucial to choose the right approach and tools to solve the defined problem. And this is where experience from multiple executed projects can be very useful. Knowledge of different methodologies that can work in solving a given problem and the correct way to apply them is very beneficial in building the right solution. Sometimes, you also need to be ready to change the approach if the use case requires a completely new way of utilizing the necessary technologies, or even if a complete new implementation is needed: the data, the operating environment, the IT infrastructure requirements (on-premise/cloud/edge), and potential integrations may require many iterations to find the optimal solution to the business problem. Comparing different approaches is often the best way to find a viable solution. Co-creation and working with subject matter experts are key to finding the right approach. Data scientists cannot be experts in industrial processes, and typically, process experts are not deeply involved in data science. Good communication, problem-solving skills, and a common understanding of the problem to be solved are crucial elements for success.
To solve a computer vision task, you need appropriate data – various approaches
All Artificial Intelligence projects start with an appropriate task and required data definitions. Data is a key parameter that affects the performance of algorithms. Most computer vision tasks require good quality and properly computed data for algorithms to achieve the desired accuracy. Still, in each case, the data requirement depends on the use case. In addition, we should note that, to start with, even a limited amount of data can be used. Once data requirements have been defined, there are several ways to tackle the data challenge. These include using publicly or commercially available datasets, storing data using tools that support active learning, or acquiring services from companies specialized in such tasks. Sometimes, organizations lack the expertise to define data requirements or lack the resources and time to invest in good data collection and cloud storage. We realize that, in some cases, these solutions must be carried out by subject matter experts to achieve the desired quality. Pix Force is able to provide specialized help in analyzing specific tasks and defining data requirements. Furthermore, there are ways to automate some of these processes. For example, by designing and implementing a solution that uses existing CAD models to generate photorealistic images that will be used in training machine learning models. Most AI projects, unfortunately, still end up stalling at the ‘Proof-of-Concept’ stage. The reasons can be the following: • Lack of clear understanding of the potential value of AI for business
• Lack of competent resources – whether to buy AI or establish your own set of skills
• Lack of management support to invest in building AI capabilities
• Uncertainty related to the maturity of AI in a specific application area
In a recent survey conducted by two researchers named Athina Kanioura and Fernando Lucini, they mention three key success factors on the path to implementing an artificial intelligence technology: 1. Pivot to piloting — A piloted technology takes a fully developed resource and launches it directly into the real world (albeit on a smaller scale). 2. Commit to action – Organizations should consider only a few valuable projects and focus on doing the proper research and putting them into production. 3. Make sure you have the right team for it. At Pix Force, our vision is to enable a more autonomous future for our clients through scalable, state-of-the-art Artificial Intelligence and Computer Vision solutions. Furthermore, we see the application of scalable AI as an active part of daily life in our clients' operations.
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
