While collecting the images of good valves is easy, modern day manufacturing has very low defect rates. But if the line switches to a new type of valve, the data collection, training, and deployment must be performed anew.įor conventional deep learning to be successful, the data used for training must be “balanced." A balanced data set has as many images of good valves as it has images of defective valves, including every possible type of imperfection. As long as the valve stays the same, all manufacturers have to do is hit the “RUN" button and inspection of the production line can begin. Let's look at the example of spotting good and bad ventilator valves. Assuming it was fed a good amount of quality data, the DNN will come up with precise, low error, confident classifications. In a typical inspection task, a DNN might be trained to visually recognize a certain number of classes, say pictures of good or bad ventilator valves. Systems such as deep neural networks (DNNs) are trained in a supervised fashion to recognize specific classes of things. Using a Conventional Deep Learning Model for Quality Controlĭata is the key in deep learning's effectiveness. No human expert is required, and the burden is shifted to the machine itself! Users simply collect the data and use it to train the deep learning model-there's no need to manually configure a machine vision model for every production scenario. In the process of this learning, they create their own implicit rules that determine the combinations of features that define quality products. Unlike their older machine vision cousins, these models learn which features are important by themselves, rather than relying on the experts' rules.
#Deep learning factory software#
The new breed of deep learning-powered software for quality inspections is based on a key feature: learning from the data. Take bottle caps, for example-there are many variations depending on the beverage, and if one has even the slightest of defect, you run the risk of having the whole drink spill out during the manufacturing process. And while traditional machine vision works well in some cases, it is often ineffective in situations where the difference between good and bad products is hard to detect. There aren't enough human experts to support manufacturers' increased appetite for automation. But manufacturers' needs for quality control have rapidly evolved over the years, pushing demand to the next level. This method was simple and effective enough. That system then automatically decides if the product is what it's supposed to be. Step 2: The expert creates a hand-tuned rule-based system, with several branching points-for example, how much "yellow" and "curvature" classify an object as a "ripe banana" in a packaging line. Step 1: An expert decides which features (such as edges, curves, corners, color patches, etc.) in the images collected by each camera are important for a given problem. The traditional machine vision approach to quality control relies on a simple but powerful two-step process:
To understand what happens in a deep learning software package that's running quality control, let's take a look at the previous standard. So, how do these approaches differ from traditional machine vision systems? And what happens when you press the “RUN" button for one of these AI-powered quality control systems? Before and After the Introduction of Deep Learning in Manufacturing While manufacturers have used machine vision for decades, deep learning-enabled quality control software represents a new frontier. And given the mandated restrictions on human labor as a result of COVID-19, such as social distancing on the factory floor, these benefits are even more critical to keeping production lines running. The benefit? By adding smart cameras to software on the production line, manufacturers are seeing improved quality inspection at high speeds and low costs that human inspectors can't match. This latest wave of initiatives is marked by the introduction of smart and autonomous systems, fueled by data and deep learning-a powerful breed of artificial intelligence (AI) that can improve quality inspection on the factory floor. In 2020, we've seen the accelerated adoption of deep learning as a part of the so-called Industry 4.0 revolution, in which digitization is remaking the manufacturing industry. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.
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