The rise of automation and technology has left the world swimming in data. With advances in artificial intelligence (AI) already poised to transform every industry and humankind concern from climate change to bubble leak detection, we should all be pleasantly primed for a bright future. Yet, with all its promise, and some of the brightest minds of our time thinking about this, the actual implementation of real-world AI seems lagging behind its value promise, and somewhat stalled within the industrial sectors who could use it the most. As new solutions emerge, learn how to evaluate tools that can help manage manufacturers' data and put it to work on their manufacturing floors.
One of the biggest challenges lies within the sheer amount of data that exists. Typically, companies have lots of random data sets that need to be collected, cleaned, consolidated, labeled and used to train robust models. Additionally, more traditional manufacturing floors may not have automated their collective data yet, leaving it nearly impossible to learn from the past, and build on the information they have already accumulated.
Creating a workable data set is not for the faint of heart, yet, being able to put data to work, can redefine quality improvement for a company. Eliminating recurring inconsistencies, incorrect defect definitions, and inspector fatigue can catapult a company to that coveted five nines quality measurement.
Think about it this way: a company with hundreds of products, where each product has hundreds of defects and patterns to detect, with each of these requiring around one-thousand of potential samples of labeled data to successfully manage, illustrates how the complexity increases with every new data set.
Gaining control of this complexity may seem like an overwhelming task and adding visuals and video to the data mix only adds to the challenge. Yet, while it may feel daunting, it is doable.
The key is to find a data capturing tool that cleans, categorizes and stores data into a concise end-to-end platform to efficiently manage data flow for manufacturers.
Next, manufacturers will want to resolve the ambiguity in defects by creating a clear and organized Digital Defect Book, dynamic in nature so it evolves with the system and expands as new data becomes available. The platform must be able to propagate changes to assure continuous quality improvements.
Identifying the right selection criteria for any tool can be tough. Limited function platforms have emerged that do one or two aspects of a project, but few that speak to the entire process. The key is to find one end-to-end platform that can accommodate all of a company's quality assessment and data needs.
These four questions will allow companies to quickly assess any potential solution:
- Labeling consistency: Labeling inconsistencies are inevitable using only human inspection. Does the tool have a built-in labeling tool and consensus mechanism to limit ambiguity and expedite the labeling process by more than 50%?
- Performance: Does it have functionalities to boost the performance of your machine learning project?
- Limited data solution: Does it allow you to augment your limited defect data sets to allow for more accurate solution robustness?
- Scale: Does it enable users to deploy and manage thousands of models with minimal resources and adopt to hundreds of product variations?
- Rapid troubleshooting: Does the platform save time and resources by identifying issues in image quality in hours versus days or weeks?
Manufacturers can put the daunting data days behind them by finding a tool that bridges the data management gap and enables customers to create, deploy, manage and scale industrial AI solutions in one end-to-end platform.
Landing AI's Industrial Visual Inspection Platform has been purpose-built to bridge the data management gap, enabling customers to create, deploy, manage and scale industrial AI solutions with one easy to use end-to-end platform.
Authored by Alejandro Betancourt, Ph.D., Landing AI
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Landing AI
www.landing.ai