Accelerate Smart Manufacturing with Digital Twins and Synthetic Data
With the integration of Digital Twins and Synthetic Data, MetAI successfully helps enterprises quickly implement smart manufacturing applications, reducing AI project development time by 70% with high model accuracy for metal manufacturers.
MetAI’s unique Digital Twin and Synthetic Data services have successfully overcome the data bottleneck of most AI projects, making data no longer a stumbling block for companies’ digital transformation!
This real case study of industrial transformation involves a metal product manufacturer based in Taiwan for several decades, which is renowned in Taiwan and has been commended by the Ministry of Economic Affairs for its outstanding performance. It specializes in producing steel pipes and metal bars.
Due to years of deep cultivation in the market, the company has a global reputation with its orders from not only Taiwanese clients but also from lots of internationally renowned machinery manufacturers. The demand for its products, while already large, has still been steadily growing. However, with increasing demand and full manual labor on the production line, the problem of orders exceeding production capacity has remained unresolved.
In order to overcome this bottleneck, the company began to explore digital transformation solutions, hoping to start with the quality control (QC) process, which is the most labor-intensive process on the production line, to improve production line efficiency. This is where MetAI can shine.
No Data, No Digital Transformation.
The customer had been exploring various defect detection solutions in the market for over two years. While solutions with contact sensors could potentially damage the products, non-contact visual inspection systems should be the ideal solution they needed. However, after trying various visual solutions, they were forced to abandon those due to various difficulties:
- Traditional AOI solutions could not meet the customer’s quality management requirements.
- AI-based vision solutions required sufficient data to drive them.
- The customer had difficulty allocating extra manpower to collect defect samples.
- Not all samples could be obtained due to quality management standards.
- Different types of sample data were generated at different frequencies.
One of the biggest challenges in digital transformation is the data problem. Many enterprises lack sufficient and high-quality data to train and support AI models. Additionally, collecting and labeling data requires a lot of manpower and time, which may be an unaffordable cost for some businesses. Therefore, solving the data problem is the main challenge that enterprises using AI technology face.
MetAI — Making Data no longer a Stumbling Block for Digital Transformation.
The majority of the customer’s challenges lie in “insufficient data” or “poor data quality,” but few system vendors can provide effective means to solve data issues. To overcome the digital transformation dilemma of data, MetAI’s in-house developed “Data Synthesizer”, while being capable of providing countless “synthesized data”, would be the most suitable solution for the current situation. Therefore, after the second meeting, the customer chose the solution provided by MetAI.
Synthetic Data is data generated by computer simulation or algorithms that contain annotated information to replace real environment data. In other words, Synthetic Data is created in a digital environment, not data collected or measured in a real environment.
Over the past five months, MetAI has assisted the customer in the following:
- Designing the optical structure of the defect detection system
- Establishing a Digital Twin and generating Synthetic Data
- Training the defect detection AI model
Designing the Optical Structure of the Defect Detection System
The customer previously relied on manual labor for metal pipes defect identification, which has long been inefficient for their needs. If automatic optical inspection (AOI) is used for character recognition, the curved surface of the stainless steel tube can cause reflections that make defects difficult to see. Additionally, factors such as dust and fingerprints can lead to misidentification of defects, making identification challenging whether done manually or with automatic optical inspection.
MetAI leverages its expertise in the machine vision industry to design an optical structure that enhances the visual features of the bar. The AI visual model, trained for this purpose, can accurately identify various types of defects on the bar.
Creating Digital Twins and Generating Synthetic Data
Unlock AI applications: Allow companies without sufficient data to import AI technology!
Small amounts of data normally fails to guarantee the generalization of accurate AI models. We estimated that it would take our client 14 months to obtain sufficient data.
However, we provided a unique solution in the market: using Digital Twins and Synthetic Data to quickly generate a large amount of training data, enabling rapid implementation of AI applications. By introducing this solution, we were able to significantly reduce the time and cost of data collection by nearly 70% during the originally estimated 14-month project period.
Generating an Unlimited Amount of 3D Data
MetAI’s Synthetic Data solution is based on a self-developed 3D Synthetic Data tool, which we call “Data Synthesizer”. This tool is easy to manage and expand, and can generate an infinite amount of randomized data.
Using 3D and physical simulation to generate Synthetic Data has the following advantages:
- Realism: Based on real-world environments and physical laws, the data generated is the closest to reality, allowing AI models to better understand the shape, texture, and physical characteristics of objects.
- Controllability: In the 3D and physical models, developers can precisely control the training process and physical features of the data.
- Credibility: Based on real-world physical environments and rules, the credibility of the generated data is higher.
- Scalability: It is free and convenient to expand the types and quantities of data to obtain a more comprehensive and diverse dataset.
By analyzing real metal bar samples, MetAI extracted the visual features of the samples and various types of defects, and built a random metal bar sample generator with defects, which can generate an unlimited number of unique 3D metal bar samples.
MetAI then created a Digital Twin of the optical structure with physical conditions similar to the real target, such as spatial relationships and environmental lighting, which enabled the generation of sampling data similar to the real optical structure.
Finally, using the ray tracing rendering technology of the NVIDIA Omniverse simulation engine, MetAI generated thousands of images that closely resemble real samples, effectively solving the biggest pain point in digital transformation for customers, “insufficient data.”
Saving Hundreds of Hours of Data Annotation Labor with Over 100 Times the Annotation Speed
MetAI’s 3D Data Synthesizer not only generates a massive amount of AI training data but also automates the data labeling process. In this case, two different visual models need to be trained with labeled training data: Semantic Segmentation and Object Detection.
The labeling task for Semantic Segmentation requires labeling each pixel by our data scientist, and is one of the most time-consuming tasks while gathering data. In this metal pipe case, manual labeling takes an average of 7 minutes per image, even with machine learning tools to assist during the process, it still takes 3 minutes per image for labeling. With Synthetic Data solution, it only takes 5 seconds to generate each image automatically, and 1 second to label each accordingly. In other words, automated labeling achieves an efficiency of 0.01 minutes per image, which is 200 and 400 times more efficient than manual and ML-assisted labeling, respectively.
Training a More Reliable Vision-Based AI Model for Defect Detection
In just 4.5 months, MetAI successfully assisted the metal pipe manufacturer in generating data and training AI computer vision technology that meets their quality management requirements.
Thanks to MetAI’s unique Data Synthesizer to create unlimited Synthetic Data, the effectiveness of the AI models was significantly improved, with the metal defect detection indicator (average absolute percentage error) increasing by over 28%.
MetAI saved 70% on data collection costs and provided a more reliable model for easier AI implementation!
The customer in this digital transformation case study had tried various solutions to solve their manual defect detection problems, but struggled with a lack of data that hindered their transformation efforts. MetAI’s Digital Twin and Dynthetic Data services effectively overcame the data scarcity challenge, helping the enterprise reduce data collection costs by 70% and accelerate AI application deployment.
This industrial transformation case demonstrates that digital transformation is not as difficult as imagined, as long as the right solutions are implemented. If you are also struggling with a lack of data and cannot smoothly carry out digital transformation, please feel free to contact us!
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MetAI | MetAI Technology Co., Ltd. | www.met-ai.net
Turning AI into a Companion We Met.
