Quality- A competitive advantage
We’ve come a long way from traditional production lines and made processes efficient with industry 4.0 technologies such as Big data analytics, IoT, Deep learning, Virtual reality, but quality control still remains a largely manual process.
It is important to point out that beyond maintaining manufacturing standards and customer satisfaction, quality control has a direct impact on profits and reputation. Furthermore, there are heavy costs related to quality that manufacturers bear. It accounts for up to 20% - 30% of sales revenues of companies in some industries  most of which are difficult to track. Hence, it becomes imperative to invest in optimizing inspection and defect prevention for quality.
Drawbacks of Manual Inspection
Apart from being cumbersome and expensive, manual inspection requires time and extensive training for the inspectors. Downtime cannot be avoided and variances in operator judgements can lead to higher scrap. Complex components are difficult and time consuming to judge with just naked eye. There is also a major loss of data because manual inspection makes it harder to collect systematic data for all inspected goods, and the data collected is prone to errors. Furthermore, scaling becomes complex with increased overheads and training expenses. Finally, since time is a key driver in these industries, human speed and weariness can cause significant bottlenecks. We’ll now see how automated inspections with deep learning can help tackle these limitations.
What is Deep Learning and how does it help?
Deep learning algorithms are revolutionizing the field of automated optical inspection for manufacturers. But what makes these algorithms so groundbreaking?
Those algorithms are called deep neural networks because they sourced their inspiration into biological neurons that are connected between each other. In neural networks, those artificial neurons are stacked in layers. Recently, with more development, more and more layers are stacked, that's why we called this “deep” learning.
Deep Learning algorithms (called deep neural networks) is the family of machine learning techniques that achieve tasks based on learning from data. A quality operator is trained to differentiate between healthy and defective parts after looking at many examples, Scortex trains neural networks the same way.
Helping machines ‘see’ with learning based machine vision
Let’s now look at how machines look at objects. Machines need to ‘learn’ from examples just as humans learn and this task turns out to be deceptively complex. Learning involves providing a set of images to establish what the element is, hence, in the future the machine can identify the patterns in the image to establish if the image and element is in concurrence.
There are 3 steps involved in computer vision, capturing images/video in real time, processing this content with a deep learning algorithm and then taking actions based on these results.
The adaptive capabilities combined with the scalability and potential insights of machine vision makes it a powerful tool for manufacturers seeking better and faster real-time visibility on Quality.
A gift that keeps on giving - Data
Data of high significance and volume is produced in the day to day operations of the factory floor. The data available from the machine vision platform can be turned into valuable insights. With the ability to automate, digitize and optimize, deep learning is ideal for the manufacturers since they can benefit immensely from real time decisions.
This data, if used effectively, can lead to the development of exceptional understanding of how supply chain, sourcing, factory operations, compliance and quality management affect costs. These technologies, if used in quality inspection, can bring a lot of difference to the future costs borne by the manufacturers that may not be directly visible. Read more about how data can help manufacturers optimize overall operations.
 Analyzing the Impact of Quality Tools and Techniques on Quality Related Costs : Comparing German Industries, Michael Donauer, Henning Mertens and Martin Boehme, 2015
Data of incredible value is created everyday on the factory floor and can help find exceptional or weak suppliers, quality insights or operational improvements. There is no doubt that Machine Vision and Deep Learning will be an integral part of Industry 4.0 revolution pushing global manufacturers of today to a new level of efficiency and productivity.