How Machine Vision Technology is Transforming Manufacturing Operations

In the realm of manufacturing, quality is a cornerstone that directly influences brand reputation, regulatory compliance, competitive edge, and customer satisfaction – and ultimately, revenue, to name a few. Maintaining high quality operations is historically a people-problem, relying on operators to conduct manual, redundant quality inspections and operations.

Rockwell Automation’s State of Smart Manufacturing report recently found that improved quality is the number one outcome respondents hope to achieve from existing smart manufacturing technology. It topped the chart for planned AI / ML use cases in 2024.

With recent efforts to produce more with less, maintaining a culture of quality has never been so critical and challenging. In a digitally-enabled Factory of the Future machine vision and automation intelligence are foundational to quality operations – enabling people at the core of a faster, better performing culture of quality.

What are Machine Vision and Automation Intelligence?

How are they used in manufacturing today?

Machine vision is the application of computer vision in a manufacturing setting to detect visual cues from product and process characteristics and infer actionable insights from the visual information. Automation intelligence is the application of operational cues, including vision-based insights, to correct and optimize production and alert operators to critical interventions.

Historically, machine vision has struggled with scalability and reliability in a manufacturing setting, but recent advancements have positioned it to address critical challenges such as workforce shortages, inspection precision, product complexity, and global quality consistency.

Evolution in Technology

Traditionally, quality inspection in manufacturing relied heavily on human operators. This manual approach, while flexible, was fraught with inconsistencies and errors due to human fatigue and subjective judgment. The early developments in machine vision aimed to automate this process, were limited by operational challenges, like poor image quality, and environmental factors, like equipment vibration.

Operationalizing these systems required highly controlled conditions, such as uniform lighting and precise positioning, to function effectively and limited adaptability to the variations and complexities inherent in manufacturing, making it difficult for vision systems to perform reliably. These systems were further limited by a reliance on rules-based algorithms and feature engineering which required individual adaptation for every defect variation, limiting the scalability and flexibility of early machine vision systems.

Software Advancements

The evolution of machine vision technology has been marked by significant milestones, culminating in the deep learning revolution we are experiencing today.

This shift has enabled the development of more sophisticated and adaptable vision systems capable of handling diverse and dynamic manufacturing conditions.

• Modern products are increasingly complex: This leads to more difficult feature profiles that require quality control. The development of Convolutional Neural Networks (CNNs) has revolutionized the ability for models to learn complex features through a layered filter approach that combines simpler feature recognition to extract reliable complex features.
• Defect detection requires samples to train the models on: This makes it difficult to compile a sizable number of samples to train a reliable model against. Some defects are also rare. Generative AI generates realistic images for training and improving vision systems, augmenting the need for the collection of massive amounts of data that can be difficult in certain applications.
• Traditional machine vision systems struggle to keep pace with the rising rates of new product SKUs and the changing demand: Few-shot learning, a data-efficient learning technique, can meet this demand by producing high accuracy and precision models with fewer images.

Hardware Advancements

The evolution of technology spans outside of the deep learning revolution to include imaging hardware and infrastructure advancements, widening the applicability of machine vision in manufacturing operations.

• Imaging hardware advancements like 3D imaging and the expansion of imaging across the spectral range, beyond visible light, allow for the inspection of more complex shapes and properties not visible to the human eye.
• A more closely linked edge and cloud infrastructure enable models to be trained in the cloud and hosted on the edge for seamless inference and integration into control strategies. The edge to cloud integration also allows personnel to monitor real time results and address root cause issues before they become costly.

Getting Started on Building the Factory of the Future

Machine vision systems hold immense potential for transforming quality operations in manufacturing, achieving a people-centered, technology-enabled culture of quality in the factory of the future. Manufacturers should begin by evaluating their existing quality control processes and identifying areas where machine vision can make the most impact. The journey to next generation quality control consists of deploying high-quality machine vision cameras and sensors to capture detailed visual data, developing advanced AI machine vision models for defect detection and characteristic verification, and integrating automation intelligence for immediate control action on the line will serve as the technology backbone of future quality operations. By strategically applying machine vision and automation intelligence systems across your manufacturing operations, your organization can realize its full potential in enhancing quality control.