The global non-destructive testing market was valued at USD 11.5 billion in 2018 and is expected to reach USD 21.6 billion in 2026, growing at a CAGR of 8.2% during the forecast period. Non- destructive testing plays a vital role in assuring that mechanical and structural components perform their function in a safe, reliable, and cost-effective manner. The non-destructive testing comprises of a wide range of analytical techniques that are applicable to a wide range of industries. These analytical techniques help in inspecting, identifying fault without interrupting the process.
GET SAMPLE COPY OF REPORT @ https://www.alltheresearch.com/sample-request/249
The manufacturing industry has been home to substantial activities in the past few years aiming new advancements based on AI. Quality control is endemic in almost every industry, be it automotive or pharmaceutical. Many factories have deployed automated solutions in every sight, ranging from injection moulding to assembly, by using Kuka and Fanuc robotic arms. However, quality control is still an enormous challenge due to its dependency on human-level visual understanding along with the adaptation to constantly changing products and conditions. Moreover, many factories resort to hiring workers to perform this quality check step, which in turn, takes time and cost in training and testing.
AI, on the other hand, can easily work for 24 hours a day and seven days a week, which offers an advantage for test execution as often as required and at minimal cost (except the initial capital investment). Owing to such benefits, the researchers from top companies and institutions are developing a myriad of techniques that can improve automation. One such technology in AI is computer vision aided by convolutional neural networks (CNNs). CNNs has the capability to automatically learn the differentiation between good parts from not good parts on an assembly line with comparatively better speed. With a good AI product devoted to quality control, and training images that depict good parts and not good parts, quick training can be provided to CNN. This can be referred to as an incredible solution for high-mix environments, in which the products are constantly changing, and time is equally valuable. For instance, in the optics industry, CNNs can quickly respond to a range of lens properties that in turn prevent errors and downtime.
In August 2017, Infosys stated in its report that machine learning is adopted by nearly 75% of manufacturing companies as a factor for transformation, and around 57% of companies adopted AI for the management of cognitive tasks. The company further stated that AI has the capability to reinforce the existing infrastructure, which enables the identification of faults and mistakes that possibly weakens the production chain and quality of products. Machine learning and deep learning algorithms are continuously contributing to the growing automation of quality control in production chains, which considerably reduces the number of faulty parts, and the high costs resulting from them.