During the early phase of an aircraft program, Industrial Architects are evaluating different industrial scenarios and trade-offs. Interesting findings, include:

(1) Manufacturing Processes are not enough agile and not enough flexible in order to answer to the complex and competitive environment and to the incessant changesneeded concerning both the performance (need to improve the ramp-up) and the product (change of MSN) on the assembly lines;

(2) Engineering, Manufacturing & support processes are still considered in a sequential approach. For solving part of the problems, Product Line and Co-design concepts needs to be tackled. A Model-based systems engineering (MBSE) approach where the Industrial System is seen as a System like any other (in parallel of the Aircraft) will allow to structure and optimise the development of Industrial System and to perform trade-offs efficiently.

Model based System Engineering, Trade Space exploration and Multi Skill optimization are common capabilities in Engineering Systems; often in the context of “Design to Aircraft Performance”. The Engineering process and the Digital solutions are not enough supporting Design to Manufacturing Value.

This leads to late design loops, rework and the end extra costs for Airbus and for the customers.

The use case involves multiple engineering options and sites and at least one Airbus supplier; keeping in mind the need to deploy the solution at a large scale and at a low cost for Airbus and the suppliers.

The pilot will demonstrate applicability of the approach to design an industrial system at high level (Supplier network, Factories, Machines and processes) for near Zero-Defect Manufacturing. By taking the Industrial System as a whole, Autonomous Quality control loops will be integrated at the pertinent points. Also, the easy reconfiguration of manufacturing process will be compliant with AQ concepts. AQ for ZDM will also ensure to keep the right quality level as a target. Case focus aero structure design –with composite or metallic machining and assembly process using robots or humans.

The production of automotive electronics for future mobility is based on automated lines that secure highest quality and output. For the production and assembly of respective PCBs an effective ZDM approach and availability is paramount. Key features, such as “product/component quality”, “cost control” and “time-to-market” for multi-millions of products outline the situation in the automotive industry at the brink of a new economic scenario and govern the global competitiveness.

As to Equipment Data Collection and Communication, it is obvious that:

(i) Sensing and combining data from a variety of sources is essential to enable a progressive approach. Setting a standard for data formats in shop floor communication, such as “Bluetooth 4.0 for Autonomous Quality” is crucial, aiming at achieving an independent format for machine communication;
(ii) Autonomous quality control loops, as well as recognition a management of unstructured data for several production lines all over Europe requires establishing an “EU-Cloud “for safe, secure and trustworthy data processing;
(iii) The adoption of Big Data technology in supervisory and strategic decision-making actions on production lines and the full supply grid are the gateway to immediate reaction to problems.

The pilot supports the realization of a ZDM Scenario for multi-stage production lines through introduction of deep analysis and decision-making control loops, for capturing, communicating, secure storing and visualizing real-time holistic data on products, material, equipment, environment, human actions; in relation to quality control and processes. Essential KPIs, such as FPY, OEE, MTBF/MTTR, PPM and Control loop time throughout the supply network will be fully taken into account.
 

Expected Outcomes:

• A common and holistic semantic model able to represent concepts at different stages of product lifecycle;
• Standard methods and tool to gather, store and share data;
• Trustworthy digital continuity and data management;
• Flexible and user-friendly analytical tools - Human-centric big data visualization;
• Comprehensive way to share results among different management roles;
• Equipment and process simulation and optimization solution as major leverage towards the efficient realization of complex first-time-right parts;
• Implementation of a closed loop over the manufacturing process between digital and real domains.

Danobat, is a benchmark manufacturer of high value-added solutions in the field of grinding. Danobat develops, in close collaboration with its customers, turnkey solutions for specific applications in the following sectors: aerospace, railway, automotive, energy, oil and gas, and metal forming. Machine condition monitoring and data analytics are the foundation of Danobat and Ideko ́s approach for ZDM at machining level.

Danobat desings, develops and provides grinding and turning machines, as well as turn key complete manufacturing lines for high added value parts in the above mentioned sectors. The new digital factory environment has driven new developments and technological offer in the areas of Smart HMI, Danobat Data System and the Control System for integral automation of manufacturing lines. 

The pilot of Danobat ZDM grinding machine will use grinding machine operational data to relate machine-use with evolution of machine components condition. The analytics and the applied control loops on ZDM grinding machine will be based on combination continuous data and test conditions.

FAGOR has a long experience in delivering manufacturing lines (machine-tools & the whole automation solution) as well as providing the building blocks of such lines (press machines). Press machine is the product par excellence of FAGOR. A press machine is composed by 2 rigid platforms (head and base), a bed, a ram, a mechanism and other surrounding components that guarantee the full automation and process control (temperature, pressure, etc.).

The pilot proposed in QU4LITY project is framed within the process of continuous improvement of FAGOR R&D strategy. The development of this project will allow FAGOR to offer to its customers a more accuracy, competitive and productive press machines that can take part in a production line. All the improvements performed at press machine level will directly improve the production line where it is integrated with. The development of this project will facilitate the increase of the level of sales. The technological development together with the quality, constitute the great bet of FAGOR to achieve its objective of customer satisfaction.

The objective of this project is to reach zero defects manufacturing process collecting press machine critical parameters and identifying exactly the process developed in the manufacturing of pieces. Traditional zero defects approaches propose the analysis of such parameters isolated from the rest of the process where the machine is integrated. Whereas these approaches try to maximize the efficiency of the process by maximizing the efficiency of the parts, it fails to maximize the efficiency of the overall system. Such process has a great complexity from the point of view of the acquisition, measurement and transmission of the parameters and variables. In addition to that, the integration of the data from other parts of the system at machine level should be valuable.

Current barriers to high accuracy in manufacturing in multi-technology and automated cells are related to limitations in data aggregation to either machining processes or machine health scopes. Zero defect manufacturing in these systems will be therefore possible by taking into account in the planning stage how the machine mechanics evolve towards states where deviations are more likely to occur, where failures might damage the part or the machine, or where uncertainties are introduced by maintenance, repairing or any other uncontrolled factor in the chain.

The pilot will address the challenge by setting up a first digital system for detecting, diagnosing, and fully compensating deviations on accuracy, productivity and sustainability of a machining cell based on the aggregation of information from milling and EDM machinery health, process performance and geometrical part characterisation, using AQ control loops and a common data space for making possible a realistic information integration from different types of hardware & software coexisting at different end-users factories, and targeting fully automated, zero defect manufacturing.

Since 1937, GHI been designing and manufacturing industrial furnaces for melting, heat treating and heating any type of metal. GHI Furnace uses cutting-edge technology, which enables us to identify anomalies, provide support for in-use improvement, and plan predictive maintenance activities. The aim is to minimise unexpected downtime and to maximize the potential of our installations.

GHI provides to worldwide customers (1) Turnkey plants. GHI has developed an extensive range of turnkey plants in response to the growing demand for integral solutions. Industrial furnaces (2) Industrial furnaces for melting, heat treating and heating any type of metal. (3) Auxiliary equipment. GHI supply is rounded off by a wide range of auxiliary equipment for providing an integral solution to customer requirements. (4) Smart SAT 4.0. Through our after-sales service, we maintain a relationship with customers throughout the service life of equipment. GHI is a global leader and has engineered and delivered the largest rotary furnace in the world.

GHI has delivered 8000 installation’s products and services to 1250 companies, in 48 different countries, and sectors around the world, among them aluminium production, automotive, aeronautical, railway, steelworks, renewable energies, zinc, etc. Customers include ALCOA, BEFESA, ThyssenKrupp, CIE Automotive, FAGOR, NEMAK, ARCELOR MITTAL, CAF, GM, GESTAMP, DAIMLER, VW, RENAULT, SCHAEFFLER, AIRBUS, EADS, SKF among many others.
One of the challenges of the automotive industry in recent years is to meet the demand to reduce fuel consumption, assuming a reduction in CO2 emissions into the atmosphere. On the other hand, automotive sector has always been looking for increase the safety of the occupants of the vehicle in the event of a collision.

In order to obtain this double objective, a series of steps have been taken aimed at optimizing the structure of the vehicle, using other materials than conventional low-carbon steel for the manufacture of the vehicle structure or implementing advanced manufacturing techniques such as the technique of hot stamping. By means of this technique, parts with the required safety characteristics are obtained, even owning a weight reduction with respect to traditional methods of cold stamping.
Hot stamping is a current process for the manufacture of structural parts in the automotive sector. Hot stamping is a process by which a sheet is subjected to a load between two dies, while the entrance temperature of the sheet is bigger than the austenitization temperature of about 900-950 °C. This process takes advantage of the high ductility of the piece due to its high initial temperature and then proceeds to a rapid cooling to achieve the martensitic hardening of the piece.

Kolektor (KOL) is known as the world's largest manufacturer of commutators, the second largest European manufacturer of slip rings and the second largest European manufacturer of plastic-bonded magnetic products. Injection moulding is enabling technology for production of all these parts. The scope of this pilot project will be a production line where Kolektor produces one type of products. The aim of this pilot is to detect, possibly predict, and remove the cause of the process failure as soon as possible, ideally in real-time.

In the process of real-time process monitoring, we are planning to:
(i) Collect moulding process parameters;
(ii) Monitor environmental parameters;
(iii) Inspect moulding tool and moulded parts;
(iv) Introduce AQ control loops at the operational level.

Based on the collected data and by applying control loops, advanced analytics and artificial intelligence methods we will better understand the moulding process and will be able to detect anomalies and failures as soon as possible. Because of the geometry of a moulding tool and number of cavities, it is not possible to inspect all cavities at once. Therefore, we are planning to use robots to perform complex moves required for inspection. We would like to study if it is possible to automate the removal of root cause of the bad parts being produced (like cleaning the cavity with dry ice).

The goal of MONDRAGON Corporation roadmap for the coming years, as a global business group, is to change the structure of the businesses, leading their evolutions towards higher added value and developing new activities in leading sectors. Machinery Building for Capital Goods is a main industrial division within the Corporation providing high quality –high performance solutions based on smart technology in a wide variety of sectors (automotive, aerospace, energy, railway, oil &gas, capital goods, white goods, etc.). Danobat Group, Fagor Arrasate, and other leading corporate brands represent a benchmark in machines, solutions and advanced services in the area of Machine Tools.

Given the strategic importance of the Machine Tools industry, and in the context of QU4LITY, MONDRAGON proposes two process pilots in the Machinery Building for Capital Goods scenario; two realities that can be complementary in many customers’ value chains:

Use Case MONDR1:
Multi stage zero defect manufacturing rayway axes production line: Manufacturing Processes with Cutting/Grinding Machinery, leaded by DANOBAT (DAN). The objective of MONDR1 pilot is to reach zero defects in the production line of axles that includes several stages: machining (gridnging and turning), finishing stages, as well as in-process and final inspection and verification operations. During this multi-stage process, several deviations or process variability may cause geometry and quality defects that cause both extensive rework operations and part scrap.

The technologies and the control loops provided by QU4LITY will make it possible to achieve observability of the product, process and resource states, throughout the system stages.

Use Case MONDR2:
Zero-Defects Manufacturing digital Hot Stamping process: Manufacturing Processes with Hot Stamping Machinery, leaded by FAGOR. The objective of this MONDR2 pilot is to reach zero defects in the hot stamping cooling temperatures, transfer speed, loss of temperature in the transfer or settings of press and identifying exactly the process developed in the manufacturing of the parts.

PHILIPS has recently developed a new male grooming device called OneBlade. The OneBlade is undergoing a phased worldwide introduction, started in 2016. PHILIPS is currently developing new production lines with increased capacity to meet the increased market demand. To remain competitive, there is a clear need to improve PHILIPS productivity by improving three main metrics: Time to market, Production costs and Product/component quality. An increase of component quality will have a positive effect on production costs and time to market.

The production line in scope at Philips is producing the cutting element for the OneBlade. Processes used for this are:

• Over moulding
• μAssembly of parts
• μWelding/joining of parts
• Pad Printing
• In-line testing

The main goal of QU4LITY for PHILIPS is to realize a holistic system that:

• can raise early warning signals based on early indicators and trends from process signals and dimensional CTQs that are still acceptable on component level but will lead to Fall of Rate on the finished good in the current quality framework
• can suggest feed-forward or feed-backward controls to neighbouring process stations, which might have an influence on the dimensional CTQ that is under observation
• increases OEE A and P by helping the operator to take correct process adjustment decisions,
• reduces Fall Off Rate (FOR) by learning from unknown data interactions

With these elements the next step in autonomous quality/production can be made: from 'descriptive' to 'prescriptive' quality assurance

Prima Industrie is a world leader manufacturer of laser machines for metal components. Prima has recently started with a new activity in Additive Manufacturing systems, investing in both technologies of metal powder bed and direct energy deposition. The scope of this use case is to enhance process monitoring and control for producing metal components and make Additive process more productive and robust.

The next generation of manufacturing systems will be based on advanced processes, where additive manufacturing (AM) leads the technology trend. If AM has done a first quantum leap shifting from prototyping to production, the second relevant leap is now directed to make series production a reality. Next Generation of AM systems go towards a cost-effective solution by embracing a more robust process and higher process quality. To reach this aim, it is important to improve monitoring and control aspects towards process reliability and ZDM in terms of part dimension, surface roughness, part quality. Improving the real time monitoring and closed loop control can lead to reduce development time, enhance part quality and reduce waste and cost of production.

In this pilot, additive manufacturing machines for powder bed and direct deposition will be considered to enhance process control for producing metal components. Traditional solutions and new concepts of machines will be considered to test new edge devices for process control, towards a ZDM result, and to work on data management and analytics to implement the whole manufacturing process by a platform and control loop approach. In laser-based additive manufacturing, production time has a great influence on the economic efficiency of the production process. To increase the productivity but also reliability of such processes, a zero-defect AM strategy is targeted. Starting from modular devices for real time detection of the process, it will be possible to collect data, deploy new parameters to adapt the machine control to the actual task and communicate data at management level, where not only the single machine is considered; approach will consider both new systems and new concepts of machines in parallel with AQ control loops.

RiaStone (RST) is part of “Visabeira Industria” a sub-holding of the “Visabeira Group” conglomerate. RST manufactures the IKEA worldwide supply of “Dinera”, “Fargrik” and “Flitighet” tableware families, being these products fabricated through an innovative Industrial ceramics production process: tableware automated single firing.

Riastone is working towards improving its Overall Production Effectiveness (OPE) KPI; from ~92%, to 99%. This requires new approaches to production, promoting better and innovative defect management and production control methods, which are consistent with the implementation of ZDM processes, namely in-line inspection technologies, and integration of tools for autonomous, automatic, smart system decision making.

In order to achieve the required improvement goals, RST is applying several systems-level strategies consisting in the integration of new inline inspections systems and AQL control loops into the current production line.

In parallel new in-line automation components will be implemented that will automatically remove detected defective parts from the production lines; enabling the introduction of recycling and re-using of raw materials into the production line for a new defect-free production cycle.

Additionally, RiaStone will expand and leverage the outcomes of a H2020 project (BOOST4.0) which will integrate the BOOST4.0 Big Data processing platforms and the newly implemented Qu4lity ZDM Autonomous Quality Control Loops.

In the process of using the Quality AQL real-time inspection of intermediate tableware products RiaStone will:
 

1. Perform Artificial Vision “Submillimeter inspection” of Tableware after the ceramics pressing and fettling stage
2. Perform Artificial Vision “Surface inspection” of Tableware after the glazing application stage
3. Use Edge computing technologies to process computer vision imaging collected from the inspected tableware
4. Through ML computing, compare processed imaging originated in the AQL Loops against an in-memory defects database consisting of 10000+ annotated defects pictures
5. Issue a decision of conformity or non-conformity after Artificial Vision analyses computing.
6.   Instruct control gate systems for each piece of tableware regarding the Go-NoGo decision at tableware piece level.

With these base systems in mind RiaStone implemented the Qu4lity ZDM -AQL loops in the following Business Processes:

Business process RiaStone_1- Isostatic Process AQL:
In Business Process 1 Iso-Static pressing is used to evenly form the main ceramic structure, due to the necessary delicate balance of several physical variables (density/Temperature/Pressure) in this stage, quality deviations can occur that cause geometry and quality defects.

Business process RiaStone 2 - Glazing Process AQL:
In Business Process 2 glazing is applied to the already pressed Tableware, due to the exact Glaze Viscosity needed in this stage, glaze viscosity variations, will originate excessive, or deficient glaze coverings, which will cause quality problems such as the formation of glaze drips and glaze bead formations,

At Amberg, Siemens’ Digital Factory Division is manufacturing its SIMATIC products. Core of the manufacturing process is the production of the circuit boards, which are later on assembled with the housing parts to form the final product.

The overall objective is the introduction of AQ control loops for testing improvement and thus for overall production efficiency, while at the same time further raising the quality rate of finished products. This shall be achieved by implementing a closed control loop approach via the deep analysis of process data and the implementation of digital twins for products and production. Data from control loops throughout the manufacturing lines are to be collected and analysed via data mining and machine learning techniques, allowing to systematically identifying faulty products and the respective failure causes, providing the base for the intended improvements. By means of quality forecasting and simulation, (production) control decisions shall be derived at an early stage if quality deviations occur, thus avoiding further added value in ok products.

Current state: In the considered production line, electronic components are manufactured automatically and tested for functional defects using multiple test stations, including optical inspections, inner circuit tests and X-ray machine. Significant process steps are the solder paste printing of the circuit board, the placement of the electronic elements and the (reflow) heating process (compare Figure 11) before assembly. For ensuring high product quality, several quality tests are integrated into the manufacturing line. Today, each of these quality tests is conducted in order to test the result of the previous manufacturing steps. From the overall setup of the manufacturing environment, ICT and X-Ray testing are placed in a testing area, decoupled from the actual manufacturing lines. The X-ray test has a comparatively high process time, is carried out as a batch process and represents the line bottleneck, since currently a 100%-testing is conducted. Since the purchasing of further X-ray machines is associated with high costs, other solutions and approaches have to be generated.

Future state: Current activities performed in the factory in Amberg are a collection of product and test data, storage and management of these data for quality scouting, with web service and graphical report extraction. Specifically, for this trial, the focus is on one specific manufacturing line including solder printing, placement of electronic components and reflow soldering. Within this manufacturing line several test stations

The current context of the automotive sector leads to continuous improvement. For all customers, the quality of THSYS products passes by an acoustic comfort. Therefore, a large part of THYS production requires acoustic control.

Today, to make an acoustic control on our steering gears, a workstation uses accelerometers to measure the noise caused by the mechanical linkages and is able to determine if the product respect the acoustic limits. In case of defect, there is a need to identify which component from the assembly is responsible of the acoustic defect, so, that after a specific treatment and analysis of the signal, component as the root cause can be isolated.

Whirlpool is opening a green field plant in Lodz/Poland. The white good that will be produced there is Dryer. Digitalizing the Factory, we want to reach a holistic approach to ZDM considering the full product lifecycle: from Product Design to Customer Service, cycling back to Product Design.

The pilot will leverage the outcomes of a previous research project (NMBP FP7 GRACE) and will integrate the QU4LITYdigital enablers and platforms (through the APIs) and the AQ control loops. The main innovation will be represented by the introduction in production of MPFQ model fused with AQ control loops: Functional Integration and Correlation between Material, Quality, Process and Appliance Functions.

The MPFQ-model is an acronym combined by the first characters of the four main elements:

• Material - as a collective term for everything that is needed to produce a certain product or product component. This may include raw materials, pre-products, consumables, operating supplies, product components and assemblies.
• Process - meaning production processes processing and transforming materials into the final goods by using machines, tools and human labour. This process is defined within the plant engineering.
• Function - meaning product feature and functions as distinguishing characteristics of a product item. This is mostly focused on functionalities as specific task, action or process the product is able to perform, but may also include other features like performance etc.
• Quality - measured as the degree of conformance of final product functions and features to customer requirements.

Every material, production process and product function/feature can be described by its technical characteristics and a collection of measured data. The processes can be additionally described as a sequence of process steps and their parameter setups. Product functions and features additionally contain performance indicators. These performance indicators may be evaluated by analysing and computing measured data from the materials forming this function, the process realizing the function or even by the directly measured data from the function measuring.

This innovative way to control quality and model data inherent to quality will be the fundamental approach that will lead to the vision of holistic Quality system.

In addition, it will be deployed AQ reference implementations to address unresolved problems in the vertical integration of data management (from data gathering to visualization and decision-making), enabling a holistic vision to be achieved.

The production process to build a Clothes Dryer comprises many stages; combination of automatic equipment and manual operation and all along the production process several Quality Stations are installed to perform gauge, to detect defective parts, filter them out or repair them. The main stages of the production process (Drum Line, Heat Pump, Side Fabrication, Main Assembly, Functional Test, ZHQ Area and Reliability Test) will be equipped with a Quality Gate, i.e. station to perform gauges and pass/fail test on product as well as Process monitoring means (OEE, SPC, Andon).

All these data sources will be integrated in the experiment, providing a comprehensive view of the production process.

Report reflecting the outcomes of T2.1 (Final version)

This document gives overview of all Pilots regarding specifications and preparations. The pilots have got a central location in the Qu4lity project. It is area where requirements, architecture, knowledge regarding ZDM have to show their real potential. By implementing the needed items to fulfil the needs within the Pilots.

The QU4LITY project demonstrates its data-driven ZDM product and related services in a combination of five strategic ZDM plug & control lighthouse equipment pilots

• Pilot #9: GHI Real-time cognitive hot stamping furnace 4.0
• Pilot #11: PRIMA Additive Manufacturing Pilot Adaptive Control Technology
• Pilot #12: Danobat Digital Machine for zero-defects at high precision cutting/grinding
• Pilot #13: FAGOR Zero-Defects Manufacturing Digital Press Machine
• Pilot #14: GF Digital machine and part twins for zero defect manufacturing

as well as nine production lighthouse facility pilots.

• Pilot #1: PHILIPS OneBlade shaving unit production line
• Pilot #2: SIEMENS SIMATIC Products Quality Improvements
• Pilot #3: CONTI Autonomous Quality in PCB Production for Future Mobility
• Pilot #4: WHR Dryer Factory Holistic Quality Platform
• Pilot #5: MONDRAGON Zero defect and Autonomous Quality in Machinery Building for Capital Goods sector
• Pilot #6: KOL’s Real-time injection moulding process monitoring-control
• Pilot #7: THYS Quality Management of Steering Gear based on Acoustic control
• Pilot #8: AIRBUS Trade space framework for Autonomous Quality Manufacturing Systems’ Design
• Pilot #10: RiaStone Autonomous Quality ZDM for “Ceramic tableware Single-firing
   

Besides global planning, agreed way of working, for each pilot relevant information is summarized in this document:

• General description of each pilot
• Involved partners per pilot
• Current state
• Future state
• Detailed plan
• Expected results
• Preparatory results

An technoeconomic evaluation report based on T77

An evaluation and feedback analysis report based on T7.6

Presentation recorded at ConnectedFactories Workshop 24 March 2021

This deliverable will comprise the pilot system deployment and operation for each one of the ZDM machining pilots, along with an accompanying report (based on T7.2)

This deliverable will comprise the pilot system deployment and operation for each one of the ZDM processes pilots, along with an accompanying report (based on T7.3)

Prototype implementation of the various fog nodes and edge gateways of task T3.4

The current deliverable (i.e., D2.12, “Reference Architecture and Blueprints (Final Version)”) aims to report on the final results of tasks T2.6 (“Reference Architecture, Open APIs and Blueprints for Autonomous Quality Solutions”). However, relevant information to the ongoing work in other technical work-packages (i.e., WP3, WP4, and WP5), it has been decided to also include a high-level specification of the QU4LITY framework in this version of the deliverable, foreseeing further details in the outcomes of the other WPs.
Task T2.6 follows an incremental-iterative approach, thus initial results were included in “D2.11 - Reference Architecture and Blueprints (Version 1)”and the final results are being reported in this D2.12.

Prototype implementation of the various fog nodes and edge gateways of task T3.4

The current deliverable (i.e., D2.12, “Reference Architecture and Blueprints (Final Version)”) aims to report on the final results of tasks T2.6 (“Reference Architecture, Open APIs and Blueprints for Autonomous Quality Solutions”). However, relevant information to the ongoing work in other technical work-packages (i.e., WP3, WP4, and WP5), it has been decided to also include a high-level specification of the QU4LITY framework in this version of the deliverable, foreseeing further details in the outcomes of the other WPs.
Task T2.6 follows an incremental-iterative approach, thus initial results were included in “D2.11 - Reference Architecture and Blueprints (Version 1)”and the final results are being reported in this D2.12.