Virtual materials testing: come funziona e i benefici sulla produzioneCompositi
Webinar 29 settembre 2020
Connettendo il mondo reale con il virtuale, il processo di sviluppo di virtual materials testing permette di accelerare i tempi e ridurre i costi della filiera.
Simulation can help in both design and process optimization for additive manufacturing industry by getting the product right the first time. Cost saving by reducing print iterations can be tremendous. The presentation covers some overview of the AM industry and specifically discusses both metal and polymer AM simulation solutions.
Fused deposition modelling (FDM) has become a prevalent technique to additively manufacture polymer that can provide design freedom and creativity. However, like any other AM technologies, FDM has its own challenges. The inherent void, surface roughness and dimensional accuracy during manufacturing can lead to tolerance rejection, cracking or failure during the product life. Also, the resultant anisotropic material properties are inherent in the layer-by-layer manufacturing features. Therefore, it is important to understand and simulate FDM process, in which complex thermo-mechanical interaction takes place due to rapid heating/cooling.
The current work will first provide a brief overview of the current polymer AM simulation solutions, validation protocols, and standardization efforts. Secondly, a simulation framework using Abaqus will be presented to replicate the FDM process that can provide insight into how the process parameters affect product quality. Currently, simulation solutions has the capability to model the polymer extrusion process with that can capture the layer-by-layer element activation feature and varying free surface in heating and cooling. Finally, an example study will be presented to show how modifying process parameters such as raster angle, contour width and layer thickness affect the transient thermal, deformation and residual stress field. Accordingly, optimal processing parameters can be identified based on the criterion of minimizing distortion and residual stresses.
With the advent of efficient and robust numerical analysis techniques such as finite element analysis (FEA) and the increased computing power of today’s hardware solutions, it has become practical to simulate thermo-mechanical behaviors of plastics and rubbers that can capture the physical reality of such materials with high fidelity. Replicating physical behavior of highly complex and non-linear materials such as plastics have positioned FEA tools to create life like models that will behave like the real part or product. Such simulation can compare very well with the physical test. Therefore, using FEA techniques one can perform virtual testing to design plastic products and also can use simulation techniques to optimize design based on a mathematically robust approach instead of heuristic, experience based approach only. Using FEA one can address the needs of the product development lifecycle from concept through detailed design capturing realistic simulation of underlying complex physics.
Additive manufacturing (AM) or 3D printing is maturing rapidly as a viable solution of make optimized parts for “real engineering” applications. The freedom of design that is achievable using AM process is un parallel in terms of reducing structural weight, reducing material cost, generating complex shapes and connections and introducing directional properties in a component. However, understanding of AM process and utilizing process parameters to optimize a design comes with many challenges. Currently, one of the emphasize is to use physics based realistic simulation to replicate the AM process numerically and relate process parameters to the concept of functional generative design that relates design with manufacturing process.
Current work, through a typical build example, discusses an integrated numerical solution on a digital platform that involves the following.
Generative Design involving topology optimization that creates parts in context of the manufacturing process and automatically generate variants of conceptual and detailed organic shapes that helps make informed business decisions based on physics-based analytic tools. Process planning that defines and customizes manufacturing environment including nesting parts automatically on the build tray, designing and generating optimal support structures, and creating machine specific slicing and scan path which is ready for print. Process simulation that automatically includes machine inputs for energy, material and supports into the simulation at layer, part and build levels for any additive manufacturing process and accurately predicts part distortions, residual stresses and as-built material behavior. Finally, the platform involves post processing to perform shape optimization where simulation is used to guide support-structure strategy for enhanced build yield, compensate distortion effects without the need to redesign the product tooling, produce high-quality morphed surface geometry with unchanged topology, and perform final in-service performance validations of manufactured part.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
Virtual materials testing: come funziona e i benefici sulla produzioneCompositi
Webinar 29 settembre 2020
Connettendo il mondo reale con il virtuale, il processo di sviluppo di virtual materials testing permette di accelerare i tempi e ridurre i costi della filiera.
Simulation can help in both design and process optimization for additive manufacturing industry by getting the product right the first time. Cost saving by reducing print iterations can be tremendous. The presentation covers some overview of the AM industry and specifically discusses both metal and polymer AM simulation solutions.
Fused deposition modelling (FDM) has become a prevalent technique to additively manufacture polymer that can provide design freedom and creativity. However, like any other AM technologies, FDM has its own challenges. The inherent void, surface roughness and dimensional accuracy during manufacturing can lead to tolerance rejection, cracking or failure during the product life. Also, the resultant anisotropic material properties are inherent in the layer-by-layer manufacturing features. Therefore, it is important to understand and simulate FDM process, in which complex thermo-mechanical interaction takes place due to rapid heating/cooling.
The current work will first provide a brief overview of the current polymer AM simulation solutions, validation protocols, and standardization efforts. Secondly, a simulation framework using Abaqus will be presented to replicate the FDM process that can provide insight into how the process parameters affect product quality. Currently, simulation solutions has the capability to model the polymer extrusion process with that can capture the layer-by-layer element activation feature and varying free surface in heating and cooling. Finally, an example study will be presented to show how modifying process parameters such as raster angle, contour width and layer thickness affect the transient thermal, deformation and residual stress field. Accordingly, optimal processing parameters can be identified based on the criterion of minimizing distortion and residual stresses.
With the advent of efficient and robust numerical analysis techniques such as finite element analysis (FEA) and the increased computing power of today’s hardware solutions, it has become practical to simulate thermo-mechanical behaviors of plastics and rubbers that can capture the physical reality of such materials with high fidelity. Replicating physical behavior of highly complex and non-linear materials such as plastics have positioned FEA tools to create life like models that will behave like the real part or product. Such simulation can compare very well with the physical test. Therefore, using FEA techniques one can perform virtual testing to design plastic products and also can use simulation techniques to optimize design based on a mathematically robust approach instead of heuristic, experience based approach only. Using FEA one can address the needs of the product development lifecycle from concept through detailed design capturing realistic simulation of underlying complex physics.
Additive manufacturing (AM) or 3D printing is maturing rapidly as a viable solution of make optimized parts for “real engineering” applications. The freedom of design that is achievable using AM process is un parallel in terms of reducing structural weight, reducing material cost, generating complex shapes and connections and introducing directional properties in a component. However, understanding of AM process and utilizing process parameters to optimize a design comes with many challenges. Currently, one of the emphasize is to use physics based realistic simulation to replicate the AM process numerically and relate process parameters to the concept of functional generative design that relates design with manufacturing process.
Current work, through a typical build example, discusses an integrated numerical solution on a digital platform that involves the following.
Generative Design involving topology optimization that creates parts in context of the manufacturing process and automatically generate variants of conceptual and detailed organic shapes that helps make informed business decisions based on physics-based analytic tools. Process planning that defines and customizes manufacturing environment including nesting parts automatically on the build tray, designing and generating optimal support structures, and creating machine specific slicing and scan path which is ready for print. Process simulation that automatically includes machine inputs for energy, material and supports into the simulation at layer, part and build levels for any additive manufacturing process and accurately predicts part distortions, residual stresses and as-built material behavior. Finally, the platform involves post processing to perform shape optimization where simulation is used to guide support-structure strategy for enhanced build yield, compensate distortion effects without the need to redesign the product tooling, produce high-quality morphed surface geometry with unchanged topology, and perform final in-service performance validations of manufactured part.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
Industrial production process visualization with the Elastic Stack in real-ti...Elasticsearch
Learn how the Mayr-Melnhof Group implemented production process visualization in a highly automated and fragmented industrial, process-control environment with the Elastic Stack.
The current work focuses on simulation based optimization of a complex, safety critical component where it is prohibitively expensive to carry out finite element analysis (FEA) simulations for all possible sample realizations and therefore requires statistical or machine learning techniques for a timely yet accurate solution. The applicability of machine learning further brings the opportunity of performing in-service monitoring using sensor data and thereby performing predictive maintenance.
Sensor-based, Data-driven Optimization of Composites Manufacturing.pptxmarketingnxp
This is the presentation of the speech of Dr. Nicholas Ecke at SAMPE Europe Conference Madrid 2023.
Plastics manufacturing can be unpredictable. Deviations in material batches, moisture content, machine calibration, among other variables, lead to issues in manufacturing quality and final part properties.
New technology has been developed to combine dielectric analysis with machine learning and material models, allowing for dynamic adjustments to machine settings, removing uncertainty from your process, and optimizing cycle times.
Software Engineering Challenges in building AI-based complex systemsIvica Crnkovic
Development of AI-based systems goes far beyond using specific AI-algorithms. The development itself is becoming more complex since data and algorithms become dependent. This presentation lists some of new challenges that AI-developers meet.
Technology Trends Opportunity Assessment for Cleantech SectorsMax Tuttman
Walks through the construction of a framework to map how different technology trends interact with cleantech sectors of interest. Key areas of potential are highlighted.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
Inria, Institut national de recherche dédié au numérique, s’installe à French Tech Central pour connecter les entrepreneurs au meilleur de la recherche publique francaise.
Inria invite le CEA List pour un Inria Tech Talk exclusif. Le List, institut de CEA Tech, focalise ses recherches sur les systèmes numériques intelligents. Porteurs d’enjeux économiques et sociétaux majeurs, ses programmes de R&D sont centrés sur le manufacturing avancé, les systèmes embarqués, l’intelligence ambiante et la maîtrise des rayonnements ionisants pour la santé.
Un événement inédit pour décrypter les potentialités du jumeau numérique. Contrepartie digitale d’un système d’informations, cette technologie vous permet d’analyser les risques et de pouvoir les anticiper tout en évaluant leurs impacts.
Arnaud Cuccuru, présentera le temps d’une heure les applications d’un environnement de modélisation en open source et ses cas d’usages.
En effet, cette brique technologique spécifique s’adapte à votre contexte pour vous fournir des solutions personnalisées adaptées à des domaines d’application métiers (par ex., transport, santé, manufacturing et énergies) ou à vos préoccupations telles que la sûreté, la sécurité, ou encore la certificabilité.
Retrouvez la présentation :
https://french-tech-central.com/events/inria-tech-talk-jumeau-numerique/
Please find our presentation for the SPE ABC 2017: FEA based realistic simulation for packaging qualification.
Please do not hesitate to contact us if you would like to discuss any particular topic in detail.
Katmanlı Üretim (Additive manufacturing) bilgi görseliAdem Çelik
3D Yazıcılar tasarım sürecine farklı bir boyut getirdi ancak tasarımı 3D yazdırma prosesi bir kaç adımdan oluşan işlemlerle gerçekleştiriyor. Creo 4.0 Additive Manufacturing Modülü CAD modeliniz ve 3D yazıcınız arasındaki boşluğu kapatarak tasarım, analiz, optimizasyon ve yazdırma işlemlerini tek bir yazılım platformu içerisinde oluşturmanıza imkan sağlayarak bu sorunu ortadan kaldırıyor.
Parametrik kontrol edilen kafes yapılarını oluşturma.
3D Systems ve Stratasys yazıcılarına doğrudan bağlanma.
Yazdırma işlerini ön izleme, doğrulama ve yönetme imkanları ile;
Gözden kaçabilecek hataları ortadan kaldırın ve Prototiplerinizi daha hızlı geliştirin.
Cutting Steelmaking Costs Without Sacrificing Quality. Machine Learning for M...Yandex Data Factory
For further information about Yandex Data Factory solutions,
please contact us at ydf-customer@yandex-team.com
Metallurgy companies must balance two competing demands: keeping production costs to a minimum while still ensuring that the resulting steel composition complies with all requirements. Given how difficult this balance is to strike, you might find it hard to believe that metallurgy companies can actually achieve 5% cost optimisation with no investments in expensive equipment and software. But that is exactly what Magnitogorsk Iron and Steel Works managed to do with the help of Yandex Data Factory’s machine learning technology.
You’ll learn how implementing Yandex Data Factory’s ferroalloy optimisation service has resulted in projected savings of more than $4m a year for Magnitogorsk Iron and Steel Works. We also discuss other advantages these new technologies bring to metallurgy and give practical advice on how to get started with your first machine learning and big data analytics project so that your company can also cut costs while maintaining the same high quality of resultant steel.
Additive Manufacturing Impact on Supply Chain and Production SchedulingVarun Patel
This is presentation of winner of case study competition conducted by GE at SIOM Nashik. The topic evolved around Additive Manufacturing/3D Printing and its commercial application.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 6 focuses on New Materials and Conductive Components.
Addressing Uncertainty How to Model and Solve Energy Optimization Problemsoptimizatiodirectdirect
During the past twenty years, IBM's CPLEX Optimization Studio has been used extensively to solve hard Energy Optimization Problems. CPLEX Optimization Studio's modeling capabilities, fast solvers and easy deployment features empower users to deploy energy applications quickly and reliably. Now, with a newly update superset offering know as Decision Optimization Center, the capabilities for this class of problems is greatly enhanced. Using the Unit Commitment Problem as a paradigm, we will demonstrate the advantages of using Decision Optimization Center, and CPLEX for modeling complex problems and application developments in the Energy Field.
Most importantly we will introduce new research developed for solving problems that address Uncertainty. We will showcase a new research approach that allows the user to test and deploy Stochastic, Robust or Deterministic models all on the same platform. This new approach allows you to truly understand your options as you explore the best plan for your business and develop a robust and repeatable process.
Industrial production process visualization with the Elastic Stack in real-ti...Elasticsearch
Learn how the Mayr-Melnhof Group implemented production process visualization in a highly automated and fragmented industrial, process-control environment with the Elastic Stack.
The current work focuses on simulation based optimization of a complex, safety critical component where it is prohibitively expensive to carry out finite element analysis (FEA) simulations for all possible sample realizations and therefore requires statistical or machine learning techniques for a timely yet accurate solution. The applicability of machine learning further brings the opportunity of performing in-service monitoring using sensor data and thereby performing predictive maintenance.
Sensor-based, Data-driven Optimization of Composites Manufacturing.pptxmarketingnxp
This is the presentation of the speech of Dr. Nicholas Ecke at SAMPE Europe Conference Madrid 2023.
Plastics manufacturing can be unpredictable. Deviations in material batches, moisture content, machine calibration, among other variables, lead to issues in manufacturing quality and final part properties.
New technology has been developed to combine dielectric analysis with machine learning and material models, allowing for dynamic adjustments to machine settings, removing uncertainty from your process, and optimizing cycle times.
Software Engineering Challenges in building AI-based complex systemsIvica Crnkovic
Development of AI-based systems goes far beyond using specific AI-algorithms. The development itself is becoming more complex since data and algorithms become dependent. This presentation lists some of new challenges that AI-developers meet.
Technology Trends Opportunity Assessment for Cleantech SectorsMax Tuttman
Walks through the construction of a framework to map how different technology trends interact with cleantech sectors of interest. Key areas of potential are highlighted.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
Inria, Institut national de recherche dédié au numérique, s’installe à French Tech Central pour connecter les entrepreneurs au meilleur de la recherche publique francaise.
Inria invite le CEA List pour un Inria Tech Talk exclusif. Le List, institut de CEA Tech, focalise ses recherches sur les systèmes numériques intelligents. Porteurs d’enjeux économiques et sociétaux majeurs, ses programmes de R&D sont centrés sur le manufacturing avancé, les systèmes embarqués, l’intelligence ambiante et la maîtrise des rayonnements ionisants pour la santé.
Un événement inédit pour décrypter les potentialités du jumeau numérique. Contrepartie digitale d’un système d’informations, cette technologie vous permet d’analyser les risques et de pouvoir les anticiper tout en évaluant leurs impacts.
Arnaud Cuccuru, présentera le temps d’une heure les applications d’un environnement de modélisation en open source et ses cas d’usages.
En effet, cette brique technologique spécifique s’adapte à votre contexte pour vous fournir des solutions personnalisées adaptées à des domaines d’application métiers (par ex., transport, santé, manufacturing et énergies) ou à vos préoccupations telles que la sûreté, la sécurité, ou encore la certificabilité.
Retrouvez la présentation :
https://french-tech-central.com/events/inria-tech-talk-jumeau-numerique/
Please find our presentation for the SPE ABC 2017: FEA based realistic simulation for packaging qualification.
Please do not hesitate to contact us if you would like to discuss any particular topic in detail.
Katmanlı Üretim (Additive manufacturing) bilgi görseliAdem Çelik
3D Yazıcılar tasarım sürecine farklı bir boyut getirdi ancak tasarımı 3D yazdırma prosesi bir kaç adımdan oluşan işlemlerle gerçekleştiriyor. Creo 4.0 Additive Manufacturing Modülü CAD modeliniz ve 3D yazıcınız arasındaki boşluğu kapatarak tasarım, analiz, optimizasyon ve yazdırma işlemlerini tek bir yazılım platformu içerisinde oluşturmanıza imkan sağlayarak bu sorunu ortadan kaldırıyor.
Parametrik kontrol edilen kafes yapılarını oluşturma.
3D Systems ve Stratasys yazıcılarına doğrudan bağlanma.
Yazdırma işlerini ön izleme, doğrulama ve yönetme imkanları ile;
Gözden kaçabilecek hataları ortadan kaldırın ve Prototiplerinizi daha hızlı geliştirin.
Cutting Steelmaking Costs Without Sacrificing Quality. Machine Learning for M...Yandex Data Factory
For further information about Yandex Data Factory solutions,
please contact us at ydf-customer@yandex-team.com
Metallurgy companies must balance two competing demands: keeping production costs to a minimum while still ensuring that the resulting steel composition complies with all requirements. Given how difficult this balance is to strike, you might find it hard to believe that metallurgy companies can actually achieve 5% cost optimisation with no investments in expensive equipment and software. But that is exactly what Magnitogorsk Iron and Steel Works managed to do with the help of Yandex Data Factory’s machine learning technology.
You’ll learn how implementing Yandex Data Factory’s ferroalloy optimisation service has resulted in projected savings of more than $4m a year for Magnitogorsk Iron and Steel Works. We also discuss other advantages these new technologies bring to metallurgy and give practical advice on how to get started with your first machine learning and big data analytics project so that your company can also cut costs while maintaining the same high quality of resultant steel.
Additive Manufacturing Impact on Supply Chain and Production SchedulingVarun Patel
This is presentation of winner of case study competition conducted by GE at SIOM Nashik. The topic evolved around Additive Manufacturing/3D Printing and its commercial application.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 6 focuses on New Materials and Conductive Components.
Addressing Uncertainty How to Model and Solve Energy Optimization Problemsoptimizatiodirectdirect
During the past twenty years, IBM's CPLEX Optimization Studio has been used extensively to solve hard Energy Optimization Problems. CPLEX Optimization Studio's modeling capabilities, fast solvers and easy deployment features empower users to deploy energy applications quickly and reliably. Now, with a newly update superset offering know as Decision Optimization Center, the capabilities for this class of problems is greatly enhanced. Using the Unit Commitment Problem as a paradigm, we will demonstrate the advantages of using Decision Optimization Center, and CPLEX for modeling complex problems and application developments in the Energy Field.
Most importantly we will introduce new research developed for solving problems that address Uncertainty. We will showcase a new research approach that allows the user to test and deploy Stochastic, Robust or Deterministic models all on the same platform. This new approach allows you to truly understand your options as you explore the best plan for your business and develop a robust and repeatable process.
Similar to ICME THE BACKBONE FOR LIGHTWEIGHT VEHICLE DEVELOPMENT (20)
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What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
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Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
The Tata Group, a titan of Indian industry, is making waves with its advanced talks with Taiwanese chipmakers Powerchip Semiconductor Manufacturing Corporation (PSMC) and UMC Group. The goal? Establishing a cutting-edge semiconductor fabrication unit (fab) in Dholera, Gujarat. This isn’t just any project; it’s a potential game changer for India’s chipmaking aspirations and a boon for investors seeking promising residential projects in dholera sir.
Visit : https://www.avirahi.com/blog/tata-group-dials-taiwan-for-its-chipmaking-ambition-in-gujarats-dholera/
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
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➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
Attending a job Interview for B1 and B2 Englsih learnersErika906060
It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
ICME THE BACKBONE FOR LIGHTWEIGHT VEHICLE DEVELOPMENT
1. 1 | hexagonmi.com/mscsoftware
ICME THE BACKBONE FOR
LIGHTWEIGHT VEHICLE DEVELOPMENT
Michela GIUGLIANO AURICCHIO
Business Development Manager EMEA
Materials & ICME CoE and Digital Enterprise Solutions
June 9th – Automotive Lightweight Materials Europe 2022
2. 2 | hexagonmi.com/mscsoftware
Materials are at the heart of any
products and innovations
Artificial
intelligence
Cloud
Ecosystem
Enterprise
integration
Data
management
Design &
engineering
Production
Quality &
Metrology
Materials
&
ICME
A design only comes to life
when materials enter manufacturing
ICME: Integrated Computational Material Engineering
3. Materials Digitization
is in our DNA
Hexagon MI is a pioneer and leader in ICME,
materials digitization and digital transformation
by building convergence between the physical
and digital worlds throughout the entire process:
from concept to customer.
150+
developments per year
Over 650
customers worldwide
Satisfied customers:
95% renewal rate
PHYSICAL
VIRTUAL
#1 in ICME
Trusted Material
Data Management
Enriching CAE
Digital
Material Lab
Sustainability &
compliance
Connecting real
& virtual
4. 4 | Hexagon Overview
Hexagon Materials solution to drive innovations across the product
lifecycle
ICME for Manufacturing
• Zero prototyping
• Defects, curing, forming, ..
• Additive manufacturing
Material development & testing
• New materials, sustainable materials
• Thermoplastic, thermoset, metals,
ceramics, coating, …
Material Data Management
• Single point of truth for material
• Traceability
• Efficiency
Material Cost
Processability &
Assembly
Function
integration
Mechanical
strength
Impact/Crash
Durability
Weight
Recyclability
Vibration
absorption
Predictivity & reliability
• Stiffness, strength, plasticity,..
• Creep, Vibration, Durability, ..
• As designed vs as-manufactured
Materials Intelligence
• Leverage AI, ML & materials modeling
• Material data enrichment
5. 5 | Hexagon Overview
5
Accelerating material development, testing and understanding
• Model materials
• Analyze and investigate material properties
• Investigate material’s behavior and damage
mechanisms
• Account for variability and defects
• Innovate and tune material’s properties
• Optimize microstructure and manufacturing
SFRP
DFC
CFRP SMC
Foams
Rubber
Metals CMC
6. 6 | Hexagon Overview
6
The Digital Material laboratory
Import or Design Solve Analyze Explore
CPU time: 2
minutes
CPU time: 2 minutes
Fast &
accurate
Multi -materials
& -physics
Better decisions
with additional
insights
7. 7 | Hexagon Overview
7
Anticipating defects, reducing costs, enhancing quality
Digital transformation in materials’ manufacturing
• The local microstructure and defects can be assessed
• By simulation
• Directly from the machines (toolpath)
• By CT-Scans
• Some applications in the field of composites
• Residual stresses after thermoforming
• Curing of thermoset
• Additive manufacturing
• Effects of defects (wrinkles, voids, …)
Quality
Functionality
Productivity
Zero waste
Innovation
Virtual testing
Time to market
Manufacturing
Materials
Design As Designed
As Manufactured
8. 8 | Hexagon Overview
8
Anticipating defects, reducing costs, enhancing quality
Digital transformation in materials’ manufacturing
• Interface with VG Studio from Volume Graphics to
account for the ply/fiber orientation, gaps, defects, …
• Perform FEA on the real microstructure combined with
advanced & accurate material models
As-manufactured part’s performance
Scan
Transfer & assign relevant
material model
Solve
Or 3rd party SW
9. 9 | Hexagon Overview
9
Application to polymer & composite additive manufacturing
Manufacturing and anticipating defects
Digimat-AM is a process simulation software
dedicated to the additive manufacturing of plastics
and composite materials. It allows engineers to
predict warpage, residual stresses and material
performance based on the local temperature
history during the build process.
• FDM & SLS
• Warpage & thermal analyses
• Unfilled & reinforced materials
• Crystallinity
• Counterwarped shape
• Layer adhesion
• Support failure
• Automatic superimposition STL/toolpath
• Inherent strain & full thermomechanical solvers
Capabilities
Simulating the AM process aims at
printing right the first time!
10. 10 | Hexagon Overview
10
Application to polymer & composite additive manufacturing
Designing reliable products
• Predictive simulations before print
• By accounting for the toolpath from the machine
• Accurate material cards including failure behavior
• Validate toolpath and material choices before print
11. 11 | Hexagon Overview
11
Enriching FEA with advanced material information connected to manufacturing
Designing reliable products
Process
simulation
Design
Structural
simulation
Post-processing Pre-processing
Batch mode
to enrich structural input decks with
lower effort and automate workflows
Valve gate control
to manage gate sequencing
during fiber orientation
tensor estimation
Acknowledgement for the model
to CCSA/GMU and the FHWA
Fatigue post-processing
improved user experience with hot
spot detection, cut views and more
Advanced creep
with temperature and strain rate
dependent elasto-plastic model
Strain
Time
Multi-physics
Crash and NVH
Lifetime
Static
12. Unified
Data Source
Global
Accessibility
Highly
Secure
AI Oriented All-in-one
Platform
Full
Traceability
MaterialCenter
Supplier /
LIMS Data
Materials &
Lab
Manufacturing
& Methods
Physical & Virtual
Testing
CAD – Config
Storage, link to PLM
Design Values
Manufacturing Data
Capture & Cost
Eco-Design -
Compliance
Sustainability
Design &
Simulation
Single Source of Truth for Materials
13. 13 | Hexagon Overview
13
Unlocking the power of materials data
Industry Context Challenges Innovative Solutions
• Design times shrink
• Increasing demand for accurate FEA
• High demand for accurate material cards
• Material data isn’t always available when
design work starts
• Material cards require a substantial amount
of material data
Leverage AI & ML to generate material data
• AI/ML only
• AI/ML + physics based meta-models
• AI/ML + virtual testing using ICME modeling solutions
Properties
Materials
Exp. test data
Properties
Materials
Fill in your Private DB Train AI/ML Engine
Exp. test data
Virtual data
Virtually Generate Material Data
Populate DB
Using MaterialCenter
• Structure, traceability
• Connect, reuse, collaborate
>> often Incomplete data sets
MISSING ELEMENT / DATA
External Databanks (optional)
Virtual
Real
Accuracy Level
14. 14 | Hexagon Overview
14
Materials data enrichment: Fast, accurate and agile by combining artificial
intelligence and material modeling
Time saving
120,000 €
Cost saving
80 days
Material waste
27 kg
Energy
XXX
15. 15 | Hexagon Overview
Air Intake
Bearing System
Electronic Connectors
Aircooler Brackets
Lower-Pillar
Oil Pan
Car Roof
FAN System
Engine Cover
Engine
Cooling Radiator
Seat
Pillar
Front-end
Pedal
Air Duct
Airbag Housings
Pedestrian
Protection
Viscous Fans
Side Impact
Pads
Engine Mount
Car Body
Oil Filter
Overmol
ded
Lightweighting with
no compromise of
safety & quality
16. 17 | Hexagon Overview
17
Could we develop faster lightweight component at lower cost?
Using efficiently materials in part’s development
❑ Lightweight vs Performance
❑ Predictive simulation
❑ Anticipate manufacturing issues
❑ Quick decision
❑ Cost optimization
❑ Multiscale predictive FEA
❑ Account for local microstructure
induced by manufacturing
❑ Account for manufacturing defect
❑ Virtual prototyping
❑ Weight reduction (up to 40%)
❑ Reduce cost of changes by 70%
❑ Design to cost & to manufacturing
Industry needs Field of applications
SFRP
DFC
CFRP SMC
Foams
Rubber
Metals CMC
Productivity Cost savings Sustainability
Predictivity
Accuracy
What ICME enables
17. 18 | Hexagon Overview
18
Suspension control arm strength
The Challenge MSC Simulation Solution Value Realized
• Three load cases tested
• Dry
• Conditioned
• Conditioned & restrained
• Digimat result showed correct
failure locations, isotropic did not
• Digimat showed average of ~6%
overestimation of force at failure
• Isotropic material properties
were not able to capture
failure locations
• Refinement of material, and
anisotropic modeling, are
investigated to improve FEA
accuracy
• Digimat creates the link between the
manufacturing process & the
structural analysis
• Fiber orientations are brought into
the anisotropic Digimat material
model and accounted for in the three
loading scenarios
18. 19 | Hexagon Overview
19
Valeo engine cooling flap
The Challenge MSC Simulation Solution Value Realized
• On average, Digimat showed
much better correlation to
experimental results than the
isotropic assumption
• Mode shapes were also
noticeably different between the
two methodologies
• Modal analysis is needed on
the flaps of the engine cooling
system
• Eigenfrequencies and mode
shapes, calculated through
simulation, are then compared
to experimental
measurements
• Digimat created the link between the
manufacturing process and
structural simulation
• Digimat was compared to Valeo’s
isotropic assumption
Fan System
Flaps Shroud
Motor
Fan
Fiber Orientation Tensor
19. 20 | Hexagon Overview
20
Ford Oil pan
The Challenge MSC Simulation Solution Value Realized
• Frequencies: Digimat shows
average improvement of 8% over
isotropic assumption
• Acceleration peaks: Digimat
shows average improvement of
43% over isotropic assumption
• NVH simulation accuracy
using standard approaches
may require corrective action,
potentially involving mass
increase, due to spatially
varying microstructure leading
to stiffness and damping
discrepancy
• Digimat provides the link between
the process simulation and the
structural simulation
• Digimat provides an anisotropic and
frequency dependent solution to
address Ford’s needs
Z
Experiment
Digimat
Isotropic
20. 21 | Hexagon Overview
21
Volvo front end carrier
The Challenge MSC Simulation Solution Value Realized
• Superpositioned video of test vs.
FEA simulation is shown on left
• Force-displacement curve of FEA
with Digimat vs. experimental
tests is shown on top right
• Dynamic impact tests on the
front end carrier are vital to
determining a vehicles
crashworthiness
• Volvo was seeing issues with
their correlation when using
their conventional isotropic
material assumption
• Digimat was incorporated into their
workflow and used to connect the
manufacturing process with the
structural simulation
• Strain rate dependent material
model was calibrated and used to
perform correlation work on a front
end carrier
Volvo XC90 FEC
21. 22 | Hexagon Overview
22
Dynamic Strength
The Challenge MSC Simulation Solution Value Realized
• Faurecia looking to capture three
significant events – rib buckling,
rib failure & failure evolution
• Only Digimat was able to capture
all three failure phenomena
accurately
• Dynamic testing is needed to
validate the metal-to-plastic
seat pan transition to
determine safety
• Faurecia was seeing issues
with their isotropic material
model & their knocked down
isotropic material model
• Digimat created the link between the
manufacturing process and
structural simulation
• Digimat was compared to isotropic
(ISO527) and knocked down
isotropic (ISO527 scaled) material
models
Event ISO 527 ISO 527
scaled
MMI / Digimat
Rib buckling - + +
Rib failure o - +
Failure evolution - - +
22. 23 | Hexagon Overview
23
Automotive OEM inner seat part made of SMC material
The Challenge MSC Simulation Solution Value Realized
• Digimat simulation achieves
much better fit with respect to
test data for most load cases,
including head impact (puncture)
• Provides good indication of hot
spot localizations
• Material anisotropy, thus far,
considered as isotropic
• Failure initiation and damage
propagation
• Weld lines – weakest SMC
defect
• Rely on Digimat mean-field and
compression molding technology to
account for material anisotropy
• Use latest damage propagation laws
for discontinuous fibers
• Identify weld line locations and
account for weakness at structural
level with knock down factor
Test data
Digimat
24. 25 | Hexagon Overview
Building, Managing and Sharing Data is Challenging!
Conditions
Rays
Temperature
Moisture
Liquids
Salts Speed
Tension
Compression
Shear
Creep
High speed
Damping
Tests
Fatigue
Materials
Foams
Metals
Composites
Plastics
Special materials
Rubber
25. 26 | Hexagon Overview
New grades in our Public Material database
+24% in one year!
26. 27 World Materials Forum | 19 June 2021 | Closing Session: WMF Top 10 Technologies | Speaker: Ola Rollèn, Hexagon President and CEO
Thank you
27 | hexagonmi.com
HexagonMI
@HexagonMI
@hexagon_mi
Chat
+39 338 7219 336
Connect
www.linkedin.com/in/michela-giugliano-auricchio
michela.giuglianoauricchio@hexagon.com
Reach out
