Quality Assurance

Assembly Line

Behind the Foldable Phones in Our Pockets

Linex Manufacturing overcomes inspection challenges

Startup’s Vision AI Software Trains Itself — in One Hour — to Detect Manufacturing Defects in Real Time

Date:

Author: Angie Lee

Topics: Visual Inspection, Quality Assurance, Defect Detection

Organizations: Covision Quality, NVIDIA

NVIDIA Metropolis member Covision creates GPU-accelerated software that reduces false-negative rates for defect detection in manufacturing by up to 90% compared with traditional methods. In addition to identifying defective pieces at production lines, Covision software offers a management panel that displays AI-based data analyses of improvements in a production site’s quality of outputs over time — and more.

“It can show, for example, which site out of a company’s many across the world is producing the best metal pieces with the highest production-line uptime, or which production line within a factory needs attention at a given moment,” Tschimben said.

Read more at NVIDIA Blog

High-Performance Machine Vision: Versatile lighting for subtle surface defects

Optimized quality control data keep the automotive supply chain flowing

Date:

Topics: metrology, quality assurance

Vertical: Automotive

Organizations: FARO Technologies, Taylor Metal Products

“What the FARO ScanArm allowed me to do was protect my company by proving to the customer that the issue started with their engineering print. With this particular issue, I provided a full layout to the customer with all of the profile call outs from the engineering drawing that showed where the issues were.”

Without FARO solutions and the more accurate data they provided, Taylor Metal Products might have been held financially responsible for these “no build conditions.” Thanks to the fact that the ScanArm was being used, however, Jason was able to “quickly address and correct these severe issues.”

“CAD is your perfect master; it can’t be refuted,” Jason explained. “The great thing about the FARO scans is that I can use color maps. One of the overseas manufacturers is really big about pulling those color maps because with the nature of our product, you’re taking a piece of metal and you’re bending it in different directions. The natural tendency of steel is to conform back to its original state. So, the stamping world is not like the machining world where you’re dealing with really tight tolerances, cutting and threading a hole, or boring out a hole. In the stamping world, you’re pushing metal. So that’s where the scans really come into play. The color maps show any deviation from CAD throughout the entire part. You can scan a profile with a fixed CMM, but it is a linear format, not 3D — and the CMM has to be programed to do this. With the FARO ScanArm after the CAD is locked in, it’s just one click to produce the color map. And the Japanese automotive manufacturers are big on using this technology.”

Read more at FARO Resource Library

Automation Within Supply Chains: Optimizing the Manufacturing Process

Quality assurance of sausage salad with 3 different inspection solutions

Industrializing Additive Manufacturing by AI-based Quality Assurance

Date:

Author: Axel Reitinger

Topics: additive manufacturing, quality assurance

Organizations: Siemens

At Siemens we are aiming to significantly improve quality assurance in Additive Manufacturing (AM) with industrial artificial intelligence and machine-learning to accelerate the time from prototype to industrialization as well as the efficiency in large-scale serial production.

Data of all print jobs are collected in a virtual private cloud (encrypted and secured by two-factor authentication), which facilitates the analysis and comparison across multiple print jobs and factory locations.

A profile of the severity scores of the final prototype can be used to define upper control limits for the serial production, which are then the basis for an automatic monitoring of the printing quality in the industrial phase. This could include, for example, the automatic creation of non-conformance reports (NCR).

The application calculates a severity score per printed part on the layer and additionally a severity score for the whole build plate. The severity score per part is calculated on the area of the bounding box of every single part, which helps to focus on those issues in the powder bed that can negatively impact the part’s quality. It allows a detailed monitoring of every part during the print process and is used by technical experts to evaluate if further Non-Destructive-Evaluation (NDE) of the finished part is required.

Read more at Siemens Ingenuity

Cost of Quality: Why A Compliance-Focused Model will Ultimately Limit Growth

Date:

Author: Denis J Devos

Topics: quality management system, quality assurance

Organizations: SafetyChain

Process manufacturers commonly consider numerous costs like labor, materials, and manufacturing and impact their bottom line. Often the same companies will overlook or undervalue the cost of quality, assuming that products that fall within specifications also meet quality targets. However, simply relying on conformance without examining the cost of quality can result in a few hidden expenses at best-and at worst, amount to considerable waste, negatively impacting the bottom line and subtracting from brand reputation.

Understanding the overall cost of quality is vital to addressing issues that signal costly or unsustainable variations and the potential for product or process failure. High incidences of failure erode capacity and make it impossible for companies to live up to their full potential.

Read more at SafetyChain Blog

Digital Part Inspection Software Creates New Business Opportunities

Date:

Author: @David__Lyell

Topics: quality assurance, digital manufacturing

Organizations: Chick Machine, ECI Software Solutions

Converting the shop to a digital inspection management system didn’t feel like an option but a necessity. “We have to evolve this capability or we will be left behind,” Bobby says. He knew that other shops had solved the inspection equation and felt confident that digital management was the solution to the shop’s bottlenecks. When speaking about the decision to purchase the shop’s first seat of the software, Bob says, “We liked auto ballooning and we also liked the data capture and reporting.” But these features were just the beginning; the capabilities of the software were far reaching and changed the culture of the shop.

Read more at Modern Machine Shop

How and Why Pharmaceutical Manufacturers Are Applying Artificial Intelligence

Date:

Author: David Greenfield

Topics: quality assurance, predictive maintenance

Vertical: Pharmaceutical

Organizations: AspenTech

“Opportunities to reduce manufacturing costs exist across all stages of the product lifecycle. Advanced analytics can reveal those opportunities, allowing pharma companies to take informed action to save money,” said Richard Porter, global director, pharmaceuticals, at AspenTech. “Whether using multivariate analytics to identify process degradation and its impact on quality or predicting final product quality to reduce lab testing lag times, these techniques offer pharmaceutical companies a competitive advantage.”

A purified water system at a pharmaceutical manufacturing facility.“The company tried to avoid batch losses—with each batch valued between $250,000-$300,000—as frequent shutdowns to replace the seals limited capacity,” said Porter. “As the company needed to ramp up capacity, it purchased two additional mills. Adopting Aspen Mtell, which connects to OPC UA supported devices, for predictive maintenance allowed the company to reduce supply chain disruptions from seal replacements and cut lifecycle maintenance costs by 60%. In addition, the company reduced capital expenditures and associated lifecycle maintenance costs by 50%.”

Read more at Automation World

Vision Cameras Inspect Disk Drive Assemblies

Date:

Author: Jim Camillo

Topics: quality assurance, machine vision

Vertical: Computer and Electronic

Organizations: Flexon Technology, Allied Vision

Once manufactured, an HDD is carefully fitted and sealed in a metal or plastic case. The case ensures that all drive components are perfectly secured in place and their mechanics work well over the lifetime of the product. It also protects the sensitive disks from dust, humidity, shock and vibration.

An HDD case must be defect-free and have perfectly machined thread holes to perform these functions, according to Somporn Kornwong, a manager at Flexon. In 2019 his company developed Visual Machine Inspection (VMI) for a manufacturer so it can quickly and thoroughly inspect each case it produces.

Read more at Assembly

Visual Inspection AI: a purpose-built solution for faster, more accurate quality control

Date:

Authors: Mandeep Wariach, Thomas Reinbacher

Topics: cloud computing, computer vision, machine learning, quality assurance

Organizations: Google

The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.

We built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. By combining ease of use with a focus on priority uses cases, customers are realizing significant benefits compared to general purpose machine learning (ML) approaches.

Read more at Google Cloud Blog

AI Vision for Monitoring Applications in Manufacturing and Industrial Environments

Date:

Topics: AI, quality assurance, machine vision, worker safety

Organizations: ADLINK

In traditional industrial and manufacturing environments, monitoring worker safety, enhancing operator efficiency, and improving quality assurance were physical tasks. Today, AI-enabled machine vision technologies replace many of these inefficient, labor-intensive operations for greater reliability, safety, and efficiency. This article explores how, by deploying AI smart cameras, further performance improvements are possible since the data used to empower AI machine vision comes from the camera itself.

Read more at Electronics Media

John Deere and Audi Apply Intel’s AI Technology

Date:

Author: David Greenfield

Topics: AI, quality assurance, robot welding, machine vision

Vertical: Agriculture, Automotive

Organizations: John Deere, Audi, Intel

Identifying defects in welds is a common quality control process in manufacturing. To make these inspections more accurate, John Deere is applying computer vision, coupled with Intel’s AI technology, to automatically spot common defects in the automated welding process used in its manufacturing facilities.

At Audi, automated welding applications range from spot welding to riveting. The widespread automation in Audi factories is part of the company’s goal of creating Industrie 4.0-level smart factories. A key aspect of this goal involves Audi’s recognition that creating customized hardware and software to handle individual use cases is not preferrable. Instead, the company focuses on developing scalable and flexible platforms that allow them to more broadly apply advanced digital capabilities such as data analytics, machine learning, and edge computing.

Read more at AutomationWorld

Machine learning optimizes real-time inspection of instant noodle packaging

Date:

Topics: AI, machine vision, quality assurance

Vertical: Food

Organizations: Beckhoff Automation

During the production process there are various factors that can potentially lead to the seasoning sachets slipping between two noodle blocks and being cut open by the cutting machine or being packed separately in two packets side by side. Such defective products would result in consumer complaints and damage to the company’s reputation, for which reason delivery of such products to dealers should be reduced as far as possible. Since the machine type upgraded by Tianjin FengYu already produced with a very low error rate before, another aspect of quality control is critical: It must be ensured that only the defective and not the defect-free products are reliably sorted out.

Read more at Beckhoff Blog

Tractor Maker John Deere Using AI on Assembly Lines to Discover and Fix Hidden Defective Welds

Date:

Author: Todd R. Weiss

Topics: AI, quality assurance, machine vision, robot welding, arc welding

Vertical: Agriculture

Organizations: John Deere, Intel

John Deere performs gas metal arc welding at 52 factories where its machines are built around the world, and it has proven difficult to find defects in automated welds using manual inspections, according to the company.

That’s where the successful pilot program between Intel and John Deere has been making a difference, using AI and computer vision from Intel to “see” welding issues and get things back on track to keep John Deere’s pilot assembly line humming along.

Read more at EnterpriseAI

AI In Inspection, Metrology, And Test

Date:

Authors: Susan Rambo, Ed Sperling

Topics: AI, machine learning, quality assurance, metrology, nondestructive test

Vertical: Semiconductor

Organizations: CyberOptics, Lam Research, Hitachi, FormFactor, NuFlare, Advantest, PDF Solutions, eBeam Initiative, KLA, proteanTecs, Fraunhofer IIS

“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”

That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”

Read more at Semiconductor Engineering

AI tool locates and classifies defects in wind turbine blades

Date:

Topics: AI, defect detection, quality assurance

Vertical: Electrical Equipment

Organizations: Railston & Co, Loughborough University

Using image enhancement, augmentation methods and the Mask R-CNN deep learning algorithm, the system analyses images, highlights defect areas and labels them.

After developing the system, the researchers tested it by inputting 223 new images. The proposed tool is said to have achieved around 85 per cent test accuracy for the task of recognising and classifying wind turbine blade defects.

Read more at The Engineer

Transforming quality and warranty through advanced analytics

Date:

Topics: manufacturing analytics, quality assurance

Organizations: McKinsey

For companies seeking to improve financial performance and customer satisfaction, the quickest route to success is often a product-quality transformation that focuses on reducing warranty costs. Quality problems can be found across all industries, and even the best companies can have weak spots in their quality systems. These problems can lead to accidents, failures, or product recalls that harm the company’s reputation. They also create the need for prevention measures that increase the total cost of quality. The ultimate outcomes are often poor customer satisfaction that decreases top-line growth, and additional costs that damage bottom-line profitability.

To transform quality and warranty, leading industrial companies are combining traditional tools with the latest in artificial-intelligence (AI) and machine-learning (ML) techniques. The combined approach allows these manufacturers to reduce the total cost of quality, ensure that their products perform, and improve customer expectations. The impact of a well-designed and rigorously executed transformation thus extends beyond cost reduction to include higher profits and revenues as well.

Read more at McKinsey

Smart quality in advanced industries

Date:

Authors: Álvaro Carpintero, Ulrich Huber, Evgeniya Makarova, and Heiko Nick

Topics: quality assurance

Organizations: McKinsey

Technological advancements have enabled a fundamentally new way of delivering quality. Under this approach, companies view the quality function as a partner and coach that delivers value, not just a business cost. This perspective helps them integrate quality and compliance into regular operations while enabling speed and effectiveness.

Read more at McKinsey

Strategic Analytics Help Intertape Polymer Shrink Inefficiencies

Date:

Author: Peter Fretty

Topics: cloud computing, quality assurance

Vertical: Plastics and Rubber

Organizations: Intertape Polymer Group, Sight Machine

For Intertape Polymer Group (IPG), a global manufacturer of packaging and protective solutions for industrial and e-commerce applications, the digital transformation process has always been about embracing technology with a keen eye on extracting the overall business value. As such, IPG is currently at different levels of maturity across the portfolio of digital technology deployments, including additive manufacturing, AR/VR training, IoT-based predictive downtime and robotic process automation.

IPG has taken advantage of the unique data modeling capabilities of the Sight Machine platform, which continuously transforms all data types generated by factory equipment and manufacturing software into a robust data foundation for analyzing and modeling a plant’s machines, production processes and finished products.

Read more at IndustryWeek

AWS Announces General Availability of Amazon Lookout for Vision

Date:

Topics: cloud computing, computer vision, machine learning, quality assurance

Organizations: AWS, Basler, Dafgards, General Electric

AWS announced the general availability of Amazon Lookout for Vision, a new service that analyzes images using computer vision and sophisticated machine learning capabilities to spot product or process defects and anomalies in manufactured products. By employing a machine learning technique called “few-shot learning,” Amazon Lookout for Vision is able to train a model for a customer using as few as 30 baseline images. Customers can get started quickly using Amazon Lookout for Vision to detect manufacturing and production defects (e.g. cracks, dents, incorrect color, irregular shape, etc.) in their products and prevent those costly errors from progressing down the operational line and from ever reaching customers. Together with Amazon Lookout for Equipment, Amazon Monitron, and AWS Panorama, Amazon Lookout for Vision provides industrial and manufacturing customers with the most comprehensive suite of cloud-to-edge industrial machine learning services available. With Amazon Lookout for Vision, there is no up-front commitment or minimum fee, and customers pay by the hour for their actual usage to train the model and detect anomalies or defects using the service.

Read more at Business Wire

Analysing fruit data in the supply chain has never been more important for business efficiency

Date:

Author: Matt Russell

Topics: machine vision, quality assurance

Vertical: Food

Organizations: Tomra

Fruit and production data can be used in ways that it has never been done before to improve a company’s efficiency and boost profits, according to global packhouse equipment and automation supplier Tomra Food.

He added that there are several different useful data types at play in a packhouse; production and traceability level data, performance level data, quality data and auditing data. This data can be used to optimise the supply chain and can be used to make decisions and directions in terms of the next big thing that needs to be done. But consumer trends will constantly change the requirements of automation.

Read more at HortiDaily.com

Pushing The Frontiers Of Manufacturing AI At Seagate

Date:

Author: Tom Davenport

Topics: AI, machine learning, predictive maintenance, quality assurance

Vertical: Computer and Electronic

Organizations: Seagate

Big data, analytics and AI are widely used in industries like financial services and e-commerce, but are less likely to be found in manufacturing companies. With some exceptions like predictive maintenance, few manufacturing firms have marshaled the amounts of data and analytical talent to aggressively apply analytics and AI to key processes.

Seagate Technology, an over $10B manufacturer of data storage and management solutions, is a prominent counter-example to this trend. It has massive amounts of sensor data in its factories and has been using it extensively over the last five years to ensure and improve the quality and efficiency of its manufacturing processes.

Read more at Forbes