What is the common point between Boeing and Toyota?
They both face tremendous expenses related to the quality of their products:
a $2.6 trillion of aerospace services of Boeing is exclusive to the quality and maintenance of aircraft parts;
a $1.3 billion settlement has been due by Toyota because of acceleration and brake defects in their automobiles.
Beyond these amounts, the Automotive and Aircraft industries illustrate the challenges faced by the whole Manufacturing Industry. Although each specific field of the industry, at its scale, has its stakes, they all face the same challenge: ensuring the quality of their goods.
Until recently, most of the Manufacturing Industry heavily relied on human vision inspection to detect these defects. Sadly, this method is not 100% effective. Each visual inspector judges the quality of the product differently, and human quality checks can't scale.
Thankfully, visual AI technology now enables the Manufacturing Industry to overcome the limitations of Human Visual Inspection. Here's a guide on how computer vision for defect detection can help manufacturers and businesses and how to implement it.
Computer Vision for Visual Inspection: what’s in it for you?
Computer Vision comes with quite some benefits from manufacturers – but also for businesses. On the one hand, it fastens defect detection, and lowers costs, reduces delays and scrap. On the other hand, it brings clear improvements to the workflow and improves the accuracy of defect detection, model, and employee satisfaction...
If we were to sum this up, the benefit is twofold:
it improves your manufacturing processes, and by doing so: your profitability;
it eases the detection work, and therefore: your employee’s satisfaction.
Levering Computer Vision to optimize manufacturing processes and drive profitability
Defect detection using computer vision improves the Manufacturing process and optimizes costs. It minimizes the manufacturer's costs by identifying detects earlier and faster. It also reduces scrap and improves employee productivity. Increasing defect detection accuracy and reducing defect-related costs such as recall also play a part in preventing:
replacement and transportation costs;
extra marketing expenses;
As the visual inspection process levering computer vision accelerate the global manufacturing processes, manufacturers using it are likely to speed up their entire operations. Freed from limitations from the traditional visual inspection performed by humans, they’ll no longer have to invest in costly employee training or additional recruitments. Additionally, they usually tend to see a reduction in their inspection issues, an increase in yield, and an overall increase in product quality with a reduced cycle time. On the midterm timeline, visual inspection powered by computer vision will likely improve the global manufacturing process and the ML model performances – hence being helpful to prepare for future purposes. One of these potential purposes is historical data tracking that Data Scientists of the manufacturing industry can leverage to pinpoint issues and, ultimately, how they can use them to learn and improve future production processes.
Implementing Visual Defect Detection to improve employees’ satisfaction
Human Visual Inspection is a tedious task that can cause visual fatigue and is vulnerable to errors. These cumbersome tasks can lead to:
unproductive work behavior,
Since computer vision solutions can detect defects at a faster rate than humans, they will not only improve staff productivity but also allow employees to allocate this extra time and energy to other areas.
Adopting computer vision will likely free employees from feeling bored and unmotivated and allow them to allocate their skills elsewhere, handle more productivity and reach better performances.
Implementing Visual Defect Detection to improve employees’ satisfaction
Depending on your business, your first point of call is stating the problem that needs to be resolved using defect detection. You could ask yourself questions such as:
Is there existing software used for visual inspection?
Should the inspection be in real-time?
Should the visual inspection system detect defects by type?
How do you want the system to notify detected defects?
Once these questions are answered, you can move on to the next step: understanding and determining what hardware you need. For example, inspection stations with cameras, lights, etc. Consulting firms can help you implement this end-to-end process, which may require changes to current processes.
ML model development stages
Step 1: Prepare the Data
To implement an AI- integrated visual inspection system, you will need to collate and prepare the data that will be inputted as the training dataset for the model. This training data needs to be cleaned and happens before the model learns from the data.
In particular, it is crucial to consider the Internet of Things (IoT) data for manufacturing processes, as it provides consistent and accurate insights. The concept of IoT is that each object is tagged with an electronic tag and connected to the Internet, allowing for each thing to be easily identifiable on the Internet. Data can be in the format of video recording, images, etc. When collecting this data, the most critical element you need to consider is the quality of the image or video recordings. The higher the quality of the data, the more accurate results.
Step 2: Develop your model
You have three options here:
1. Using a deep learning model development service, such as Google Cloud ML Engine.
2. Using pre-trained models.
3. Building your model from scratch.
When building your model for visual inspection, there are several computer vision algorithms that you can use, such as image classification and object detection.
Your choice of algorithm depends on different factors, such as:
the business goal;
the size of the product that may have defects;
the types of defects;
the lighting conditions;
the volume of products that need to be inspected;
and the quality of the input data (videos and images).
Step 3: Train and Evaluate
Once your training data is ready, and you have your model prepared, the next step is to train your model. Data scientists will evaluate the model's performance and validate its accuracy level. Data scientists can fine-tune areas they believe need improving to increase the model's accuracy. During this phase, a test dataset can be very useful for conducting a further evaluation.
Step 4: Deploy
Once your model is producing accurate results, you are ready to deploy. However, the best practice is continuously improving your model – for example, inputting new training data.
Computer Vision for Defect Detection: a key factor of scaling
Scaling is one of the manual visual inspection's biggest and most common challenges. It's no surprise when considering that a single inspection can require 3 control inspectors; running 2 shifts; for 6 consecutive days a week.
By implementing computer vision, businesses are more likely to optimize their processes and achieve their scaling goals. As the computer vision tool resolves the bottleneck of human quality control inspection, the manufacturers can build in other areas.