Even the initial concerns about Artificial Intelligence (AI) have not slowed its adoption. Experts say that some companies are already taking advantage and that companies that do not adopt new technology cannot compete over time. However, AI adoption seems to be slowing down despite early successful case studies.
Why are AI services so slow in manufacturing?
AI is growing, but getting accurate numbers is difficult because the definition of machine learning, AI, machine vision, and other technologies is often obscure. For example, the use of a robotic arm and camera to inspect parts may be advertised as a machine learning or AI device.
Although the device works well, it can only compare images taken to others that have been added manually to the library. Some would argue that this is not a machine learning device because it is a pre-program decision, not a “learned” from the machine’s experience.
Going forward, this article uses generic terms when referring to Artificial Intelligence technology. When deciding on a design or product, make sure you understand the differences like buzzwords versus unsupervised and other buzzwords that are obscured by sales and marketing efforts.
According to a report by Global Market Insights published in February this year, the market size of AI in manufacturing is projected to exceed $ 1 billion in 2018 and is expected to grow at a CAGR of more than 40% from 2019 to 2025. Sources say the AI is moving slowly.
Some resources often compare AI case studies to the overall size of the manufacturing market, talk about investing in individual companies or, in particular, AI. From this prospect, AI growth is slow and it is for some reason other than the above.
AI is still a new technology. Most success is in the form of testbeds, not full-scale projects. In large companies, a small adjustment can affect billions of dollars, so managers are reluctant to test full-scale projects until they find the best solution. Also, companies of any size need to justify or guarantee the return on investment (ROI). This can lead to smaller projects, focusing on low-hanging fruit, or projects that can be separated into a testbed.
To know more- AI In Manufacturing — 10 AI Manufacturing Use Cases
Small or isolated projects may work well as a test, but in theory, AI should provide greater benefits when operating at larger scales. This usually requires greater connectivity and data to maintain accuracy. This is the next reason for AI to move slowly: scale and connectivity.
Most companies have legacy devices that do not provide data or have the means to send data to another location. New technology is working on retrofitting legacy equipment, but there may be infrastructure issues for design engineers. For example, some factories do not have easy access to smart sensors or data that provides greater access to the IT network.
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As AI grows and continues through all resources, maturity, confidence, ROI, scaling and connectivity can slow down mass adoption.
What AI can do for manufacturing and design?
This section may be difficult because it relates to the previously mentioned blurred lines and buzzwords. Designers and manufacturers have used CAD tools, machine vision, and maintenance attendee management before. AI technology is taking these technologies to new heights, but personal devices can be discussed wherever the AI is on the spectrum.
AI CAD tools
Design engineers have features they need to achieve when developing new components and equipment. To do this, it is important to understand the information about the end user’s applications and needs, from materials and processing. CAD programs with theoretical data include tools such as finite element analysis (FEA), and the design engineer must add data manually or select from the library.
A new tool in CAD technology is using AI to create product design. It takes the necessary features and inputs for the design and also generates all possible materials, geometries, and costs. If the new features are user friendly, the technology is just as good as the user.
Not only do you need knowledge of what to add to the specification and input, but the user still needs to review the possibilities to choose the best solution. This type of AI CAD technology helps to increase the capabilities of design engineers and saves time because the design engineer does not have to create multiple iterations manually.