Quality Assurance
Assembly Line
The Role of AI-Powered Machine Vision Systems in Textile Quality Control
Integrating AI with machine vision enables systems to learn from past data and improve defect detection over time. For example, Robro Systems’ Kiara Web Inspection System (KWIS) uses AI-driven algorithms to enhance detection capabilities, adapting to new defect patterns that may emerge during production.
With KWIS, saw a 25% improvement in defect detection accuracy compared to manual inspection methods. For instance, in a batch of conveyor belt fabric, the system detected micro-tears that manual inspection would have missed, allowing to correct the issue early and avoid downstream quality failures. This reduced our material waste and ensured that only high-quality products reached our customers.
Implementing machine vision technology has also translated into significant cost savings for manufacturers. According to a study by the International Journal of Advanced Manufacturing Technology, machine vision can reduce defect-related production costs by up to 30%. For manufacturers, this has meant reducing the costs associated with rework and waste and minimizing customer returns and complaints.
Using AI to Detect Faulty Crimps
Crimp force monitoring (CFM) has long been the standard for fault detection in wire assemblies. The technique can reliably detect many defects, including wrong strip length, missing strands, wrong wire cross section, wrong terminal, inconsistent terminal material, insulation in the crimp, wrong insertion depth, and wrong crimp height.
We propose a fault detection system that employs AI with regional selective data scaling (RSDS). RSDS generates synthetic abnormal data from reference data by performing upscaling or downscaling on specific regions of the data. This allows the fault detection system to efficiently train an AI model with a dataset comprised exclusively of normal operational data and still achieve high accuracy in detecting faults.
In this study, a multilayer perceptron (MLP) classification model was trained exclusively on normal data and was able to effectively distinguish between normal and abnormal conditions. To validate the system, 15 unique raw datasets from a real-world wire harness manufacturing facility were collected and tested with four anomaly detection algorithms: Isolation Forest, one-class autoencoders, k-means, and Histogram-Based Outlier Score (HBOS).
Big Changes In Optical Inspection
AM-QUALITY - The world's first in-line quality control solution
New Technique Improves Finishing Time for 3D Printed Machine Parts
North Carolina State University researchers have demonstrated a technique that allows people who manufacture metal machine parts with 3D printing technologies to conduct automated quality control of manufactured parts during the finishing process. The technique allows users to identify potential flaws without having to remove the parts from the manufacturing equipment, making production time more efficient. Specifically, the researchers have integrated 3D printing, automated machining, laser scanning and touch-sensitive measurement technologies with related software to create a largely automated system that produces metal machine components that meet critical tolerances.
When end users need a specific part, they pull up a software file that includes the measurements of the desired part. A 3D printer uses this file to print the part, which includes metal support structures. Users then take the printed piece and mount it in a finishing device using the support structure. At this point, lasers scan the mounted part to establish its dimensions. A software program then uses these dimensions and the desired critical tolerances to guide the finishing device, which effectively polishes out any irregularities in the part. As this process moves forward, the finishing device manipulates the orientation of the printed part so that it can be measured by a touch-sensitive robotic probe that ensures the part’s dimensions are within the necessary parameters.
AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding
Online welding quality monitoring (WQM) is crucial for intelligent welding, and deep learning approaches considering spatiotemporal features for WQM tasks show great potential. However, one of the important challenges for existing approaches is to balance the spatiotemporal representation learning capability and computational efficiency, which makes it challenging to adapt welding processes with complex and drastic molten pool dynamic behavior. This paper proposes a novel approach for WQM using molten pool visual sensing and deep learning considering spatiotemporal features, the proposed deep learning network called attention fusion based frame-temporality two-stream network (AF-FTTSnet). Firstly, a passive vision sensor is used to acquire continuous dynamic molten pool images. Meanwhile, temporal difference images are computed to provide novel features and temporal representations. Then, a two-stream feature extraction module is designed to concurrently extract rich spatiotemporal features from molten pool images and temporal difference images. Finally, an attention fusion module with the ability to automatically identify and weight the most relevant features is designed to achieve optimal fusion of the two-stream features. The shop welding experimental results indicate that the proposed AF-FTTSnet model can effectively and robustly recognize five typical welding states during helium arc welding, with an accuracy of 99.26%. This model has been demonstrated to exhibit significant performance improvements compared to mainstream temporal sequence models.
AI-powered 3D inspection system for factory automation - In-Sight L38 Series from Cognex
Inside Boeing’s Quality Control Process for 737 Max Planes
Product inspection of coffee beans
High Speed Dual View X-ray Inspection of Cans
AI Driven Vision Inspection Automation for Engine Tappets
How AI helps this contract manufacturer to stand out on product quality
Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning
Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000 µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. We also introduce for the first time in the AM in situ sensing literature the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the belief of the authors that future studies should adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.
Cone Ice cream Inspection using Machine Vision
🧠⏳ Multi-layer parallel transformer model for detecting product quality issues and locating anomalies based on multiple time‑series process data in Industry 4.0
Smart manufacturing systems typically consist of multiple machines with different processing durations. The continuous monitoring of these machines produces multiple time-series process data (MTPD), which have four characteristics: low data value density, diverse data dimensions, transmissible processing states, and complex coupling relationships. Using MTPD for product quality issue detection and rapid anomaly location can help dynamically adjust the control of smart manufacturing systems and improve manufacturing yield. This study proposes a multi-layer parallel transformer (MLPT) model for product quality issue detection and rapid anomaly location in Industry 4.0, based on proper modeling of the MTPD of smart manufacturing systems. The MLPT consists of multiple customized encoder models that correspond to the machines, each using a customized partition strategy to determine the token size. All encoders are integrated in parallel and output to the global multi-layer perceptron layer, which improves the accuracy of product quality issue detection and simultaneously locates anomalies (including key time steps and key sensor parameters) in smart manufacturing systems. An empirical study was conducted on a fan-out, panel-level package (FOPLP) production line. The experimental results show that the MLPT model can detect product quality issues more accurately than other methods. It can also rapidly realize anomalous locations in smart manufacturing systems.
📱 Inside the Factory Where Robots Are Building Your Next Samsung Phone
The sound of bots whirring, air gaskets blowing and mechanical arms shifting positions can be heard throughout the facility. Every once in a while, an autonomous robot will play a cute jingle to signal its arrival. These robots, known as AGVs (for automated guided vehicles), roam the factory floor shuttling materials to their designated stations, guided by aluminum tracks on the floor. I’m told there are 80 of the bots in the company’s Gumi facility, where phones like the Galaxy S23 and the new Galaxy Z Flip 5 are assembled.
A large portion of the assembly line is dedicated to quality checks. Samsung says there are about 30,000 to 50,000 inspection items for the Galaxy S23 lineup alone. That includes the S Pen connection; the charging port; near-field communication functionality (or NFC, the tech that powers contactless payments); touch screen panels; fingerprint sensors; cameras; speakers; the SIM card tray; and Wi-Fi connections. There are also checkpoints within the assembly line for chips that enable millimeter wave 5G connections and ultra wideband, the proximity-sensing tech that enables phones to more easily share files and to function as digital car keys.
Bulk handling system cuts dust, improves accuracy at graphite plant
Asbury Graphite & Carbons is one of the largest global processors of graphite and other carbon materials used in the plastics, automotive, lubrication, powder metallurgy, petroleum and coatings industries. Its European installation in the Netherlands opened in 2014 to take in raw graphite from around the world, reduce it into fine particles through a variety of milling and screening processes and fill 2,200 lb bulk bags and smaller bags, based on customer needs.
The plant operators had experienced problems with inaccurate fill weights of milled graphite, as well as issues with dust control. The bulk bag filler frames operated with a poorly designed bag spout seal that wasn’t reliable. “Very often, the seal inflated incorrectly or wasn’t strong enough or exploded,” Stassen said. As a result, dust and fine particles escaped, putting the plant’s compliance with Dutch health and safety guidelines at risk. Spills were also occurring with the original bulk bag dischargers. “We had to do something else,” Stassen said.
On the recommendation of Dutch distributor Matec Techniek, the company turned to Flexicon (Europe) Ltd., which specializes in bulk bag filling and discharging systems. “We tried one bulk bag filling station, and that reduced our dust big time,” Stassen said. “So we chose to go forward with Flexicon for all 11 stations, followed over the years by nine bulk bag dischargers and numerous flexible screw conveyors. They reduced dust tremendously in the plant.”
IBM and AWS partnering to transform industrial welding with AI and machine learning
IBM Smart Edge for Welding on AWS utilizes audio and visual capturing technology developed in collaboration with IBM Research. Using visual and audio recordings taken at the time of the weld, state-of-the-art artificial intelligence and machine learning models analyze the quality of the weld. If the quality does not meet standards, alerts are sent, and remediation action can take place without delay.
The solution substantially reduces the time between detection and remediation of defects, as well as the number of defects on the manufacturing line. By leveraging a combination of optical, thermal, and acoustic insights during the weld inspection process, two key manufacturing personas can better determine whether a welding discontinuity may result in a defect that will cost time and money: weld technician and process engineer.
Behind the A.I. tech making BMW vehicle assembly more efficient
🧠📹 What Sets Toshiba’s Ceramic Balls Apart? The AI Quality Inspection System
Bearings cannot be easily replaced once a vehicle is assembled. In the U.S., bearings used in EVs are expected to be of high enough quality to withstand long distances. One issue that can occur with EVs, however, is the “electric corrosion” of the bearings that mount the various vital parts of the vehicle onto the motor—a serious issue, as it can lead to the breakdown of the vehicle. High-performance bearings would drive the widespread use of EVs, and contribute to the push towards carbon neutrality. The electrical corrosion phenomenon had hampered these efforts, but not anymore—therein lies the beauty of Toshiba’s ceramic balls.
“Our ceramic balls go through slight changes about every year and a half due to changes in material and other factors. To keep up the accuracy of the quality inspections, we have to continually update the AI system itself. The MLOps system automates that process,” says Kobatake.
“We’ve been able to dramatically reduce the time spent on these inspections. Ceramic balls are expensive compared to their metal counterparts. They have so many different strengths, and yet they haven’t been able to replace the metal ones precisely because of this particular issue. If we’re able to reduce the cost through AI quality inspection, we’ll be able to lower the price of the products themselves,” says Yamada.
🖨️ Visual quality control in additive manufacturing: Building a complete pipeline
In this article, we share a reference implementation of a VQC pipeline for additive manufacturing that detects defects and anomalies on the surface of printed objects using depth-sensing cameras. We show how we developed an innovative solution to synthetically generate point clouds representing variations on 3D objects, and propose multiple machine learning models for detecting defects of different sizes. We also provide a comprehensive comparison of different architectures and experimental setups. The complete reference implementation is available in our git repository.
The main objective of this solution is to develop an architecture that can effectively learn from a sparse dataset, and is able to detect defects on a printed object by controlling the surface of the printed object each time a new layer is added. To address the challenge of acquiring a sufficient quantity of defect anomalies data for accurate ML model training, the proposed approach leverages a synthetic data generation approach. The controlled nature of the additive manufacturing process reduces the presence of unaccounted exogenous variables, making synthetic data a valuable resource for initial model training. In addition to this, we hypothesize that by deliberately inducing overfitting of the model on good examples, the model will become more accurate in predicting the presence of anomalies/defects. To achieve this, we generate a number of normal examples with introduced noise in a ratio that balances the defects occurrence expected during the manufacturing process. For instance, if the fault ratio is 10 to 1, we generate 10 similar normal examples for every 1 defect example. Hence, the pipeline for initial training consists of two modules: the synthetic generation module and the module for training anomaly detection models.
World’s Leading Electronics Manufacturers Adopt NVIDIA Generative AI and Omniverse to Digitalize State-of-the-Art Factories
More than 50 manufacturing giants and industrial automation providers — including Foxconn Industrial Internet, Pegatron, Quanta, Siemens and Wistron — are implementing Metropolis for Factories, NVIDIA founder and CEO Jensen Huang announced during his keynote address at the COMPUTEX technology conference in Taipei.
Supported by an expansive partner network, the workflow helps manufacturers plan, build, operate and optimize their factories with an array of NVIDIA technologies. These include NVIDIA Omniverse™, which connects top computer-aided design apps, as well as APIs and cutting-edge frameworks for generative AI; the NVIDIA Isaac Sim™ application for simulating and testing robots; and the NVIDIA Metropolis vision AI framework, now enabled for automated optical inspection. NVIDIA Metropolis for Factories is a collection of factory automation workflows that enables industrial technology companies and manufacturers to develop, deploy and manage customized quality-control systems that offer a competitive advantage.
AI vision for print quality inspection on bottles
🦾 Factory Visit: Investment bankers tour client’s robot-filled machine shop
“Many shops can’t get the parts out because their quality control has gone from four days to six weeks. They just don’t have the staff and it becomes a major bottleneck in the company,” Dave Henderson explains. New Scale’s Q-Span workstation is a robotic arm that has grippers on the end that can pick up parts and then measure them using an automated dimensional gauging system.
“We saw a need for lower cost, easier to use, less risky, and more flexible automation to allow small- and medium-size enterprises to leverage automation just as the big guys have for decades,” according to Josh Pawley. “The shortage of welders and skilled fabricators is the biggest driver of our business,” says Pawley. “It’s largely the nature of welding – it’s dull, dirty and dangerous in many cases. There are not a lot of folks going into the space, and the average age of a welder is in the late 50s. But the most dull and dirty jobs can be supplemented with automation.”
“Incremental automation is very important, the ability to break it down into step-by-step pieces,” Henderson says. “We consistently get requests from people who were thinking of heavy integration, but they haven’t had any automation before, and they wanted a turnkey system which cost $1 million and take a year to implement. “But traditional automation for some fabricators is too much to jump into to begin with. We can get them up and running in three months for $100,000. By doing that you empower your staff to operate machines, as opposed to having turnkey systems that are dependent on the system integrator. So you get the best out of both automation and your people.”
Industrial defect detection at the edge
Building a Visual Quality Control solution in Google Cloud using Vertex AI
In this blog post, we consider the problem of defect detection in packages on assembly and sorting lines. More specifically, we present a real-time visual quality control solution that is capable of tracking multiple objects (packages) on a line, analyzing each object, and evaluating the probability of a defect or damaged parcel. The solution was implemented using Google Cloud Platform (GCP) Vertex AI platforms and GCP AutoML services, and we have made the reference implementation available in our git repository. This implementation can be used as a starting point for developing custom visual quality control pipelines.
How Corning Built End-to-end ML on Databricks Lakehouse Platform
Specifically for quality inspection, we take high-resolution images to look for irregularities in the cells, which can be predictive of leaks and defective parts. The challenge, however, is the prevalence of false positives due to the debris in the manufacturing environment showing up in pictures.
To address this, we manually brush and blow the filters before imaging. We discovered that by notifying operators of which specific parts to clean, we could significantly reduce the total time required for the process, and machine learning came in handy. We used ML to predict whether a filter is clean or dirty based on low-resolution images taken while the operator is setting up the filter inside the imaging device. Based on the prediction, the operator would get the signal to clean the part or not, thus reducing false positives on the final high-res images, helping us move faster through the production process and providing high-quality filters.
Ultrasound Inspection Optimizes EV Battery Manufacturing
Battery cell inspection technology has been neglected in favor of other innovation categories. According to a recent MIT study, inspection has not been a factor in previous price declines and therefore increased use of cell interrogation should not come as a surprise. Seemingly this would not require an engineering leap. After all, ‘borrowed technology’ from previous chemistries and other industries has worked well enough in the past.
However, large-format cells have proven to be far more difficult to manufacture at scale, compared to their small-format counterparts that have dominated the market until recently. This difficulty is in part manifested by industry-wide low manufacturing yields. Based on reports and interviews with industry insiders, it can be estimated that large-format battery yield is somewhere between 70–90% with a ramp period of five years to reach steady state yield for a new production run.
Titan Advanced Energy Solutions (‘Titan’) is one of the companies working to meet a growing demand for better inspection technology. Their ultrasound sensing technology combined with a system-based approach to manufacturing provides early and actionable feedback to the manufacturing floor, positively impacting yield and scrap rates as well as overall cell production economics. Moreover, their scan-as-a-service business model does not burden customers with additional capital expense.
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
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.
High-Performance Machine Vision: Versatile lighting for subtle surface defects
Optimized quality control data keep the automotive supply chain flowing
“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.”
MIRAI Case: Micropsi Industries Automates Leak Testing with Intelligent Complete solution
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
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.
Cost of Quality: Why A Compliance-Focused Model will Ultimately Limit Growth
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.
Digital Part Inspection Software Creates New Business Opportunities
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.
How and Why Pharmaceutical Manufacturers Are Applying Artificial Intelligence
“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%.”
Vision Cameras Inspect Disk Drive Assemblies
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.
Visual Inspection AI: a purpose-built solution for faster, more accurate quality control
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.
AI Vision for Monitoring Applications in Manufacturing and Industrial Environments
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.
John Deere and Audi Apply Intel’s AI Technology
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.
Machine learning optimizes real-time inspection of instant noodle packaging
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.
Tractor Maker John Deere Using AI on Assembly Lines to Discover and Fix Hidden Defective Welds
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.
AI In Inspection, Metrology, And Test
“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.”
AI tool locates and classifies defects in wind turbine blades
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.
Transforming quality and warranty through advanced analytics
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.
Smart quality in advanced industries
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.
Strategic Analytics Help Intertape Polymer Shrink Inefficiencies
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.
AWS Announces General Availability of Amazon Lookout for Vision
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.
Analysing fruit data in the supply chain has never been more important for business efficiency
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.
Pushing The Frontiers Of Manufacturing AI At 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.
Early And Fine Virtual Binning
ProteanTecs enables manufacturers to bin chips virtually, in a straightforward and inexpensive way based on Deep Data. By using a combination of tiny on-chip test circuits called “Agents” and sophisticated AI software, chip makers can find relationships between any chip’s internal behavior and the parameters measured during the standard characterization process. Those relationships can be used to measure similar chips’ internal characteristics at wafer sort to precisely predict how chips would perform during Final Test, even before the wafer is scribed.
Computer Vision Advances Zero-Defect Manufacturing
A key part of the process it wanted to automate is server assembly quality assurance, which was being done manually by quality operators. This labor-intensive process is prone to error due to human eye fatigue and the inability of quality operators to catch critical defects.
This situation is hardly unusual. According to Kemal Levi, Founder and CEO of Relimetrics, there is “a strong demand for computer vision to replace manual visual inspections. Yet, due to a high production variability, particularly in the case of discrete manufacturing, computer vision systems today are not able to keep up with the rate of change in configurations.”
Lynx Visual Inspection System
VIRO WSI: New Standards for Automated Weld Seam Inspection | VITRONIC
Google Glass Didn't Disappear. You Can Find It On The Factory Floor
With Google Glass, she scans the serial number on the part she’s working on. This brings up manuals, photos or videos she may need. She can tap the side of headset or say “OK Glass” and use voice commands to leave notes for the next shift worker.
Peggy Gullick, business process improvement director with AGCO, says the addition of Google Glass has been “a total game changer.” Quality checks are now 20 percent faster, she says, and it’s also helpful for on-the-job training of new employees. Before this, workers used tablets.
Augmented Reality Is Already Improving Worker Performance
The video below, for example, shows a side-by-side time-lapse comparison of a GE technician wiring a wind turbine’s control box using the company’s current process, and then doing the same task while guided by line-of-sight instructions overlaid on the job by an AR headset. The device improved the worker’s performance by 34% on first use.
There’s been concern about machines replacing human workers, and certainly this is happening for some jobs. But the experience at General Electric and other industrial firms shows that, for many jobs, combinations of humans and machines outperform either working alone. Wearable augmented reality devices are especially powerful, as they deliver the right information at the right moment and in the ideal format, directly in workers’ line of sight, while leaving workers’ hands free so they can work without interruption. This dramatically reduces the time needed to complete a job because workers needn’t stop what they’re doing to flip through a paper manual or engage with a device or workstation. It also reduces errors because the AR display provides explicit guidance overlaid on the work being done, delivered on demand. Workers need only follow the detailed instructions directly in front of them in order to move through a sequence of steps to completion. If they encounter problems, they can launch training videos or connect by video with remote experts to share what they see through their smart glasses and get real-time assistance.