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We’ve written before about the importance of incorporating sound quality management principles within a manufacturer’s operations. Failure to meet specific quality standards can not only hurt the brand’s reputation, but also result in compliance issues should the business fail to meet stringent regulatory requirements.
As a result, manufacturing businesses invest significant resources to implement tools and strategies for identifying and eliminating the source of quality defects.
In the more traditional manufacturing settings, a significant majority of the defect detecting efforts are manual. In other words, manufacturers depend on humans to visually check and/or test each product to ensure that it’s free of defects.
Some relatively modern manufacturing operations utilize different types of equipment and machinery for visual defect detection. This approach offers an edge over human quality inspection.
More advanced manufacturing businesses have gone further to both improve defect detection and increase production efficiency. Businesses have begun investing in artificial intelligence (AI) and machine learning (ML) to ensure that they catch as many defective products as possible without the need for manual human inspection which has proven to be more resource-intensive and prone to error.
In fact, according to Fortune Business Insights, manufacturers across the globe are expected to spend $9.89 Billion on artificial intelligence by 2027.
In this post, we’ll discuss why manufacturers are adopting a combination of computer vision and artificial intelligence to help streamline visual quality inspections and reduce costs associated with quality management.
Challenges posed by human-intensive defect detection
Although using workers to detect product quality failures visually seems relatively straightforward to implement, it can present more challenges to the manufacturer in the long run. These challenges include:
The human eye isn’t always keen enough to detect inconsistencies or faults in the product quality.
If they do detect an issue, it can be inefficient to document, track, and pass the information on to the relevant personnel to implement corrective action.
For time and resource-intensive quality inspections, bottlenecks are more likely to occur on the production line. This interrupts work schedules and other vital processes in the workday.
Downtime on the production line resulting from quality inspections can throw off order fulfillment and delivery times, increasing customer frustration with the brand.
Furthermore, the accumulated work-in-progress drive up production and warehousing costs.
In short, human/manual visual quality inspections often create challenges for manufacturers, costing the business money and diminishing production efficiency. These issues tend to be more pronounced in larger manufacturing businesses because scaling human visual inspection further compounds the problem.
Manufacturers who have upgraded to quality inspection equipment and machinery machine also face similar problems. For example:
These machines are often still operated by humans, again introducing the potential for human-based errors and inconsistencies.
Traditional machinery can’t quickly adapt to new product changes. Instead, manufacturers would need to invest in new machinery, re-tooling, or significant reprogramming.
These machines can only detect a few types of defects at a time.
These challenges have driven manufacturers to invest in and implement AI-powered defect detection systems for more sensitive, comprehensive, and effective product quality inspection and automate traditionally manual quality management processes.
What does AI-enabled quality inspection look like with Tulip?
Integrate machine learning-powered defect detection into your operator workflows with off-the-shelf cameras and no-code configuration.
Using Artificial Intelligence to automate defect detection
As manufacturing techniques and operations have grown, so has the technology needed to run them effectively. Industry 4.0 has introduced manufacturing operations with an ever-growing list of options to connect the machines, devices, and equipment to the internet. This phenomenon has provided manufacturers with more control and insight into every process and task executed on the factory floor.
In more recent years, introducing AI into the manufacturing process has become possible due to the cloud computing capabilities afforded by Industry 4.0. Modern manufacturers leverage this development to marry computer vision hardware along the production line with AI-powered, cloud-based digital tools.
This visual inspection equipment (oftentimes, basic web cameras are all that’s necessary) sends product images to the cloud. A well-programmed machine learning algorithm will then analyze them to point out any potential defects and nonconformities.
Through large amounts of product data and images, the algorithm is programmed to detect even the smallest discrepancies in product quality, improving defect detection capabilities beyond even what the human eye is able to achieve.
Benefits of Artificial Intelligence in visual quality inspection
As discussed, the combination of AI and computer vision systems provide many benefits, differing significantly from human-based visual quality inspection in manufacturing settings. These benefits include:
Eliminating the potential for human error can significantly decrease the rate of quality defects slipping through production.
The algorithm is able to detect multiple flaws in a single product in a matter of seconds. Humans and traditional visual inspection machines may only be able to identify a few defects in a significantly longer amount of time.
Personnel requires training to adjust to new product specifications and features, requiring additional time and resources. While the AI needs to be “trained” to identify defects, it requires significantly less time and human input.
Unlike humans, artificial intelligence can beyond surface-level defect identification. Its machine learning technology applications also provide further insight into the blemishes and possible causes.
While human-based quality inspection is difficult to scale as production levels increase, AI-powered quality inspection solutions can be easily scaled across multiple production lines and manufacturing locations, providing more effective defect detection.
Ultimately, implementing artificial intelligence and computer vision can provide massive benefits for manufacturers by eliminating a core source of inefficiency and reducing the costs required to manage quality control.
Using Tulip, manufacturers are able to leverage AI-powered quality inspection and integrate quality management with digital solutions such as digital andon, production tracking dashboards, and other common assembly-related use cases to provide a comprehensive, centralized platform.
If you’re interested in learning more about how Tulip is helping to streamline quality management practices for manufacturers across the globe, reach out to a member of our team today!
Streamline Your Quality Management Efforts With Tulip
Learn how leading manufacturers are using Tulip to capture real-time data, automate defect detection, and improve their operations.