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The Value of AI-Based Visual Inspection in 2020

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For over a decade, manufacturers have turned to automated solutions to improve their bottom line. Automation and machine vision are now being augmented and even replaced by AI. Here is the value of AI-based visual inspection in 2020.

Value of AI-Based visual inspection

Being replaced by AI is especially true when it comes to visual inspection. The use of AI-based visual inspection technology is transforming manufacturing’s ability to improve business operations.

AI-based visual inspection relies on two of AI’s main strengths: computer vision and deep learning. Every AI system is built with the core capacity to perceive its environment (computer vision) and act on those perceptions (deep-learning).

As a result of deep-learning, AI adapts to a range of environments, making it useful across a multitude of industries. It has unlimited potential and can be developed rapidly to meet a manufacturer’s needs.

Concept of AI-based visual inspection

Well-trained human eyes can detect defects. A well-trained AI-based vision system can do the same — but with greater efficiency. Like a human eye, AI-based vision systems capture an image and send it to a central “brainâ€� for processing.

Like a human brain, an AI “brain� makes detailed meaning from the image by contrasting it against its existing knowledge.

AI-based vision systems are made of two integrated components. A sensing device acts as an “eye,� while a deep learning algorithm acts as a “brain.� The integrated system successfully mimics the human eye-brain ability to interpret images.

AI-based vision systems are more efficient than human eyes because the AI “brain� stores greater amounts of information.

Robust computational power can parse through available data at rapid speeds. The system can classify objects in both photos and videos and perform complex visual perception tasks.

AI-based vision systems can search images and captions, detect objects, and classify multi-media.

Thanks to deep learning-based visual processing, AI-based visual inspection systems can perceive cosmetic flaws and detect defects across general or conceptual surfaces (mobidev dot biz).

Benefits of AI-based visual inspection

1. Fast Implementation

Decades-old automated systems depend on defect libraries, lists of exceptions and complicated filters. The time it takes to accrue this information, clean it for accuracy, and re-implement it decreases its efficacy. It also wastes labor.

AI and deep learning do not require prolonged programming or tediously lengthy algorithms. AI-based visual inspection systems might be constructed by several quality engineers and a dataset of training images. The system learns rapidly and is integrated over several weeks.

2. Improved Analytics and Quality Control

Manufacturers can use AI to document inspection results and to assess product quality. Some overall process improvement initiative metrics that can be successfully tracked and correlated with concrete vision data include:

  • process recipes
  • equipment differences
  • component suppliers
  • factory location

In addition, inspection images and results can also be tracked and documented. These initiatives prevent future failure, which saves time and additional production costs. Applying deep learning-based machine vision across all initiatives and inspections helps manufacturers recognize and address defects early.

3. Labor Costs Reduction

AI solutions have higher rates of consistency than most expert human inspectors. Human inspectors must be trained and are only able to maintain a high degree of focus for 15-20 minutes at a time. Labor costs are incurred yearly and staff turn-over is an issue. For these reasons, AI-based vision inspections are more cost-effective than manual labor.

Use Cases

AI is increasing the competitiveness of manufacturers across every industry. Here are recent use cases from the aviation industry, semi-conductor manufacturing sector, and bio-science.

Alibaba has risen to meet healthcare challenges created by the coronavirus. Alibaba’s deep-learning-based visual recognition system is capable of detecting the coronavirus in chest CT scans at a 96% accuracy rate. The system accessed 5,000 COVID-19 cases and can provide a diagnosis within 20 seconds. Moreover, the system can differentiate between images of viral pneumonia and images of coronavirus.

Fujitsu Laboratories implemented an Image Recognition System at Fujitsu’s Oyama factory. The system ensures that parts are produced at optimal quality levels by supervising the assembly process. The system was so successful that Fujitsu implemented it across the entirety of the company’s production sites.

Airbus introduced an automated, drone-based aircraft inspection system in 2018. The system has improved the quality of inspections and reduced aircraft downtime.

GlobalFoundries is a leader in semiconductor manufacturing. The company designed a visual inspection system that detects defects in a scanning electron microscope (SEM) images. The system detects defects in a wafer map which then helps to determine the semiconductor device’s performance.

The use cases listed above reveal the extent to which AI is capable of automating many aspects of our lives. Although AI vision will never replicate human vision, the technology continues to classify information and advance in ways human eyes and brains cannot. And only humans might consider how to use this technology to get advantages.

The post The Value of AI-Based Visual Inspection in 2020 appeared first on ReadWrite.

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manufacturing Operate workforce management

Can Other Industries Replicate MyWorkChoice’s Manufacturing Overhaul?

Automation is often seen as manufacturers’ ticket to the future. Lately, however, it’s a new labor model that has been drawing jealous stares from other sectors.

Upending how manufacturers get work done are platforms like MyWorkChoice, an hourly workforce management platform. MyWorkChoice uses smart matching technology to build custom communities, onboarded according to each client’s specifications.

By letting workers choose their shifts, MyWorkChoice cuts manufacturers’ absenteeism rates from around 35 percent to just 3 percent. For plant output, managers’ stress levels, and company culture, that’s a night-and-day difference.

The question: Could MyWorkChoice’s model work for other industries? Maybe so — but only if they can fit two puzzle pieces that, frustratingly, never seem to go together: flexibility and dependability.

Engineered for Flexibility

For years, many white-collar workers have enjoyed the flexibility to work when and where they want. Until recently, however, blue-collar sectors like manufacturing have pigeonholed workers into 40-hour-per-week arrangements.

Understandably, blue-collar workers want those same freedoms. What broke the dam, according to MyWorkChoice CEO Tana Greene, is the coronavirus pandemic.

“The traditional staffing agency model is dead,� explains Greene. “Prior to the pandemic, flexibility was supporting both workers and companies who needed to scale their workforce at any given time; today it’s a critical piece of the puzzle to keep our supply chain moving and put healthy people to work.�

Instead of requiring a rigid schedule, MyWorkChoice lets the company’s regular workforce sign up for four-hour blocks on the days they choose. Many opt to work a full 40 hours per week, producing a dependable primary workforce.

Workers understand that MyWorkChoice isn’t just another day-labor app, nor is it a split-shift system. They stick with it because of its flexibility, enabling the companies they serve to build tenured hourly teams.

Could sectors outside of manufacturing make that model work? Sure, Greene says: MyWorkChoice has applied it to call centers, distribution centers, and more. But before they make the leap, they need to solve for the second part of the equation: dependability.

Solved with Scale

Shorter shifts are a big reason why MyWorkChoice delivers workers reliably. But there’s a second factor that, for industries looking to follow the manufacturing sector’s lead, may be more difficult to replicate: scale.

Largely because of the flexibility it offers workers, MyWorkChoice is the largest recruiter in most markets it operates in. That ensures it has the bench strength to make the model work, covering gaps in a company’s regular workforce. Access to a larger, scalable workforce creates a secondary line of defense while eliminating the need for overtime.

A larger labor pool, combined with shorter shifts, lets employers accommodate all sorts of life situations. The result is MyWorkChoice’s third talent stream: nontraditional workforce segments, such as seniors and college students, who wouldn’t otherwise look for manufacturing work.

Many of these MyWorkChoice workers have responsibilities outside the platform that would make it tough to work longer shifts. Its model makes tough-to-fill times less onerous. For example, the second shift is typically the most difficult to fill, but when flexible four-hour slots are made available, stay-at-home parents and second incomers flock to this shift, making it one of the most coveted on the platform.

In the industries MyWorkChoice operates in — manufacturing, call centers, and warehousing — that three-pronged approach proves flexibility works. In more niche ones, it may not.

Take surveying. According to the U.S. Bureau of Labor Statistics, there are fewer than 50,000 surveyors in the entire U.S. Because it’s a specialized field, no amount of flexibility could help employers build secondary and tertiary teams. In all but the largest of labor markets, there simply aren’t enough surveyors to go around.

Making Flexible Work Work

Plenty of industries struggle to fill open positions, despite the economic downturn. Plenty of workers in them want flexibility. So what can employers in other sectors do to marry the two?

One option is to transition hourly workers to an employer-of-record model. Because MyWorkChoice is the employer of record, it handles worker’s compensation, unemployment claims, and other back-office matters that employers otherwise have to deal with.

The other option is to bring new demographic groups into the fold. Look for ways to increase flexibility: If possible in your industry, consider making work-from-home options permanent. If not, perhaps you could give workers more choice over their hours.

The final ingredient? Client service. MyWorkChoice provides regional managers to its clients, ensuring that workers are happy, safe, and getting the job done.

Without someone on the ground who knows the rules, no amount of software can make workforce management a hands-off process in industries like manufacturing. Technology can make matches, but it takes a human being to make sure those matches actually work out.

Great client service exists in every industry, as do flexibility and reliable workforces. In manufacturing, what MyWorkChoice has done is put them together in a lasting, harmonious way. Whether that can be done in every industry, however, is a challenge waiting to be conquered.

The post Can Other Industries Replicate MyWorkChoice’s Manufacturing Overhaul? appeared first on ReadWrite.