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In today’s highly competitive global market, winning requires shipping a product with near-perfect quality. Although most mature organizations manage to operate their manufacturing processes at very low defect rates, customers expect products that are entirely defect-free. Hence, the ability to accurately detect defected products before they hit the market, while maintaining high production rates, is a core competency that enterprises should possess.
Prompt detection of rare defect incidents is not only an issue of paramount importance: it also creates an opportunity for manufacturers to raise the bar on their quality standards. Defect detection is a vivid example of the potential value that can be unleashed by implementing AI technologies in the manufacturing industry. When implemented properly, AI defect detection tools are vastly superior to manual inspection in tracking products on the assembly line, delivering significantly higher precision rates, enhanced product quality, increased productivity, higher throughput and lower productions costs.
In the manufacturing process of mechanical products, certain levels of defects are inevitable. These defects may include undesired holes, pits, abrasions, and scratches on various pieces that exit the assembly line. Defects may originate from design failures, faulty production equipment, metal fatigue and unfavorable working conditions – or any interplay between these factors. Regardless of the source of the defect, defected components spike production costs, degrade product quality, shorten product lifespan, hamper customer satisfaction and result in an extensive waste of resources. In some scenarios, faulty items can even harm people and put lives at risk (think of the airline industry as an example).
Defect detection is therefore a core part of any manufacturing quality control and assurance processes. Traditionally, defect detection was executed manually by human inspectors, naturally prone to fatigue, inattentiveness and biases. Later, manual inspection was augmented by rule-based machine vision technologies. Over the past decade defect detection has become increasingly technology-driven, building on advancements in artificial intelligence and big data. The use of smart cameras and related AI-enabled systems is already helping manufacturers deliver high quality inspection in shorter cycles, reducing latency and costs, and setting new standards that are far beyond the capabilities of even the most experienced human inspectors.
AI solutions used for quality control utilize Machine Learning and defect prediction models to autonomously learn and make inferences from the manufacturer’s data. Rather than relying on experts’ rules like their older machine vision counterparts, these models learn on their own which features are important — and create new implicit rules that determine which combinations of features impact overall product quality. Autonomous manufacturing defect detection systems deliver improved efficiency and accuracy, constantly adjusting themselves to detect new types of defects across industries and verticals. From nanometric semiconductors to huge engine parts of a commercial airplane – production yield and customer satisfaction greatly depend on AI-based quality control.
There are several applications of defect detection in Industry 4.0, but the most dominant by far is AI-driven visual inspection. Sensor-based quality control technology enables to generate, process, and interpretate data used for real-time quality assurance without interrupting the production flow. The visual inspection of the product is conducted by a set of sensors, cameras and scanners. This early detection reduces the waste incurred by defected parts, minimizing scrap and rework. In addition, the sensor data can be used to guide rework efforts required in case of product damages, and increase rework efficiency. Other use cases supported by defect detection technology include defect analysis, defect prevention, and defect testing.
AI-based defect detection systems provide significant benefits for manufacturers:
These benefits of defect detection and other Industry 4.0 applications are estimated by McKinsey to create a potential value of $3.7 trillion in 2025 for manufacturers and suppliers. Given these prospects and the general buzz around industry 4.0, it’s no surprise that 70% of companies have already started to pilot Industry 4.0 solutions. At the same time, this same study found that only about 30% percent of companies today capture value from Industry 4.0 solutions at scale.
The main hurdles of bringing AI-driven manufacturing solutions to production and generating positive ROI can stem from business related issues, technical issues, and their intersection – leaving the majority of companies stuck in “pilot purgatory.”
In addition, AI implementation in production at scale is still at a stage where neither the build nor most buy options provide working solutions for companies seeking AI transformation. That’s because, on the one hand, every AI problem, scenario and use case is completely unique, requiring minute customization. Defect detection is a fundamentally different task for manufacturers of apparel, automotive or consumer electronics, for example, based on different data and driving different production and business needs. For most companies and use cases, this rules out most off the shelf verticalized or use case based solutions that lack the specificity required to achieve significant business and ROI impact in the long term.
On the other hand, building AI technology in-house requires cutting-edge domain expertise. Enterprises attempting to build AI solutions in-house often end up amassing huge costs and an extremely long time to value on their AI projects, which, depending on scope, take anything between one and three years to achieve — if success in production does indeed ensue.
This AI in production Catch-22 is demonstrated by the massive 87% failure rate of enterprise AI projects in reaching production, according to Venturebeat.
The BeyondMinds Enterprise platform is utilized by manufacturing, energy, heavy-infrastructure, and logistics companies to transition core business processes, such as defect detection, to human-machine automation, adapting to the specific business and data needs of each enterprise. The platform automatically scans for product defects to reduce waste, ensuring product quality and maximizing productivity. BeyondMinds’ Self-Supervised Image Representation module provides unparalleled detection abilities and allows efficient onboarding of new defects, enabling enterprises to build and deploy hyper-customized AI-based defect detection solutions that generate tangible and sustainable business impact within just weeks.
The BeyondMinds platform supports numerous defect detection AI solutions, all of which are built on top of the patform and untilize its unique technology infrastructure and capabilities. Each defect detection solution is tailored to specific business needs of our enterprise customers, using their individual production data as inputs. These solutions meet the following criteria:
Customized. Every company’s unique use cases and specifications are built into every BeyondMinds solution, ensuring that it will provide a perfect fit for data, usability, and business needs.
Production-grade. The BeyondMinds Enterprise platform is a robust, end-to-end state of the art AI platform — that goes far beyond a simple trained model and provides a complete working system.
AI-as-a-Service. The BeyondMinds AI platform is provided as a service (AI as a Service) to ensure continuous improvement and increasing value over time, enabling manufacturers to build, deploy, run and maintain their AI-based technologies fast.
BeyondMinds’ mission is to create AI partnerships that enable the world’s most sophisticated companies to benefit quickly and enduringly from AI’s benefits, while freeing themselves of the risks and burdens of its development and ongoing maintenance.
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