i4Q Newsletter September 2021 |
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We are really excited to share i4Q’s current progress with you, as the Consortium has been working on some exciting things! All user requirements are currently set by our end user partners and are being mapped in the selected reference architecture in order to start preparing for our technical developments starting from Oct. 2021. In parallel, the focus has been casted on other viewpoints as well, such as the “Business” , “Usage”, “Functional” and “Implementation” viewpoint, in order to be able to provide a spherical end result that could be used in the market. Stefanos Vrochidis (Coordinator, CERTH) Anastasios Karakostas (Deputy Coordinator, CERTH) |
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Having a central role in this project, the six pilots are briefly presented with their identity, their business and their challenges to take up. We invite you to visit their websites for more information. The i4Q community will provide updates on progress and solutions when available on our communications channels." |
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Fidia designs, manufactures, and sells Numerical Controls, High Speed Milling Systems and Flexible Manufacturing Systems for the automotive, aerospace and energy sectors. A specialised CNC (Computer Numerical Control) is expensive, but suitable for high precision machining: it allows a much higher control on the machine, a larger exploitation of its manufacturing capabilities and an uncanny access to the data. Vibrations (e.g. due to chatter) are well-known issues in the machining and metal cutting sector. They are responsible for poor surface quality in workpieces. The main problem of conventional solutions is the separation between quality control process and suppression of machine vibrations. |
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The use case will be implemented in FIDIA’s high speed milling machines production site in Forlì, Italy. The proposed i4Q Solutions will combine advanced vibration monitoring methods, with AI-driven prediction of Quality indicators. The solutions will increase the quality, productivity, and efficiency of the process. The number of rejected parts, time devoted to reworking and manual finishing and costs will be reduced considerably. |
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Since 1969 BIESSE has been designing, producing and marketing a complete range of technologies and solutions for the carpenter and the large furniture, window and wooden building components industry. BIESSE is now present in plastic processing machines with solutions designed specifically for a growing market. Biesse already shows data for statistics, diagnostics and preventive maintenance, using OPC-UA as standard protocol. However, an enhancement of this protocol is needed to feed predictive analysis algorithms with new deep data. BIESSE aims at increasing data quality by using additive and virtual sensors and adopting an edge architecture to increase computational processing capacity. |
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The project’s scope is to improve data quality, consistency and integrity in its CNC (computer numerical control) machines to allow the use of available information and add-on sensors in diagnostic analytic tools. The proposed solutions will be developed for the top CNC woodworking solution by BIESSE. I4Q outputs will be exploited to continuously monitor working conditions and process parameters. The objective is to minimize the rejects due to incorrect planning or configuration and setting of the processes. |
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Whirlpool Corporation is the biggest white goods business player in the world, with 60M products sold in 170 countries in 2018. Whirlpool EMEA is a relevant part of this business distributing around 20 Million appliances in a challenging business. The product conformity test is currently based on a statistical verification in a laboratory and is applied to pre-series or new variant products. The products are selected randomly and moved to dedicated labs which test the marketing key features. |
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The use case will be conducted in the Dishwasher factory of Radomsko, Poland. i4Q tools will use a rich and deep dataset and specific business requirements to infer conformities and non-conformities in real time without an expensive and slow massive execution of tests. Additionally, this continuous process verification can be used to detect drifts in the production’s quality. Finally, the manufacturing line reconfiguration guidelines and the prescriptive analysis tools will allow adapting the manufacturing line to correct quality drifts before it compromises the conformity of several products. |
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FACTOR is specialised in metal machining and precision turning, offering a comprehensive solution for the outsourcing of metal mechanical components to customers of the most demanding and leading industrial sectors (Aeronautics, Agriculture, Automotive, Electronics, Elevation and Handling, Energy, Hydraulics, Medical, Naval). Currently, FACTOR wants to improve its manufacturing line qualification system based on a continuous process validation to certificate its manufacturing quality level, guaranteeing the inalterability of product and process data to its customers. The objective is to meet this requirement of some aerospace and automotive customers and improve the Overall Equipment Effectiveness (OEE). |
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The data collected by the i4Q systems will allow to build a knowledge base to estimate the optimal working ranges of the machines and tools, anticipate maintenance, increasing the efficiency of the entire production system and a reduction of incidents. Finally, FACTOR will use the i4Q Data Integration and Transformation Services for assuring its manufacturing data quality and to ensure the reliability of all the raw industrial data to its customers, especially of aerospace and automotive sectors. |
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Part of the “Visabeira Group” conglomerate, Ria Stone is a greenfield factory created in 2014, after being awarded a contract by IKEA Sweden for the manufacturing of 486 million tableware products in the timeframe 2014-2026. Quality control (QC) validation techniques being applied today to incoming raw matters from 3rd party suppliers, namely Ceramic pastes are performed only through lot sampling, by using the traditional methods of collecting two ceramic paste samples and performing two separate QC operations: - A regular chemometric laboratory composition analysis,
- A physical QC test performed in an isostatic press by using the sampled paste to produce a limited quantity of test greenware plate.
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Both QC operations are performed off-line, the immediate in-line and in-realtime and accurate confirmation that incoming ceramic pastes comply with the exact formulas for trouble free production is not possible. The future objective is to improve the quality control and detect the defective products directly on the line in-realtime. The i4Q use case will be implemented at the Ria Stone raw matter Storage and dispensing Silo Units in one of the production lines for stoneware ceramics. Ria Stone has the ambition of improving the Production Efficiency of its Production Processes, thanks to new and advanced processes that can measure the quality of the incoming Raw Matters inline. The objective is to go from the current status of statistical offline sampling quality control methods to a continuous and complete data driven incoming raw matter quality control. |
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Farplas, flagship of “Fark Holding” established in 1968 as an automotive supplier, designs, develops and manufactures vehicle plastic systems, such as interior/exterior parts, instrument panels and electronic based ceiling and lighting systems. Farplas has consolidated as a full system automotive Tier 1 company. The 0% defects are a basic requirement for the original equipment manufacturer (OEM) to introduce these plastic parts in the final assembling of the car structure in their facilities. If there is any defective part detected by the OEM, all the production sent by the Tier 1 provider is rejected until the problem is solved. This causes delays on the whole process assembly and influences other parallel process of the car assembly. |
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The use case will be developed in one of the plastic injection machines that Farplas has in its factory in Kocaeli, Turkey. Each machine produces approximately 10 different pieces. Currently, detection of defective pieces is performed by visual control equipment. The objective of the pilot is to incorporate automatic industrial inspection and AI-based detection algorithms, reduce workforce in the process, and move the visual defect control to the conveyor belt. This use case complements the plastic injection manufacturing process with an automatic advanced inspection phase based on artificial intelligence. The i4Q Solutions developed in the project will collect data from all phases, perform the corresponding data analytics, and actuate over the different devices to optimize several processes. |
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Find more information about the pilots on the i4Q Community website. |
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The i4Q Community is the community for the Industrial Data Services for Quality Control in Smart Manufacturing (i4Q) program that will help micro, small, and medium European manufacturing enterprises overcome the hurdles preventing them from entering the Fourth Industrial Revolution. Join the i4Q ZeroDefect Manufacturing Community and enter the Fourth Industrial Revolution | | |
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 958205 |
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