The document discusses design of experiments (DOE) and Taguchi methods. It explains key concepts in Taguchi's approach such as quality robustness, quality loss function, and target specifications. Taguchi techniques use experimental design methods to identify important variables affecting a product or process and optimize the design. The document provides examples of full and fractional factorial experiments to study the effects of multiple factors and interactions. It emphasizes the importance of considering interactions between factors rather than just main effects.
Introduction to Design of Experiments by Teck Nam Ang (University of Malaya)Teck Nam Ang
This set of slides explains in a simple manner the purpose of experiment, various strategies of experiment, how to plan and design experiment, and the handling of experimental data.
An introduction to SigmaXL's Design of Experiments tools.
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
Understanding the customer needs and predicting customer’s purchase intents form the core of success for any business. For the vastly diversified automobile industry, with technological breakthroughs happening almost every day, it becomes essential to study the factors which affect directly or indirectly when a customer decides to buy a vehicle and to predict the market trend.
Advanced DOE with Minitab (presentation in Costa Rica)Blackberry&Cross
DOE:Diseño de Experimentos
Esta presentación fue dada por Minitab Inc., en Costa Rica, en el año 2007, como parte del trabajo de Blackberry&Cross, socio de Minitab Inc., para América Central, en la promoción y difusión de temas STEM, y de la comercialización de Minitab Statisitical Software.
S1 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Introduction to Design of Experiments by Teck Nam Ang (University of Malaya)Teck Nam Ang
This set of slides explains in a simple manner the purpose of experiment, various strategies of experiment, how to plan and design experiment, and the handling of experimental data.
An introduction to SigmaXL's Design of Experiments tools.
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
Understanding the customer needs and predicting customer’s purchase intents form the core of success for any business. For the vastly diversified automobile industry, with technological breakthroughs happening almost every day, it becomes essential to study the factors which affect directly or indirectly when a customer decides to buy a vehicle and to predict the market trend.
Advanced DOE with Minitab (presentation in Costa Rica)Blackberry&Cross
DOE:Diseño de Experimentos
Esta presentación fue dada por Minitab Inc., en Costa Rica, en el año 2007, como parte del trabajo de Blackberry&Cross, socio de Minitab Inc., para América Central, en la promoción y difusión de temas STEM, y de la comercialización de Minitab Statisitical Software.
S1 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Experimental methods are widely used in industrial settings and research activities. In industrial settings, the main goal is to extract the maximum amount of unbiased information regarding the factors affecting production process form few observations, whereas in research, ANOVA techniques are used to reveal the reality. Drawing inferences from the experimental result is an important step in design process of product. Therefore, proper planning of experimentation is the precondition for accurate conclusion drawn from the experimental findings. Design of experiment is powerful statistical tool introduced by R.A. Fisher in England in the early 1920 to study the effect of different parameters affecting the mean and variance of a process performance characteristics
Taguchi's orthogonal arrays are highly fractional orthogonal designs. These designs can be used to estimate main effects using only a few experimental runs.
Consider the L4 array shown in the next Figure. The L4 array is denoted as L4(2^3).
L4 means the array requires 4 runs. 2^3 indicates that the design estimates up to three main effects at 2 levels each. The L4 array can be used to estimate three main effects using four runs provided that the twthree-factoro factor and three factor interactions can be ignored.
Optimization is selecting the most suitable element from available resources considering all the factors which influence decisions in any experiment.
In the pharmaceutical industry, optimization has always meant changing one variable at a time to solve a problem formulation.
To improve formulation irregularities, modern pharmaceutical optimization uses a systematic design of experiments (DoE).
Quality by Design enhances the assurance of safe and effective drugs to consumer and promise to improve manufacturing quality performance and also product free of contamination and gives the desired benefit as in the label to the consumer.
Measurement Systems Analysis - Variable Gage R&R Study Metrics, Applications ...Gabor Szabo, CQE
This presentation walks you through the components of variation and the various metrics used in Variable Gage R&R Study. It also talks about the different root causes associated with a failing study, and how to perform root cause analysis using statistical tools.
Experiments
A Quick History of Design of Experiments
Why We Use Experimental Designs
What is Design of Experiment
How Design of Experiment contributes
Terminology
Analysis Of Variation (ANOVA)
Basic Principle of Design of Experiments
Some Experimental Designs
Guidelines to Understanding Design of Experiment and Reliability Predictionijsrd.com
This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers' in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
4. Taguchi’s View of Variation Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs. Tolerances are continuous, not yes/no Incremental Cost of Variability High Zero Lower Spec Target Spec Upper Spec Traditional View Incremental Cost of Variability High Zero Lower Spec Target Spec Upper Spec Taguchi’s View
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9. Quality Loss Function and its distribution Low loss High loss Frequency Lower Target Upper Specification Loss (to producing organization, customer, and society) Quality Loss Function (a) Unacceptable Poor Fair Good Best Target-oriented quality yields more product in the “best” category Target-oriented quality brings products toward the target value Conformance-oriented quality keeps product within three standard deviations Distribution of specifications for product produced
30. EXAMPLE OF AN EXPERIMENT Studying the effect of two different hardening processes, oil quenching and salt water quenching on an aluminium alloy. OBJECTIVE: To determine the quenching solution that produces the maximum hardness. PROCEDURE: The experimenter decides to subject a number of alloy specimens to each quenching solution and measure the hardness of the specimens after quenching. The average hardness of the specimens treated in each quenching solution will be used to determine the best solution.
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32. STATISTICAL DESIGN OF AN EXPERIMENT The process of planning the experiment so that appropriate data collected which shall be analyzed by statistical methods resulting in valid and objective conclusions. Two aspects of experimental problem: The design of the experiment The statistical analysis of the data
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35. Blocking: Is a technique used to increase the precision of an experiment. A block is a portion of the experimental material that should be more homogeneous than the entire set of material. Blocking involves making comparisons among the conditions of interest in the experiment within each block. It is also a restriction on complete randomization. Three basic principles of design
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45. FACTORIAL DESIGNS MAIN EFFECT : Change in response produced by a change in the level of the factor B A FIG. 1 FIG. 2 FIG 1 Avg. effect of A = [(40-20)+(52 –30)]/2 = 21 Avg. effect of B = [(52-40)+(30-20)]/2 = 11 B A L L H H . . . . 20 40 50 12 L L H H . . . . 20 40 30 52
46. INTERACTION EFFECT The difference in response between the levels of one factor is not the same at all levels of the other factors. At low level of ‘B’, the ‘A’ effect is : 50-20=30 At high level of ‘B’, the ‘A’ effect is : 12-40=-28 The avg. interaction effect ‘AB’ = (-28-30)/2 = -29 RESPONSE RESPONSE FACTOR A FACTOR A A1B1 A1B1 A1B2 A1B2 A2B2 A2B2 A2B1 A2B1 NO SIGNIFIANT INTERACTION SIGNIFIANT INTERACTION
47. INTERACTION EFFECT When interaction is maximum, then corresponding main effects have little practical meaning. However, the effect of A at different levels of factor B is significant Ex. Effect of A is : (50+12)/2-(20+40)/2= 1 may lead to a conclusion that A has no effect ( Is it correct?) Factor A has an effect and it depends on the level of factor B. Hence the knowledge about interaction between AB is more useful than main effects. The interaction effect of one factor (let A) with levels of other factors fixed to draw conclusions. Information on both can be studied by varying one at a time. The effect of changing factor A (B is fixed) is given by A + B - – A - B - B A L L H H . . . A - B - A + B - A + B + B A L L H H . . . . 20 40 50 12
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50. H 0 : No significant difference If F 0 > F x,v1,v2 , reject H 0 abn-1 TOTAL MS E =SS E / ab(n-1) ab(n-1) SS E Error MS AB /MS E MS Ab =SS AB / (a-1)(b-1) (a-1)(b-1) SS AB AB MS B /MS E MS B =SS B /b-1 b-1 SS B B MS A /MS E MS A =SS A /a-1 a-1 SS A A F 0 Mean square Degrees of freedom Sum of squares Source of variation
51. TWO FACTOR EXPERIMENT : AN ILLUSTRATION Life Data (Hrs) for a battery Design T=3799 770 1291 1738 Ti 60 82 139 150 160 168 1501 342 104 96 583 120 174 576 110 138 3 45 58 115 106 126 159 1300 198 70 25 79 122 136 623 188 150 2 58 82 75 80 180 74 998 230 70 20 229 40 34 539 155 130 1 125 70 15 T j Temperature (A) Material Type (B)
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53. ANOVA : Battery Life F 0.05, 4,27 = 2.73 F 0.05, 2,27 = 3.35 35 77646.97 Total 675.21 27 18230.75 Error 3.56 S 2403.44 4 9613.78 Interaction (AB) 7.91 S 5341.86 2 10683.72 Material Type (B) 28.97 S 19559.36 2 39118.72 Temperature (A) F O Mean Square Degrees of freedom Sum of Squares Source of variation
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55. A SINGLE FACTORS EXPERIMENT (one-way ANOVA) FABRIC WEAR RESISTANCE DATA T… = 38.41 CF = T 2 /N SS TOTAL = SS FACTOR + SS E SS TOTAL = 1.93 2 + 2.38 2 +……+ 2.25 2 –(38.41) 2 /16 = 0.7639 SS FACTOR = (8.76 2 + 10.72 2 + 9.67 2 + 9.26 2 )/4 – CF = 0.5201 Col. Total 9.26 9.67 10.72 8.76 2.25 2.28 2.70 2.25 2.28 2.31 2.75 2.20 2.40 2.68 2.72 2.38 2.33 2.40 2.55 1.93 D C B A TYPE OF FABRIC
56. ANOVA : Fabric Wear Resistance F 0.05, 3,12 = 3.49 15 0.7639 Total 0.0203 12 0.2438 Within Fabrics (Error) 8.54 S 0.1734 3 0.5201 Between Fabrics F O MS df SS Source