AI in Quality Control: How to do visual inspection with AI
•
2 j'aime•615 vues
About the webinar Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products. Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live. Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives. What you will learn - How various industries are leveraging AI to assist in visual inspections. - Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes. - Address the challenges of AI & Machine learning and how to overcome them.
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à AI in Quality Control: How to do visual inspection with AI
Similaire à AI in Quality Control: How to do visual inspection with AI (20)
Plus de Skyl.ai
Plus de Skyl.ai (15)
Dernier
Dernier (20)
AI in Quality Control: How to do visual inspection with AI
- 1. AI in Quality Control: How to do visual inspection with AI
- 2. Technology enthusiast with 13+ years of experience working in the information technology and services industry. Leads cutting-edge solutions for businesses using Machine Learning and Artificial Intelligence. Areas of expertise includes Architecture design, Solutioning, Data Engineering and Deep Learning.Mohit Juneja Solutions Architect The Speaker
- 3. Extensive experience building future tech products using Machine Learning and Artificial Intelligence. Areas of expertise includes Deep Learning, Data Analysis, full stack development and building world class products in ecommerce, travel and healthcare sector. Shruti Tanwar Lead - Data Science The Speaker
- 4. Bikash Sharma CTO and Co-founder at Skyl.ai CTO & Software Architect with 15 years of experience working at the forefront of cutting-edge technology leading innovative projects Areas of expertise include Architecture design, rapid product development, Deep Learning and Data Analysis The Panelist
- 5. Getting familiar with ‘Zoom’ All dial-in participants will be muted to enable the presenters to speak without interruption Questions can be submitted via Zoom Questions chat window and will be addressed at the end during Q&A The recording will be emailed to you after the webinar Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
- 6. Live Demo of detecting cracks in cement surfaces using Machine learning How industries are leveraging AI to assist in visual inspection How to quickly overcome the challenges in building ML models 1 2 3 ...In the next 45 minutes
- 7. Machine Learning automation platform for unstructured data A quick intro about Skyl.ai Guided Machine Learning Workflow Build & deploy ML models faster on unstructured data Collaborative Data Collection & Labeling Easy-to-use & scalable AI SaaS platform
- 8. POLL #1 At what stage of Machine learning adoption your organization is at? ⊚ Exploring - Curious about it ⊚ Planning - Creating AI/ML strategy ⊚ Experimenting - Building proof of concepts ⊚ Scaling up - Some departments are using it ⊚ In production - Using it in product features ⊚ Transforming - AI/Ml driven business
- 9. How industries are leveraging AI to assist in visual inspections01
- 10. Food and Beverage Industry Foreign object detection - detailed inspection of incoming food materials on the production line Defective Labeling Identification - Identify misbranding, incorrect positioning and damaged labels to ensure the product conformity
- 11. Automotive Industry Imperfect Surface Detection - Spot the visible irregularities on the product to improve quality of the output Inspect Automobile parts - detection of missing nuts and bolts in the automobile under production
- 12. Pharmaceuticals and Medical Industry Vial/Phial cap and liquid level Detection - detect missing caps and check the liquid levels in the vials used for clinical diagnostic Quality check of Face Masks - detection of missing facemask components and damages to ensure the produced masks pass ISO standards
- 13. Construction Industry Concrete crack detection - detection of surface cracks during monitoring and inspection of civil engineering structures Spall detection - detects damages and segment the intact region to measure the spall depth
- 14. Benefits of Visual Inspection for Quality Control Increased Accuracy of Final Goods Reduced Quality Control Downtime Reduced Costs of Quality Checking Improved Production Efficiency
- 15. Demo of detecting cracks in cement surfaces.02
- 16. 8 stages of Machine Learning workflow
- 17. Demo of detecting cracks in cement surfaces
- 18. Skyl.ai - as ML automation platform Efficient Data Management Solve your data issues; collect and manage data efficiently Accuracy & Quality Maintain accuracy and quality; train and test faster; monitor quality Effective Collaboration Collaborate and manage projects efficiently Early Visibility Get early visibility; visualize and affirm correctness on every step of the way Scalable High - Performance Access on-demand and scalable, high-performance infrastructure Reduce Cost Reduce cost of implementation; do it with less specialized resources
- 19. POLL #2 Some challenges that you are facing while implementing AI & Machine Learning ⊚ Not started yet, so no challenges ⊚ Data collection ⊚ Data Labeling ⊚ Large volumes of data ⊚ Identifying the right data set to train ⊚ Data Security ⊚ Lack of knowledge of ML tools ⊚ Lack of end to end platform ⊚ Lack of expertise ⊚ Choosing the right algorithms
- 20. Overcoming the AI / ML challenges with the right tools and technologies03
- 21. Best Practices for Data Collection ⊚ Use relevant data sources for data collection ⊚ Establish proper data collection mechanisms ⊚ Do not stop with too-small data sample size Data Collection Data Quality Data SecurityData Security Data SecurityData Labeling
- 22. Best Practices for Data Quality ⊚ Do validate your data and data sources ⊚ Clean up your data regularly - “garbage out” ⊚ Data correction - remove duplicates, missing data, etc ⊚ Check the consistency of data while data acquisition Data Collection Data Quality Data SecurityData Security Data SecurityData Labeling
- 23. Best Practices for Data Security ⊚ Monitor data processes continuously to mitigate risks ⊚ Increase data security with encryption and tokenization ⊚ Controlled access flows with different organizational roles Data Quality Data Collection Data SecurityData Security Data SecurityData Labeling
- 24. Best Practices for Data Labeling ⊚ Define the problem you want to solve and use relevant labels inline with the entities you want to predict ⊚ Analyse trends and progress of your data labeling in real time - to find biases ⊚ Do not add new entity types midway ⊚ Use short tag lists and annotationsData Labeling Data Quality Data SecurityData Security Data Collection
- 25. Challenges ⊚ Requisite Infrastructure ⊚ Cost of Infrastructure ⊚ Data and ML pipeline ⊚ Model at scale for inference Best Practices ⊚ Use SaaS Model (Pay as you go) - reliable, scalable and secure ⊚ The right software tuned and optimized to fit the underlying hardware ⊚ A flexible infrastructure that can be deployed in the cloud or in an on-premise data center to optimize performance Technology issues and solutions
- 26. Best Practices ⊚ Train existing employees with education related to AI and ML ⊚ Use Saas products with good documentation, support and implementation that alleviates the need to have highly skilled data scientists and resources with multiple skills. 40% Lack of skilled talent Source: Techrepublic Barrier in adopting AI ⊚ Companies face shortage of necessary in-house talent. Specialized skills and knowledge
- 27. Challenges ⊚ Long implementation time ⊚ Measure ROI of the AI deployment Best Practices ⊚ AI implementation results in increased process efficiency and automation. ⊚ Create own AI KPIs and analyze the difference in the measurements before and after AI deployment. TechRepublic claims that 56% of global CEOs expect it to take 3-5 years to see any real ROI on their AI investment. Speed and time to market
- 28. Collect Feedback Monitor the model Process Feedback Deploy the changes Train and Evaluate Continuous Improvement Best Practices ⊚ Perform incremental and measurable improvements ⊚ Monitor your deployed models and analyse inference count, accuracy and execution time. ⊚ Check model performance in real time Monitoring and continuous improvement
- 29. AI Project Management More Challenges and Concerns ⊚ Project Cost ⊚ Return on Investment ⊚ On-demand scalability ⊚ Iterative corrections in AI project Source: AI for People and Business: A Framework for Better Human Experiences and Business Success DATA Time Cost Performance Requirements The TCPR Model
- 30. Skyl.ai - as ML automation platform Efficient Data Management Solve your data issues; collect and manage data efficiently Accuracy & Quality Maintain accuracy and quality; train and test faster; monitor quality Effective Collaboration Collaborate and manage projects efficiently Early Visibility Get early visibility; visualize and affirm correctness on every step of the way Scalable High - Performance Access on-demand and scalable, high-performance infrastructure Reduce Cost Reduce cost of implementation; do it with less specialized resources
- 31. ⊚ Free 1 month Trial + POC ⊚ Complimentary 30 min consultation ⊚ AI Implementation Playbook www.skyl.ai contact@skyl.ai Special offer for you...
- 32. Questions? ?
- 33. We hope to hear from you soon Thank you for joining! 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 contact@skyl.ai
Notes de l'éditeur
- Hello everyone and welcome. Thank you for joining today’s webinar on AI in Quality Control: How to do visual inspection with AI. My name is Edwin Martinez and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
- First we have Mohit Juneja, Mohit is a Solutions Architect and Technology supporter with over 13 years of experience in the IT and Service industry. He leads cutting-edge solutions for businesses using Machine Learning and AI. He’s an expert in Architect design, Data Engineering, and Deep Learning. Welcome Mohit!
- Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI. Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
- Finally, we have Bikash Sharma joining us today. Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions. He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
- Before we begin, I’d like to briefly talk about some Zoom features that will be relevant to us. All participants in the webinar will be muted to avoid any interruptions during the session. Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen. We’ll make sure to address your questions during the Q&A session. Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again. So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Mohit
- Exploring - Curious about it Planning - Creating AI/ML strategy Experimenting - Building proof of concepts Scaling up - Some departments are using it In production - Using it in product features Transforming - AI/Ml driven business
- In the automotive and automotive-parts industries, the production rate, manufacturing environment, and composition of raw materials all markedly affect the quality and yield of the final product. As a result, auto manufacturers have been early adopters of machine-vision systems
- Defective Labeling Identification: The food production chain includes several crucial steps requiring close inspection of a product. One of them is checking if the label is correct. Misbranding, incorrect positioning and damaged labels must be avoided to ensure the product conforms to current Good Manufacturing Practices. Foreign object detection: In a production line or during packaging, very detailed assumptions about the incoming material are made. Foreign objects being conveyed into a machine might destroy the machine or, when packaged and delivered harm or at least annoy the consumer. Thus, the detection of any foreign object is necessary in order to take respective actions. In the figure above, you see a stone that was erroneously harvested together with apples and that needs to be separated before any subsequent treatment.
- Imperfect surface detection: To guarantee the right level of quality of a manufactured item, we detect any visible irregularities. In addition, we not only compute metrics such as the shape or the extent of a defect, but also apply classification with regard to its type. While certain types of defects might be tolerable, others will be blockers. Also this information helps to identify the cause of the defect. Inspect Automobile: This has become a primary mission for many machine vision systems on automotive industry production lines. These vision systems use powerful pattern recognition capabilities to find missing material, chips, scratches, dents, misplaced markings, and a wide variety of other flaws. In addition to ensuring the quality of finished parts and products, they also enable manufacturers to reduce costs by eliminating defective pieces before wasting additional material and production time on them.
- Clinical diagnostic vision technology can provide improved system ease of use, intelligence and error proofing to set your systems apart from the competition.(Test tube cap presence/absence, Test tube identification by shape, Proper system set up through object recognition, Test tube size verification by width, Micro-array tray identification / barcode reading, Liquid level detection) By leveraging both machine vision and deep learning technology manufacturers can ensure masks are produced in compliance with ISO standards and catch defective masks before they are shipped. vision system detects the presence of facemask components such as earbands and strap welds, while also measuring the width of the masks to ensures they are manufactured to the correct size.
- The manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. Manual inspection can also be difficult to perform in case of high rise buildings and bridges.
- Increased Accuracy: CV-based approaches ensure a higher grade of accuracy within the accepted tolerance in every manufacturing process. Even when workers use specific equipment, such as a magnifying glass, machines are still more precise. Reduced Downtime: An automated system is an effective tool to reduce quality control downtime. As the system is fully automated, it runs much faster, it is able to work 24/7 and it does not need any breaks for rest. Improved Efficiency: Visual inspection can improve the production efficiency. These systems can catch errors at a faster rate. Analysis of these observed defects can be made quickly and necessary corrections can be made quickly. Reduced Costs: An automatic machine vision system provides tangible economical benefits. With such a system, manufacturing companies do not require working personnel to manually perform control of manufactured products, allowing them to concentrate on more important work. Additionally, a CV-powered system does not make mistakes, which can appear during manual control. The cost of a small human mistake can sometimes be valued at millions if not billions of dollars and Machine Vision helps to avoid it.
- How
- 5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA Define problem - Features model - How this model is built using skyl.ai
- Not started yet, so no challenges Data collection Data Labeling Data Bias Large volumes of data Identifying the right data set to train Lack of knowledge of ML tools Lack of end to end platform Lack of expertise Choosing the right algorithms Monitoring the model performance
- Benefit
- Data is one of the most valuable resources today’s businesses have. The more information you have about your customers, the better you can understand their interests, wants and needs. Use of relevant data sources - to get consistent and accurate data relevant for the problem you want to solve Collection mechanisms - A formal data collection process is necessary as it ensures that the data gathered are both defined and accurate. Small sample size - do not give the distribution of the data to the edge cases and will not train the model for exceptional cases. Keep in mind that machine learning is a process of induction. The model can only capture what it has seen. If your training data does not include edge cases, they will very likely not be supported by the model.
- The best practices can be achieved by Data Cleaning: Applying a detailed data analysis at the initial phase for recognizing which sorts of irregularities and errors must be expelled. Notwithstanding a manual assessment of the information or data samples, analytic programs are frequently expected to pick up metadata about the data resources and distinguish the issues of data quality. Don’t let bad data or records go unresolved - remove duplicates and fill missing data; For missing data, you should flag and fill the values. Flag the observation with an indicator variable of missingness. Incorrect or inconsistent data leads to false conclusions. And so, how well you clean and understand the data has a high impact on the quality of the results.
- Encrypted data sources All data sources are encrypted; thus giving users an additional layer of security, making sure your data stays safe and protected. Access controlled flow Defined and controlled access flows with different organizational roles like business owner, project lead, collaborators etc. allow for selective restriction so that you have full command to regulate who can view or use resources in your ML projects.
- Adding tags midway - For example, the set of tags for a pizza chatbot might start with the tags “Size” “topping” and “drink” before someone realizes that you also need a “Side Dish” tag to capture Garlic Bread and Chicken Wings. Simply adding these tags and continuing work on the documents that haven’t been labeled yet poses a danger to the project. The new tags will be missing from all of the documents annotated before the new tags were added This means that your test set will be wrong for those tags, and your training data won’t contain the new tags leading to a model that won’t capture them. <https://towardsdatascience.com/four-mistakes-you-make-when-labeling-data-7e431c4438a2> In an annotation process, increasing the number of choices the annotator needs to make slows them down and leads to poor data quality.
- Requisite Infrastructure: When launching a machine learning initiative, organizations can easily underestimate the resources they need for infrastructure. There can be substantial infrastructure requirements for machine learning, especially in the cases of image, video, and audio processing. Cost of Infrastructure: Training and deploying a scalable infrastructure to support machine learning can be expensive and difficult to maintain. Having a cloud approach allows experimentation with machine learning at scale without the overhead of physical hardware acquisition, configuration, and deployment. Data & ML pipeline: AI, Machine learning and deep learning solutions require a high degree of computation speeds offered. Model at Scale for inference: Deploying a scalable infrastructure to support machine learning can be expensive and difficult to maintain. Things get tedious and difficult to maintain at scale compared to having single server deployment or in a developer’s environment which is not a usual case.
- Skills challenges: Choice of right ML algorithm - ML, DL, RL AI product management - Dealing with Cold start, managing data labeling project, keeping transparency in the project; Keeping the model up to date. Adoption of AI technologies requires specialists like data scientists, data engineers, infrastructure engineers and other SMEs (Subject Matter Experts).
- Even with long implementation time, AI has potential to cut the expenses. TechRepublic claims that 56% of global CEOs expect it to take 3-5 years to see any real ROI on their AI investment. Machine automation produces quality products faster and more efficiently, while providing critical information to help managers make more informed business decisions.
- Making continuous improvement part of company culture is an excellent and cost-effective approach to tackling an organization’s most difficult challenges. When supported by improvement technology, results can be achieved quickly and success can be sustained over time.
- On-demand scalabilty: The truth that it’s better to have a working prototype of a smaller product, rather than an unfinished large one, still stands here with machine learning products. New ML MVPs should be prioritized based on the speed of delivery and their value to the company. If you can deliver products, even those which may be smaller, with speed, it can be a good, quick win for the whole team—you should prioritize these products first. Organizations need to keep in mind that machine learning is an iterative process, and modifications to models might happen over time to support changing requirements. TCPR Model: The TCPR model represents an indeterminate system—one in which more than one solution exists. In this way, it’s like a four-legged table. Engineers know that unless a four-legged table is perfectly made, and the floor on which it rests is completely flat, it’s impossible to calculate the simultaneous forces on all four legs. Moreover, the table is unbalanced and is likely resting mainly on three legs, which causes it to wobble as a result. Link: TCPR
- Thank you Mohit and Shruti, for the wonderful presentation and demo. As mentioned earlier, the recording of the webinar will be emailed to you afterwards. [pause] Before we get to the Q&A, I want to mention some of the offers Skyl has for those of you that are curious about incorporating Machine Learning to your business. Skyl offers a free 1 month trial, plus Proof of Concept. You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model! Skyl also offers a complimentary 30 min consultation and an AI Implementation Playbook to go along. This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges. If you’re interested in finding out more, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai.
- Alright, now it’s Q&A time! As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen. We’ll try to answer as many questions as possible in the time that we have left. So let’s answer some questions. Sample questions: Shruti -(Julie) If I build a lot of models, how do I handle model deployment in that case? - (Aaron) Can Skyl help me in figuring out if my data needs re-labelling? Mohit -(anonymous) How can Skyl help me with my data labelling needs if I have data privacy issues? -(Jose) Apart from images, can we use Skyl for classifying text data or extracting data from documents like pdfs? Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
- All right, so we have reached the end of the webinar. We hope you enjoyed it. We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries, So don’t miss out and make sure you sign up for upcoming webinars as well Thank you for joining and I hope you have a wonderful day.