Manufacturers are Turning to AI-Powered Machine Vision to Succeed

Manufacturers are Turning to AI-Powered Machine Vision to Succeed
By Stephan Pottel

Progressive manufacturers are increasingly embracing Artificial Intelligence (AI), particularly a branch of machine learning known as deep learning, to enhance their machine vision applications. While machine vision solutions have existed in the industrial sector, their advantages are most apparent in industries where safety, quality, compliance, and efficiency are paramount, such as automotive, food and beverage, pharmaceuticals, and electronic manufacturing.

Machine vision applications encompass various functions, including quality control and end-of-line inspection, part traceability throughout the supply chain, measurement, presence/absence detection, metrology, and porosity inspection. Nevertheless, older machine vision systems still present operational challenges. These challenges encompass issues like compatibility between hardware and software, financial expenditures, procurement timelines, maintenance requirements, interoperability concerns, the need for training, and the management of complex use cases. For manufacturers in Germany, addressing the shortage of skilled labor is also becoming a more pressing concern. Automation is increasingly filling the labor gap and supporting the existing workforce.

Leverage Deep Learning for Machine Vision

The setup and management of industrial automation within manufacturing plants can often be slow and challenging. This difficulty arises from the reliance on multiple devices with varying software interfaces and outdated user interfaces. Moreover, many vendors require customers to use different software for fixed industrial scanners and machine vision cameras, resulting in a complex and costly landscape. This approach contradicts the fundamental principles of scalability, durability, and compatibility that we emphasize across all our portfolios, especially in our mobility, scanning, and automation platforms.

Manufacturing industries have undergone significant transformations. Production volumes and speeds continue to rise, necessitating compliance with new safety and regulatory measures. Data generated is also increasing, demanding efficient processing for valuable business insights. Manufacturers require modern machine vision solutions capable of addressing these challenges.

However, within the industry, many individuals are unaware of the new range of machine vision solutions powered by deep learning or have yet to grasp how these solutions can enhance their inspection and measurement workflows.

A substantial 81% of automotive decision-makers believe that their organizations could better achieve their business objectives through increased investments in technology. Similarly, 78% believe their organizations must foster innovation to maintain competitiveness in the automotive sector. Nevertheless, eight out of ten (78%) agree that their organizations need help to keep pace with the rapid pace of technological innovation.

Expanding automation, including the automation of visual inspection through machine vision, can unlock improved accuracy, speed, compliance, and safety. It also means that front-line engineers can delegate inspection tasks to machine vision, freeing their time for other valuable and essential workflows.

More Powerful, Flexible, and Easier to Use

Deep learning-powered machine vision software offers an outstanding solution for various applications, including surface inspection, examination of raw materials with inherent variations, textile scrutiny, categorization, conformal coating assessment, segmentation, and identifying features and anomalies.

The optimal combination of hardware and software harnessing deep learning capabilities can enhance machine vision applications, including more intricate scenarios, and empower engineers to think and act more like data and AI specialists. Modern cameras, sensors, and machine vision platforms equipped with deep learning can address longstanding challenges that older machine vision systems struggle with.

Contemporary AI-driven machine vision tools come with user-friendly interfaces that facilitate the creation of solutions through ‘drag and drop’ functionality, pre-built tools and libraries, and a flowchart approach. Additionally, experienced machine vision technology partners offer valuable data quality and labeling guidance. These tools also provide flexibility in terms of upgradability, enabling the conversion of fixed industrial scanners into machine vision cameras with a simple license upgrade, resulting in cost and time savings and the ability to adapt to evolving demands.

Engineers, programmers, and data scientists can collaborate effectively using graphical environments featuring comprehensive sets of thousands of proven and ready-to-use filters for crafting sophisticated vision applications. Programmer libraries are available for customized code and integrations, employing C++ code generators and various functions for image analysis applications.

The ease of use and flexibility extends to the plant floor, where engineers encounter numerous applications like end-of-line inspections, parts traceability, and presence/absence checks, often requiring Optical Character Recognition (OCR). While OCR is not a new technology and has been used for reading barcodes, serial numbers, lot numbers, and Vehicle Identification Numbers (VINs), it has its share of challenges, including lengthy training times, instability in changing environments, and difficulties with complex use cases. Many OCR tools demand significant investments of time and need help with reading obscure or damaged characters, engraved and embossed formats, characters on reflective or curved surfaces, and changing lighting conditions.

The latest OCR tools powered by deep learning utilize neural networks that mimic the human brain, delivering high accuracy straight out of the box. They are compatible with NVIDIA GPUs and CPUs, easily handle complex use cases, eliminate training requirements, and ensure stability and simplicity, even for non-experts. These new deep-learning OCR tools come with pre-trained neural networks based on thousands of different image samples, enabling users to create robust OCR applications in just a few straightforward steps. Deep learning OCR offers a versatile ‘deep learning everywhere’ experience for industrial imaging professionals, supporting desktop PCs on various operating systems, Android handheld devices, and smart cameras.

Stay Ahead of The Curve

Leaders in the manufacturing sector are already leveraging deep learning technology to gain a competitive advantage in response to the evolving demands of their industry and customers. Additionally, ongoing discussions revolve around the potential shortage of skilled labor affecting various industries. The challenges associated with labor recruitment could further drive the adoption of machine vision solutions to ensure continued operations and support for the existing workforce.

A recent global survey conducted among original equipment manufacturers in the automotive industry revealed that 24% of them currently utilize machine vision, and an additional 44% plan to implement it by 2027. This represents a substantial 83% increase in adoption. Furthermore, there is a 70% increase anticipated in the use of machine learning, with current adoption at 27% and future adoption projected at 46%.

Rather than waiting for industry peers and competitors to take the lead, and instead of merely reacting to industry challenges, the present moment presents an opportunity to capitalize on the potential of deep learning machine vision.