IPU
Kongens Lyngby, Denmark
IPU
Kongens Lyngby, Denmark

Time filter

Source Type

News Article | December 20, 2016
Site: marketersmedia.com

— According to the new market research report "Deep Learning Market by Application (Image Recognition, Signal Recognition, Data Mining), Offering (Hardware (Von Neumann and Neuromorphic Chip), and Software), End-User Industry, and Geography - Global Forecasts to 2022", the deep learning market is expected to be worth USD 1,722.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. The deep learning market has a huge potential across various industries such as advertisement, finance, and automotive. The major factors driving the deep learning market globally are the robust R&D for the development of better processing hardware and increasing adoption of cloud-based technology for deep learning. Browse 71 market data tables and 67 figures spread through 180 pages and in-depth TOC on “Deep Learning Market - Global Forecasts to 2022” The market for the data mining application is expected to grow at the highest rate between 2016 and 2022 The deep learning market for data mining application is expected to grow at the highest CAGR between 2016 and 2022. The increasing usage of deep learning in data analytics, cyber security, fraud detection, and database systems is fueling the growth of data mining applications in the deep learning market. Medical industries generate huge amounts of data sets related to medication, patient details, and diagnosis. This data is converted into valuable patterns and is used to forecast future trends. Thus, data mining is expected to witness the highest growth rate in the medical industry. Deep learning hardware market expected to grow at the highest rate between 2016 and 2022 The high growth rate of the hardware market for deep learning is attributed to the growing need for hardware platforms with a high computing power to run deep learning algorithms. There is increasing competition among established as well as startup players, leading to new product developments including both hardware development and software platforms to run deep learning algorithms and programs. For instance, Graphcore (a U.K.-based company) is developing the intelligent processing unit (IPU) for machine learning technology for use in applications from driverless cars to cloud computing. Some of the companies involved in the development of hardware for the deep learning technique are Google, Inc. (U.S.), Microsoft Corporation (U.S.), Intel Corporation (U.S.), Qualcomm, Inc. (U.S.), IBM Corporation (U.S.), and others. North America leads the deep learning market in terms of market size North America is currently leading the deep learning market and is projected to be in the leading position for the next few years owing to the wide adoption of deep learning technology. The growth of the deep learning market in North America is attributed to the high government funding, presence of leading players, and strong technical base. The major driving factor for the market in North America is the presence of prominent vendors of deep learning technology such as IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google, Inc. (U.S.), Facebook, Inc. (U.S.), and Qualcomm, Inc. (U.S.), among others as well as hardware providers such as NVIDIA Corporation (U.S.) and Intel Corporation (U.S.). North America has been receptive toward adopting deep learning technology within organizations for safeguarding content from piracy and data breaches, web and network threat security, cyber-attacks, and severe data losses. MarketsandMarkets is the largest market research firm worldwide in terms of annually published premium market research reports. Serving 1700 global fortune enterprises with more than 1200 premium studies in a year, M&M is catering to a multitude of clients across 8 different industrial verticals. We specialize in consulting assignments and business research across high growth markets, cutting edge technologies and newer applications. Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model – GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. M&M’s flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. The new included chapters on Methodology and Benchmarking presented with high quality analytical infographics in our reports gives complete visibility of how the numbers have been arrived and defend the accuracy of the numbers. We at MarketsandMarkets are inspired to help our clients grow by providing apt business insight with our huge market intelligence repository. For more information, please visit http://www.marketsandmarkets.com/Market-Reports/deep-learning-market-107369271.html


News Article | November 24, 2016
Site: www.prnewswire.co.uk

According to the new market research report "Deep Learning Market by Application (Image Recognition, Signal Recognition, Data Mining), Offering (Hardware (Von Neumann and Neuromorphic Chip), and Software), End-User Industry, and Geography - Global Forecasts to 2022", published by MarketsandMarkets, the market is expected to be worth USD 1,722.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. (Logo: http://photos.prnewswire.com/prnh/20160303/792302 ) Browse 71 market data Tables and 67 Figures spread through 180 Pages and in-depth TOC on "Deep Learning Market" Early buyers will receive 10% customization on this report. The deep learning market has a huge potential across various industries such as advertisement, finance, and automotive. The major factors driving the deep learning market globally are the robust R&D for the development of better processing hardware and increasing adoption of cloud-based technology for deep learning. The market for the data mining application is expected to grow at the highest rate between 2016 and 2022 The Deep Learning Market for data mining application is expected to grow at the highest CAGR between 2016 and 2022. The increasing usage of deep learning in data analytics, cyber security, fraud detection, and database systems is fueling the growth of data mining applications in the deep learning market. Medical industries generate huge amounts of data sets related to medication, patient details, and diagnosis. This data is converted into valuable patterns and is used to forecast future trends. Thus, data mining is expected to witness the highest growth rate in the medical industry. Deep learning hardware market expected to grow at the highest rate between 2016 and 2022 The high growth rate of the hardware market for deep learning is attributed to the growing need for hardware platforms with a high computing power to run deep learning algorithms. There is increasing competition among established as well as startup players, leading to new product developments including both hardware development and software platforms to run deep learning algorithms and programs. For instance, Graphcore (a U.K.-based company) is developing the intelligent processing unit (IPU) for machine learning technology for use in applications from driverless cars to cloud computing. Some of the companies involved in the development of hardware for the deep learning technique are Google, Inc. (U.S.), Microsoft Corporation (U.S.), Intel Corporation (U.S.), Qualcomm, Inc. (U.S.), IBM Corporation (U.S.), and others. North America leads the deep learning market in terms of market size North America is currently leading the deep learning market and is projected to be in the leading position for the next few years owing to the wide adoption of deep learning technology. The growth of the deep learning market in North America is attributed to the high government funding, presence of leading players, and strong technical base. The major driving factor for the market in North America is the presence of prominent vendors of deep learning technology such as IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google, Inc. (U.S.), Facebook, Inc. (U.S.), and Qualcomm, Inc. (U.S.), among others as well as hardware providers such as NVIDIA Corporation (U.S.) and Intel Corporation (U.S.). North America has been receptive toward adopting deep learning technology within organizations for safeguarding content from piracy and data breaches, web and network threat security, cyber-attacks, and severe data losses. Neuromorphic Computing Market by Offering (Hardware, Software), Application (Image Recognition, Signal Recognition, Data Mining), Industry (Aerospace & Defense, IT & Telecom, Automotive, Medical & Industrial) and Geography - Global Forecast to 2022 http://www.marketsandmarkets.com/Market-Reports/neuromorphic-chip-market-227703024.html MarketsandMarkets is the largest market research firm worldwide in terms of annually published premium market research reports. Serving 1700 global fortune enterprises with more than 1200 premium studies in a year, M&M is catering to a multitude of clients across 8 different industrial verticals. We specialize in consulting assignments and business research across high growth markets, cutting edge technologies and newer applications. Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model - GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. M&M's flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. The new included chapters on Methodology and Benchmarking presented with high quality analytical info graphics in our reports gives complete visibility of how the numbers have been arrived and defend the accuracy of the numbers. We at MarketsandMarkets are inspired to help our clients grow by providing apt business insight with our huge market intelligence repository.


News Article | October 31, 2016
Site: www.prnewswire.co.uk

Graphcore Ltd, a startup developing new technology to deliver massive acceleration for machine learning and AI applications, has completed a $30m Series-A funding round from a world-class line up of venture capital and strategic investors. The funding was led by Robert Bosch Venture Capital GmbH with Samsung Catalyst Fund and other major technology firms, alongside leading venture capital funds from London, Silicon Valley and Israel: Amadeus Capital Partners, C4 Ventures, Draper Esprit plc, Foundation Capital and Pitango Venture Capital. Graphcore has spent the last two years building an experienced hardware and software team to develop a system designed from the ground up to accelerate both current and next generation machine intelligence applications such as natural language dialogue, autonomous vehicles and personalized medicines. The company will bring its IPU (Intelligent Processing Unit) system to market in 2017 with the IPU-Appliance™ designed to lower the cost of accelerating AI applications in cloud and enterprise datacenters. The IPU-Appliance aims to increase the performance of both training and inference by between 10x and 100x compared to the fastest systems in use today. The company also plans to make its low power IPU technology available for embedded consumer applications including autonomous cars, collaborative robots and intelligent mobile devices. IPU systems will accelerate the full range of training, inference, and prediction approaches. Its huge computational resources and software tools and libraries are flexible and easy to use, allowing researchers to explore machine intelligence across a much broader front than the current focus on feed-forward neural networks. This technology will enable recent success in deep learning to evolve rapidly towards useful, general artificial intelligence. A Bank of America Merrill Lynch report citing IDC research recently predicted that the AI industry will exceed $70 billion by 2020 and Tractica predicts that spending on hardware for deep learning projects will grow from $436 million in 2015 to $41.5 billion by 2024. Graphcore CEO and co-founder, Nigel Toon, said, "Machine intelligence will have a bigger impact on our lives over the next 10 years than mobile technology has had in the last two decades. Next generation machine intelligence will allow us to translate foreign languages in real-time, help diagnose illnesses and develop personalized treatments, control robots that clean our houses and offices, drive cars autonomously and provide us with intelligent digital assistants that can help us organize our busy lives. The IPU is the first system specifically designed for machine intelligence." Graphcore CTO and co-founder, Simon Knowles, said, "For 70 years we have built computers to do exactly what a software program says, step by step. The program is an algorithm for solving a problem, and that algorithm must come from a human. Today's computers do not actually help the human to solve the problem. Machine intelligence is turning that on its head.  Intelligent machines can analyse data like humans, discover underlying patterns, and effectively write their own programs. They can then adapt their behaviour through trial and error, like humans. They can deal with probabilities and exercise judgement in the presence of uncertainty, like humans." "We are at the dawn of this second age of computing, in which machines are given the capacity for intelligence. The value to society of intelligent computing will be far greater than that of all computing so far. Silicon is still our best technology for building such machines, but the design details will be quite different from today's microprocessors. Graphcore is at the vanguard of this revolution in computer design and has assembled a peerless engineering team to deliver the first processors designed from scratch for general intelligence." Linley Gwennap principal analyst of The Linley Group commented, "Machine intelligence and deep learning applications are now popular enough to justify new silicon approaches. The team at Graphcore has a strong track record of creating successful new processors for emerging markets." Dr Hongquan Jiang, Partner at Robert Bosch Venture Capital GmbH added, "Graphcore has a unique technology that has massive potential in the fast emerging market for deep learning. A new processor technology is needed for intelligent systems and Graphcore has the first technology that we have seen which really delivers the performance and efficiency needed for this style of compute. We are excited to have led this very significant investment round." "Graphcore's approach is unique in its capability to enable advanced intelligent systems," said Ekaterina Almasque, Managing Director, Samsung Catalyst Fund, Europe. "It closes the gap between the level of intelligence we want to see on edge devices and compute limitations of existing hardware architectures. At Samsung Catalyst Fund, we invest in disruptive companies in this space, and believe tremendous value will be created in the next 10 years  by artificial intelligence applications. Graphcore will play an important role as a key enabling technology." Hermann Hauser, co-founder at Amadeus Capital Partners and a renowned technology entrepreneur, said, "I have worked with Nigel and Simon before in their previous companies where they achieved over $1bn in successful exits for their investors. The team they have assembled is second to none. Machine learning is becoming a major market and Graphcore has the technology to lead this next wave of computing." Bill Elmore, General Partner and co-founder at Foundation Capital, one of Silicon-Valley's top venture capital firms behind a large number of highly successful companies including Netflix, said, "Graphcore will lower the cost of accelerating AI applications in the cloud. This is exactly the type of world-class systems company, with breakthrough technology, and a great team, that Foundation Capital supports." Simon Cook, CEO Draper Esprit Plc, stated, "Graphcore is one of the first investments made since we publicly listed in London and Dublin. This is a sector we know well and a founding team that we have successfully backed before. Nigel and Simon have created an exciting company developing next generation processor technology and we are pleased to be investing at this key stage in its development." Pascal Cagni, Founding Partner of C4 Ventures, Apple GM and VP EMEA (2000-2012), said: "Since its foundation C4 Ventures has been backing hardware companies revolutionising their sector and we believe Graphcore's disruptive technology is a game changer in the computing field. Graphcore's solution pushes further the boundaries of Machine Intelligence, which will unlock value across every industry." Eyal Niv, Managing General Partner at Pitango Venture Capital, said: "We are very excited to become part of Graphcore and to be backing great entrepreneur like Nigel and Simon. I believe we are on the verge of a new and smarter era, in which computer intelligence, machine learning and deep learning, will transform every aspect of our lives. Smart personalized medicine, autonomous transportation and robotics, smart infrastructure and accurate business prediction are just some of the areas which will be transformed and immensely improved by machine learning technologies. I really believe machine learning will bring about the biggest transformation ever, bigger than the internet, mobile and social put together." Graphcore was advised by Orrick, Herrington & Sutcliffe in the financing. Graphcore is a systems and silicon company that has created the Intelligent Processing Unit (IPU) to accelerate machine intelligence, making it faster and easier to deliver AI applications. Graphcore is backed by leading venture capital and strategic investors including Amadeus Capital Partners, C4 Ventures, Draper Esprit, Foundation Capital, Pitango Venture Capital, Robert Bosch Venture Capital and Samsung SSIC, and is headquartered in Bristol UK. More information can be found at https://www.graphcore.ai


News Article | December 21, 2016
Site: www.businesswire.com

美國加州聖荷西--(BUSINESS WIRE)--(美國商業資訊)--Tessera股份有限公司(Nasdaq: TSRA) (Tessera)今日宣布其獨資子公司FotoNation有限公司和芯原股份有限公司(VeriSilicon,芯原)簽訂合約,雙方共同開發下一代影像處理平臺,以提供領先的可程式設計、低功耗、高性能及小尺寸的電腦視覺(CV)、計算成像(CI)和深度學習解決方案。該已市場化的全新IP平臺名為IPU 2.0,將從2017年一季開始正式開放客戶授權和設計,可為包括監控、汽車電子、行動裝置、物聯網等廣泛的應用提供一個統一的程式設計環境以及預整合的影像特性。 FotoNation的混合成像方法與芯原的平行計算能力緊密結合,使得IPU 2.0成為向市場提供業經驗證的電腦視覺、計算成像和深度學習解決方案的理想平臺。IPU 2.0透過OpenVX、OpenCL、OpenCV、Caffe、TensorFlow等開放技術提供差異化,促進了電腦視覺、計算成像和深度神經網路功能之間的無縫和並行處理。FotoNation和芯原之間的共同技術及IP開發協議整合了雙方各自的獨特技術,為市場


SAN JOSE, Calif.--(BUSINESS WIRE)--Tessera Holding Corporation (Nasdaq: TSRA) (the “Company” or “Tessera”) today announced that its wholly owned subsidiary, FotoNation Limited, and VeriSilicon Holdings Co., Ltd. (“VeriSilicon”), have entered into an agreement to jointly develop a next generation image processing platform that offers best-in-class programmability, power, performance and area for computer vision (CV), computational imaging (CI) and deep learning. The market-ready IP platform, named IPU 2.0, will be available for customer license and design in the first quarter of 2017. IPU 2.0 offers a unified programing environment and pre-integrated imaging features for a wide range of applications across surveillance, automotive, mobile, IoT and more. FotoNation’s hybrid imaging approach tightly coupled with VeriSilicon’s parallel computation capabilities makes IPU 2.0 the ideal platform to deliver industry proven computer vision, computational imaging and deep learning solutions to the market. IPU 2.0 offers differentiation via open initiatives such as: OpenVX, OpenCL, OpenCV, Caffe and TensorFlow, facilitating seamless, concurrent processing between computer vision, computational imaging and deep neural networking features. The joint technology and IP development agreement combines unique technologies from both companies and brings to market an imaging platform preloaded with a wide range of industry proven real-time parallel-execution imaging features. According to a recent report by the market intelligence firm, Tractica, the computer vision hardware and software market is estimated to grow from $6.6 billion in 2015 to $48.6 billion by the year 2022.1 Tractica credits automotive, sports and entertainment, consumer, robotics and machine vision, security and surveillance, agriculture, retail and medical markets for growing market demand for high performance, fast and low-power embedded vision solutions. “With the introduction of IPU2.0, FotoNation and VeriSilicon are raising the bar for computer vision and computational imaging technologies by offering the best combination of programmability, power, performance and area in the market,” said Sumat Mehra, senior vice president and general manager, FotoNation. “We have teamed up with VeriSilicon to help our OEM and ODM customers accelerate the development and delivery of end-to-end integrated computer vision applications and systems with our latest CV, CI and deep learning capabilities. These new solutions fill an important need in the market as OEMs and ODMs integrate computer vision and advanced imaging applications across their product portfolios.” “VeriSilicon’s proven and scalable vision processor core family has been adopted by world’s leading automotive and surveillance suppliers over the years,” said Weijin Dai, EVP, chief strategy officer and general manager of the IP Division, VeriSilicon. “These highly power efficient, scalable processors with deep learning CNN technology in a unified programing environment unleashes great potential in an increasingly connected world with big data analytics and artificial intelligence. Working closely with FotoNation, we will jointly provide the total solution to address the challenges in intelligent devices.” For more information, or to get an early preview of the FotoNation and VeriSilicon solutions at CES, please visit the FotoNation suite (Westgate Hotel, North Tower, Floor 23, Suite 121) or the VeriSilicon suite (Westgate Hotel, North Tower, Floor 26, Suite 121). Tessera Holding Corporation is the parent company of Tessera, DTS, FotoNation and Invensas. We are one of the world’s leading product and technology licensing companies. Our technologies and intellectual property are deployed in areas such as premium audio, computational imaging, computer vision, mobile computing and communications, memory, data storage, 3D semiconductor interconnect and packaging. We invent smart sight and sound technologies that enhance and help to transform the human connected experience. For more information, call +1 408-321-6000 or visit www.tesseraholdingcorporation.com. Tessera, FotoNation, and their respective logos, are trademarks or registered trademarks of affiliated companies of Tessera Holding Corporation in the United States and other countries. All other company, brand and product names may be trademarks or registered trademarks of their respective companies. About VeriSilicon Holdings Co. Ltd. VeriSilicon Holdings Co., Ltd. is a Silicon Platform as a Service (SiPaaSTM) company that provides IP-centric, platform based custom silicon solutions and end-to-end semiconductor turnkey services for a wide range of applications across a wide variety of end markets including mobile internet devices, datacenters, the Internet of Things (IoT), wearable electronics, smart homes, and automotive. VeriSilicon's SiPaaS solutions shorten design cycle, enhance quality, and reduce risk. The breadth and flexibility of our SiPaaS solutions make it an attractive alternative for a variety of customer types, including both emerging and established semiconductor companies, Original Equipment Manufacturers (OEMs), Original Design Manufacturers (ODMs), and large Internet platform companies. VeriSilicon's silicon platforms include licensable Vivante GPU cores and vision image processors, ZSP® (digital signal processor) based HD audio, HD voice, multi-band and multi-mode wireless platforms, Hantro HD video platforms, wearable electronics platforms, IoT platforms, mixed signal NUI (natural user interface) platforms for voice, motion and touch interface. VeriSilicon's custom silicon turnkey service encompasses design service that combines its technology solutions and value-added mixed signal IP portfolio targeted for a wide range of process technology nodes, including advanced nodes like 28nm and 22nm FD-SOI, FinFET and provide product engineering service for System on a Chip (SoC) as well as System in a Package (SiP). Founded in 2001 and headquartered in Shanghai, China, VeriSilicon has over 600 employees with six R&D centers (China, US and Finland) and nine sales offices worldwide. This press release contains forward-looking statements, which are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements involve risks and uncertainties that could cause actual results to differ significantly from those projected, particularly with respect to the expected benefits of FotoNation’s agreement with VeriSilicon; the availability, characteristics, benefits and features of IPU 2.0; and the projected growth of the computer vision hardware and software market. Material factors that may cause results to differ from the statements made include the plans or operations relating to the businesses of the Company; market or industry conditions; changes in patent laws, regulation or enforcement, or other factors that might affect the Company's ability to protect or realize the value of its intellectual property; the expiration of license agreements and the cessation of related royalty income; the failure, inability or refusal of licensees to pay royalties; initiation, delays, setbacks or losses relating to the Company's intellectual property or intellectual property litigations, or invalidation or limitation of key patents; fluctuations in operating results due to the timing of new license agreements and royalties, or due to legal costs; the risk of a decline in demand for semiconductors and products utilizing FotoNation technologies; failure by the industry to use technologies covered by the Company's patents; the expiration of the Company's patents; the Company's ability to successfully complete and integrate acquisitions of businesses; the risk of loss of, or decreases in production orders from, customers of acquired businesses; financial and regulatory risks associated with the international nature of the Company's businesses; failure of the Company's products to achieve technological feasibility or profitability; failure to successfully commercialize the Company's products; changes in demand for the products of the Company's customers; failure to successfully commercialize the Company's products; changes in demand for the products of the Company's customers; limited opportunities to license technologies due to high concentration in the markets for semiconductors and related products; the impact of competing technologies on the demand for the Company's technologies; failure to realize the anticipated benefits of the Company’s recent acquisition of DTS, Inc., including as a result of integrating the business of DTS; pricing trends, including the Company's ability to achieve economies of scale; the expected amount and timing of cost savings and operating synergies; and other developments in the markets that the Company operates, as well as management's response to any of the aforementioned factors. You are cautioned not to place undue reliance on the forward-looking statements, which speak only as of the date of this release. The foregoing review of important factors should not be construed as exhaustive and should be read in conjunction with the other cautionary statements that are included herein and elsewhere, including the Risk Factors included in the Company’s recent reports on Form 10-K and Form 10-Q and other documents of the Company on file with the Securities and Exchange Commission (the "SEC"). Any forward-looking statements made or incorporated by reference herein are qualified in their entirety by these cautionary statements, and there can be no assurance that the actual results or developments anticipated by the Company will be realized or, even if substantially realized, that they will have the expected consequences to, or effects on, the Company or its business or operations. Except to the extent required by applicable law, the Company undertakes no obligation to update publicly or revise any forward-looking statement, whether as a result of new information, future developments or otherwise.


Eriksen R.S.,IPU | Arentoft M.,IPU | Gronbaek J.,Strecon | Bay N.,Technical University of Denmark
CIRP Annals - Manufacturing Technology | Year: 2012

The tool surface topography is often a key parameter in the tribological performance of modern metal forming tools. A new generation of multifunctional surfaces is achieved by combination of conventional tool manufacturing processes with a novel Robot Assisted Polishing process. This novel surface texturing method allows for a large degree of freedom in specifying surface characteristics and facilitates a high degree of reproducibility between samples surfaces. A series of strip reduction tests, equivalent to a metal forming ironing process, are conducted to benchmark the tribological performance of 15 generated tool surfaces. © 2012 CIRP.


Mahshid R.,Technical University of Denmark | Hansen H.N.,Technical University of Denmark | Arentoft M.,IPU
Procedia Engineering | Year: 2014

Multi-step micro bulk forming is characterized by complex processes and high precision requirements. Several process parameters influence on accuracy of micro forged parts where small tolerances in the order of few μm are in demand. The paper introduces a high performance transfer press for micro cold bulk forming. A methodology for selection of linear motors on the bases of the process parameters was obtained. In order to examine the effectiveness of the machine, specific geometry was investigated for production. Kinematic parameters were found for a production rate of 200 strokes per minute. A forged part with three different diameters in height was produced in a two-stage forming process using the introduced transfer press. © 2014 The Authors. Published by Elsevier Ltd.


Arentoft M.,IPU | Eriksen R.S.,IPU | H.n.hansen,Technical University of Denmark | Paldan N.A.,IPU
CIRP Annals - Manufacturing Technology | Year: 2011

The industrial demand for micro mechanical components has surged in the later years with the constant introduction of more integrated products. The micro bulk forming process holds a promising pledge of delivering high quality micro mechanical components at low cost and high production rates. This work describes a number of prototype system units, which collectively form a desktop sized micro forming production system. The system includes a billet preparation module, an integrated transfer system, a temperature controlled forming tool, including process simulation, and a dedicated micro forming press. The system is demonstrated on an advanced micro forming case where a dental component is formed in medical grade Titanium. © 2011 CIRP.


Takata N.,IPU | Morishita Y.,Japan National Institute of Advanced Industrial Science and Technology
Radiation Protection Dosimetry | Year: 2011

The signal current from a thimble ionisation chamber with a build-up cap made of an insulator decreases by about 0.41 % after being irradiated for 17 h at an air kerma rate of 41 Gy h. -1 by a collimated . 60Co gamma-ray beam in air. In contrast, the signal current remains constant when the thimble ionisation chamber is irradiated in a water phantom. During irradiation, positive charge is considered to accumulate near the outer surface of the build-up cap where electron equilibrium is not achieved. Secondary electrons travelling in the build-up cap and the chamber wall toward the ionisation volume are decelerated by the electric field generated by the positive charge. Consequently, the signal current decreases with increasing charge accumulation because some secondary electrons are prevented from entering the ionisation volume. In the water phantom, electron equilibrium is established in and around the ionisation chamber and charge does not accumulate. To confirm this hypothesis, the signal current was measured for an ionisation chamber in air with a build-up cap wrapped with Al foil and covered with PMMA tubes. Electron equilibrium was established over the build-up cap because the tubes were thicker than the secondary electron range. The signal current decreased with increasing positive voltage applied to the Al foil. It was estimated from the results that positive charges equivalent to a voltage of over 6 kV applied to the Al foil accumulated during irradiation. The signal current was also measured for an ionisation chamber with a metal build-up cap and for an ionisation chamber with a wall and build-up cap made of conductive plastic. © The Author 2011. Published by Oxford University Press. All rights reserved.


Olsen F.O.,IPU
30th International Congress on Applications of Lasers and Electro-Optics, ICALEO 2011 | Year: 2011

Now, nearly a decade since the introduction of the high brilliance solid state lasers, an evolution of industrial high power laser materials processing is ongoing. In this paper will be focused on the largest market for high power lasers: metal laser cutting. The state-of-the-art of laser cutting with fibre- and disc-lasers will be compared to the benchmarking laser for metal cutting: The CO2-laser. The opportunities and challenges for the new laser sources in conquering this market will be discussed. Both classical cutting with gas assistance as well asremote laser cutting will be discussed. State-of-the-art and new directions for the evolution of the laser cutting process will be presented.

Loading IPU collaborators
Loading IPU collaborators