A use case derived from Continental’s measurement lab has been used for validation, revealing the importance of task properties careful choice, time to familiarize employees to such system and assuring sensitive data security. 

The smart HMI was tested in E80, with expert and nonexpert operators working on an AGV in real working environment. The availability of such a tool to guide the use and maintenance of complex vehicles was strongly appreciated by workers, since it simplifies the interaction with the vehicle proprietary user interface and enable structured access to knowledge of specific procedures that have been carried out empirically. Moreover, expert operators are now able to take advantage of their experience and plan ad hoc maintenance plan, customized on the current status of the fleet. 

With the provision of the right tools, every person can become part of a manufacturing system, including people with disabilities. These people are eager to work and feel productive which can affect global industry very positively. 

The developed tool, has been applied in VOLVO, utilizing an offline production line, similar to the actual one. The perception time of the model has been reduced. Notably, more aspects of the model have been taken into consideration compared to the conventional simulation representation currently used. Finally, collaborative design of the simulation model has been rendered feasible, as a team of production managers can group and discuss on the same 3D model by making annotations. 

The assembly collaborative robot considers both the operation being performed and operator’s anthropometric characteristics for control program selection and part positioning. Besides, the workplace includes multimodal interactions with both the dual arm assembly and logistic robots as well as with the Manufacturing Execution System. Verbal interaction includes natural speaking (i.e. Spanish language) and voice-based feedback messages, while nonverbal interaction is based on gesture commands considering both left and right-handed workers and multichannel notifications (e.g. push notifications, emails, etc.). Furthermore, the maintenance technician is assisted by on event Intervention request alerts, maintenance decision support dashboard and AR/VR based step by step on the job guidance.

Trust is identified as a key indicator in the pilot. Trust experiments are critical when introducing automation mechanisms that co-operate with workers.

An AR based solution is proposed for instructions visualization enabling also on-job training activities and guidance. Regarding the ergonomics, an autonomous tool-trolley has been integrated including voice command and AR based gesture steering.

Adaptation within automation mechanisms is reported to be an enhancement at the workplace, according to workers.

The proposed solution comprises an adaptive smart tool and an AR instruction application using HoloLens wearable devices and a framework for ensuring digital continuity starting from the data recorded in the system for manufacturing engineering up to the execution and analysis phase

Workers’ opinion is key, especially for decision and acceptance, during the design and development of adaptive automation solutions.

Correct determination of best maintenance strategies and computation of components RUL requires the collection of a vast amount of data in a format that must be easily accessible and analyzed.
 

Robot components show a slow degradation of their performances: data collection must begin as soon as possible. 
 

To develop and apply statistical models for supporting PdM, it is always crucial to have as much as possible failure data, which is not easy to find in the companies’ databases. Furthermore, advancing and integrating different technologies in a single automatic and digitised smart PdM system is a challenge that requires close collaboration between research and industry players. 

GESTAMP, besides getting familiar with Z-BRE4K’s solution validation and assessment methodology, got a better understanding of internal reflection and readiness to apply predictive maintenance solutions to its plants while new mitigation actions related to process flaws and defects identification were developed during the Z-BRE4K. Also, they have understood the importance of solution validation and assessment methodology defined in Z-BRE4K. 
 

SACMI-CDS found out the importance of collaboration not only with a mechanical engineering/maintenance-related professionals but also with different technical background experts that together can improve multi-tasking and combining shopfloor and office-related activities as well as scheduling of activities during the work journey.

In general, after the solution implementation (TRL5), testing the system on the shop floor (TRL6) and validation of the Z-BRE4K solution (TRL7) at end users, the very final lesson learnt can be summarised as follows:

• Live data are gathered by sensors and other systems.
• Data from individual data systems incorporated in a distributed system.
• Quality and maintenance measurements are available.
• Manual maintenance schedules are replaced with PdM procedures and schedules.
• Maintenance experts supported by gathered data and predictions to improve their know-how in the maintenance domain.
• PdM accuracy and performance is established.
• Productivity improved.
• Cost reduction obtained.
• Possibility of Testing a full end-to-end solution for maintenance management including prescriptive and predictive analytics. 
   

PHILIPS supports the idea of predictive maintenance, “listening to the machines” and understands that the key to success is close contact between technology providers and experts where data integration/architecture and machine learning are both very important projects. 
 

Quantity and quality of data need to be ensured from the beginning of the process. It is important to gather more data than needed and to have a high-quality dataset. Machine learning requires large sets of data to yield accurate results. Data collection needs however to be designed before the real need emerges. Moreover, it is important having a common ground to share information and knowledge between data scientists and process experts since in many cases they still don’t talk the same language and it takes significant time and effort from mediators to help them communicate properly. 

 

Installation of sensor infrastructure: during the initial design to incorporate the new sensors into the existing infrastructure, it is necessary to take into consideration the extreme physical conditions present inside the milling station, which require special actions to avoid sensors being damaged or falling off. A flexible approach is adopted, which involves the combination of internal and external sensors to allow the sensor network prone to less failure. Quantity and quality of data: it is necessary to have a big amount of collected data for the training of algorithms. Moreover, the integration of real-time analytics and batch data analytics is expected to provide a better insight into the ways the milling and support rollers work and behave under various circumstances. 

 

Quantity and quality of data: the available data in the FFT use case mainly consists of legacy data from specific measurement campaigns. The campaigns were mainly targeted to obtain insights about the effect of operational loads on the health of the asset, which is therefore quite suitable to establish the range and type of physical parameters to be monitored by the UPTIME system. UPTIME_SENSE is capable of acquiring data of mobile assets in transit using different modes of transport. While this would have been achievable from a technical point of view, the possibility to perform field trials was limited by the operational requirements of the end-user. Therefore, only one field trial in one transport mode (road transport) was performed, which yielded insufficient data to develop useful state detection capability. Due to the limited availability of the jig, a laboratory demonstrator was designed to enable partially representative testing of UPTIME_SENSE under lab conditions, to allow improvement of data quantity and diversity and to establish a causal relationship between acquired data and observed failures to make maintenance recommendations. 

 

• The PROPHESY-SHARE platform has developed into a multi-purpose tool for both remote support as well as providing specific work instructions and visualization of (predicted) tooling information to the maintenance mechanic. As a result, both industrial partners are considering investments in this technology.
• The end-users are very eager to work with digital support systems that ease their job as it brings a single point of data.
• Quite some hurdles to take in the field of health and safety, IT security, GDPR (e.g. face blurring).
• The development cycle with prototype demonstration v1-feedback from the end-users and other stakeholders-demonstration of an improved prototype v2-feedback from the end-users and interdepartmental stakeholders worked very well. 

• It is challenging to run a development project in a real-life production environment due to high pressure on production output.
• Data collection from legacy systems brings some specific IT challenges.
• Data understanding and data-pre-processing are crucial before starting the data analysis. 
• It was proven once more that close collaboration is needed between data scientists and process experts to get reliable results.
• The results of PROPHESY-ML application to the use cases have been reported for both demonstrators in the project’s deliverables, achieving RUL prediction with accuracy as little as 4-5% Mean Absolute Percentage error. 

The Battery Pilot will aim at demonstrating that the DigiPrime platform can unlock a sustainable business case targeting the remanufacturing and re-use of second life Li-Ion battery cells with a cross-sectorial approach linking the e-mobility sector and the renewable energy sector, specifically focusing on solar and wind energy applications.

As the proactive exploitation of the DigiPrime platform enables the car-monitored SOH tracing and availability, less testing is needed to assess the residual capacity of the battery. Moreover, by knowing the structure of the battery packs, a decision support system can be implemented to adjust the de-and remanufacturing strategy accordingly and select the most proper cells for re-assembly second-life modules, thus unlocking a systematic circular value chain for Li-ion battery cells re-use. Furthermore, excessively degraded cells which cannot be re-used can be sent to high-value recycling, based on the knowledge of their material compositions.

Benefits:

• Interoperability with legacy data management solution (e.g. MindSphere, HANA, etc.)
• Open solution acting as horizontal stack that connects legacy systems.
• Speeding up the development of digitalized industrial processes both in pilot and factory scale.

Some tasks humans are good at and some they are not. Optimisation and scheduling tasks are examples of the latter. Even for the computers brute force optimisation or scheduling approach could be very difficult and require lots of computational recourses. On the other hand, there are different algorithms that can be employed depending on the problem at hand. Machining 4.0 platform demonstrator allows to experiment and demonstrate different approaches and well as scheduling problem complexity itself.

Proprietary Reconfigurable Flooring – A bespoke reconfigurable floor system is being installed that allows for fixtures and robots to be rapidly moved and securely and accurately fixed in place. A lack of a common or established standard in this area was noted.

Manufacturing systems are often characterised by ‘silos’ of data which cannot be accessed easily horizontally, and by varied and incompatible data types. By utilising a single data bus for all data to be transmitted on, standards are more easily implemented and all data is accessible by all equipment.
This is particularly important in this context where diverse sources of data (such as metrology systems, CAD data) must be analysed by software (e.g. data analytics, metrology software), and then used to adapt a process (e.g. robotic pathing, machining processes).

When a manufacturing system is fixed and will repeat the same tasks, having hard-coded and non-dynamic data exchange may be sufficient. When a system is reconfigurable and flexible, being able to define data sources and destination in software is critical (so-called software-defined networking).

SMEs often have an advantage over larger companies by being agile and able to change to meet demands more easily. However, this is only possible with an agile and flexible data system. For many SMEs, this burden is carried by human workers, with manual and often paper-based data management and exchange systems.

By implementing a common manufacturing service bus for data, this reliance on human data input (and the associated risk of error and time burden for skilled engineers) can be reduced, and data standards can be more easily implemented.

For flexible, reconfigurable systems where everything is connected together and must utilise a common data format, selecting the correct data format and a common structure for its use is key. B2MML worked very well for this application, but there is still scope for variation in the way terms and variables are defined, which must be settled on.

Converting an agreed process plan for manufacturing into the B2MML has some degree of automation, but also required a large amount of manual processing. More time should have been spent on automating this process.
Ideally, all components of the system would communicate directly with the service bus. Practically, not all devices will support the service bus, so use of an intermediary communication protocol such as OPC UA may be necessary. 

Although process control may all be centralised with a manufacturing service bus, safety systems may not be. This can cause unexpected system behaviour when the system starts a new process unless the safety system is fully understood by the users. 

Selection of flexible technologies and standards does not necessarily mean that any given implementation using those technologies will be flexible. A system implementation must be designed specifically to be flexible and future proof.

Integration of adaptive robot control technology into a complex and variable manufacturing process allows for accurate positioning of assembly components despite variability in component manufacture, existing assembly deviation, and the robots themselves.

This allows for progress towards jig-less assembly – saving non-recurring costs in the assembly of large, low batch products. Rather than building large, welded jigs and fixtures, robots are used to position and align parts. As the robots can easily be reused, this saves significant time and money.

Note: Since this demonstrator implementation, the Adaptive Robot Control and K-CMM technologies are now available from True Position Robotics .

Though the integration of adaptive robot control represents a significant upfront cost, the ability to save money on fixturing in the long term makes the creation of low batches of large, accurate products a more realistic proposition for small to medium enterprises.

As the robots can be re-used over and over for different situations, it enables significant flexibility in what products and product variants can be built.

The ARC robotic control system is extremely effective for bringing a robot / part to a specific and highly accurate location. However, it does not allow for accuracy along a path, so would not be appropriate for continuous path accuracy e.g. robotic milling or welding.

The large amount of metal in the cell (robots, parts) dramatically lowered the accuracy that was possible with the RFID positioning system. Rather than being able to track parts to a specific location, we could determine no better than if a part was inside or outside the cell. Active RFID tags may help mitigate this.

K-CMM technology was extremely effective, but subject to line-of-sight restrictions for large assemblies such as aerospace fuselages.

When integrating technologies and solutions from multiple equipment vendors, the challenge is almost always interoperability and standards compliance. The ARC system was comparatively simple to integrate and commission, but integration into the larger context of a manufacturing process with a SCADA and other physical devices was more of a challenge.

The realized progress advances the state of the art in several aspects:                                               

• Electric machine design process including electromagnetic finite element computation, winding design, and post-processing are available on a pay-per-use basis as a cloud solution. To the knowledge of the experiment partners, such a solution has never before been presented.
• Seamless, automated creation of production data avoids data handover errors and provides fast transition from simulation and design to manufacturing of motor prototypes. The full parametric manner of realization also provides a feature never available before.
• The capability to bring a customer requirement in the form of a motor specification to a fully functional motor prototype within one week is a revolutionary USP for Hanning. Despite the fact that this capability still needs to be demonstrated in real life, the steps taken into this direction have greatly improved Hanning’s process know-how and agility.

The establishment of the cloud cluster solution for running SyMSpace has greatly improved the engineering capacity of LCM which no longer includes the bottleneck of local computation resources. Being able to reserve as many cloud computing resources as necessary at any given time takes away the need to invest into hardware and maintenance just for covering peak loads. This means a great improvement of customer satisfaction.

Additionally, already five industrial partners are testing SyMSpace on a pay-per-use basis in the cloud. The main USPs are the low barrier for access (no upfront costs, no long-time installation, etc.) and the low total costs.

A certain interest has also been coming from academic players. As SyMSpace will see an open source release in the near future, this seems to be an interesting opportunity to run and eventually publish or contribute own algorithms from the academic research around electric machines. With the help of the cloud solution, the access barrier for remote partners has completely fallen away: beginning of September 2018, the solution was presented to a partner at the WEMPEC consortium at University of Madison, Wisconsin who is considering applying the SyMSpace cloud solution for research in magnetic bearings.

This example shows how the international contact, also across research areas has been facilitated using the cloud cluster solution of SyMSpace. This is an important driver for the innovation potential at LCM which, as a trans-academic-industrial player heavily relies on close contacts with other research institutions.

The demonstration of the faster and more reliable prototyping production process is expected to be a major improvement both internally and externally at Hanning. However, due to the delays and difficulties experienced in the implementation of the winding process, this step still needs to be realized. The potential, however, is impressive as demonstrated in the KPI metrics below.

Table 12: experiment 1 impact summary

The experiment setup was well prepared and discussed by the experiment partners LCM and Hanning. The implementation of the solution in the CloudiFacturing Marketplace, however, was unclear and needed significant efforts during the early stages of the experiment. This partly concerned the technical solution and partly was felt in the definition and in conveying the experiment structure to the consortium.

While this has added some unplanned efforts to the experiment which then reduced the time available for the practical implementations, the cause seems quite unavoidable for wave 1 of the experiments since the development of the Marketplace happened in parallel to the experiments. This situation will, therefore, automatically improve for the experiments in wave 2.