A recent study addressed that VARTM is among the fastest growing technology in the composite industry.
The cloud-based solution will reduce the cost of entering VARTM technology by 40% to 60%. This will lead to wider adoption of the technology with significant advantages in terms of quality, repeatability, and reduction of environmental and safety impact for many SMEs around the EU.
In addition to this, affordable simulations will lead to fast prototyping, experimentation, and bolder design.

From a business point of view, this project allowed CATMARINE to reduce its Time To Market (TTM), reduce its process design cost and material waste. Looking in perspective, this technology will allow the shipyard to offer its customer innovative solutions, especially in the building of complex geometry and expansive material as carbon fiber and epoxy resin.

For SKA, VARTM simulation will be a new service to add to its catalog as for FEM analysis.

"In our field, it is no simple task to find a partner who is not only a top expert in a given field but also able to take initiative in finding innovative solutions. It is only in more challenging situations
when cooperation with real professionals is genuinely appreciated. CloudiFacturing project has certainly been the right choice", Ondřej Tůma, Managing Director, FERRAM STROJÍRNA s.r.o.

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

As a customer, easy access to equipment manufacturers repositories or to manufacturing services. As an equipment or service provider, easy access to the market.

Higher reliability and availability of stamping facilities by reducing downtime due to unforeseen breakdowns.

Reduced the analysis times of specialized personnel.

Reduce the costs of the Predictive Maintenance service for stamping companies, facilitating the access of SMEs to the advantages of failure prevention technologies

Improved ability to failure anticipation by being possible a much faster analysis,

Improve competitiveness of the stamping sector by reducing production costs due to unforeseen failures, as well as the Predictive Maintenance service provider

SWARM platform aims to enable building blocks to digitalize the life cycle, project management, collaborative conception and additive manufacturing prototyping. 

It creates a unique and consistent source of data across the entire product lifecycle form the idea to production, including customer feedback, training and after-sale service.

Better life cycle and project management for plastronic product and automatic generation of the control process on the basis of specifications and other project documents.

Higher product quality, higher production flexibility, optimized costs

The experiment provide a useful tool to help the enterprise to optimize the inventory and the use of resources, support components purchasing, support day to day production management on shop floor, support on-time delivery, improve quality.

Estimate the weather conditions at the city of Purmerend to help operators on the decision making

Increase AI based projects at ARMAC BV

Increase the heat delivery on set point 

Reduce heat loss

Increase of productivity and quality of work (increase competiveness)

Increase  in workers employability

Increase in job satisfaction and reduction of work-related stress

Reduction of maintenance costs and number of faults

Optimization of production quality (reduction of discards), costs (times, maintenance)

Optimization of safety and wellness of operators

Letting the designers and reconfiguration planners to use their time for the actual design and planning tasks, instead of cumbersome search and filtering of feasible resources and resource combinations from various catalogues with somewhat incomparable information. 

Reducing human errors in resource search and filtering (e.g. potential alternative resources overlooked by human, selecting resources with incompatible interfaces) 

Increasing the amount of alternative resource solutions considered, leading potentially to more efficient production system configurations. This may mean, e.g. faster throughput time, better product quality, reduced investment cost or some other improvement, depending on the target KPIs. 

Reducing the time used for system design and reconfiguration planning activity, and thus lowering the design costs.

Early estimating the potential faultiness could help local manufacturing companies on planning, controlling and executing productive activities in an optimized and predictive manner.

Production planning optimization

Reduced costs 

Optimized efficiency and effectiveness of the manufacturing systems

Tooling savings.

Operators savings. Less operators needed for machines management. 

Increase of the production due to increase of the machine availability.

Increase of quality of pieces and tools life time.

Increased visibility on the execution of a production schedule

Increased flexibility and efficiency of a production system

Increased robustness of operations of robotic mobile manipulators

Optimize the Handling

Reduce Technical Checks by Humans

Improve data quality

Optimization of maintenance intervals 

Reduction of regular service intervals such as (scanner cleaning, ..) by replacing a service call according to schedule with a service call according to actual need

Early detection of sensor faults (Lidar, IMU, Camera, ...) to ensure a proactive fault clearance of the system

Having insights on the accuracy of measurement in real-time is important not only for the end-user, as it can add a layer of subjectivity to the observations conclusions, but it also impacts the network management maintenance strategy.

The AI model reduces the time spent by the maintenance staff and optimize the routine of maintenance.

Optimize water consumption.

Generate Reports and Field Maps

Identify malfunction in the water consumption.

Prevent unintendedly water leakages. 

Identify and Predict water consumption

Measure water quality

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.

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.

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.

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.