Enhance production and control strategy with AI,

Improve  Worker’s safety using AI to identify unsafe working conditions

Improve predictive maintenance accuracy

Enable remote and highly interactive control strategy for working environment

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

Provide methods for the continual learning of AI model

Provide a specific user solution designed to help decision making of operators

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

Provide automatically feasible resource suggestions to the system designer

Reduce time used for system design and reconfiguration planning

Allow more alternative resources to be considered during design and planning, leading to potentially innovative solutions  

Reduce humane errors in search and filtering

Automating the search and filtering of feasible resources and resource combinations for specific product requirements from large search spaces.

Automating the identification of required reconfiguration actions on current layout.

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.

Score the quality of measurement

Detect failures and their root causes

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.

An adaptive support system for training and guidance in assembly making use of an AI based adaptive algorithm  based on both productivity and quality as well as operator capacity and needs 

Increase of productivity and quality of work (increase competiveness)

Increase  in workers employability

Increase in job satisfaction and reduction of work-related stress

The troubleshooting system implemented in the Experiment is an AI-driven advanced solution that improved an already existing system, totally deterministic. The new solutions instead is based on a probabilistic approach. Compared to the previous one, it is:

• more robust: each answer modifies the probability associated with each component without deleting or discarding any of them
• more flexible: the system also includes the ability to skip a specific question
• more efficient: the tool can determine the best question based on the probability associated with each component. 

Shorten the engineering cycle => automatic feedback and decision making

Save time and reduce cost => reduce manual operations, automation process

Automatic recommendation for printing configuration and additive manufacturing feasibility => Covering all the lifecycle (ideation, request, design, simulation …)

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.

Increased Overall Equipment Effectiveness

Reduced Carrying Cost of Inventory

Increased On Time Orders

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.

improved accuracy of robot arm during milling operations

Higher product quality, higher production flexibility, optimized costs

Improved Reliability of Maintenance and Service

Reduced Working Capital

Improved Customers Acceptance

Decreased Number of training data to adapt the model to new applications

Decreased Training time

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

Deployment of an IIoT platform serving IILAB demonstrators

Increased real time visibility on a production system

Increased flexibility and efficiency of a production system

Increased robustness of operations of robotic mobile manipulators

Real-time Production Data Monitoring

Industrial Autonomous Systems Reliability

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

Online prediction of the maximum number of pieces to be produced by a specific machine according to the current status of the tooling and the readings of its control parameters.

Optimization of machine control parameters configuration to maximize the tooling life while the quality of the production is maintained.

Maximization of the tooling usage time of every machine, while maintaining the machining quality.

Optimization of machine control parameters configuration to maximize the tooling life while the quality of the production is maintained

Coordination of the tooling change in all the machines. This objective would  face the tooling change considering the impact of machines among themselves, instead of just one by one.

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.

The SERENA project has provided a deep dive into an almost complete IIoT platform, leveraging all the knowledge from all the partners involved, merging in the platform many competencies and technological solutions. The two main aspects of the project for COMAU are the container-based architecture and the analytics pipeline. Indeed, those are the two components that more have been leveraged internally, and which have inspired COMAU effort in developing the new versions of its IIoT offering portfolio. The predictive maintenance solutions, developed under the scope of the project, have confirmed the potential of that kind of solutions, confirming the expectations. 

On the contrary, another takeaway was the central need for a huge amount of data, possibly labelled, containing at least the main behaviour of the machine. That limits the generic sense that predictive maintenance is made by general rules which can easily be applied to any kind of machine with impressive results. 

More concretely, predictive maintenance and, in general, analytics pipelines have the potential to be disruptive in the industrial scenario but, on the other hand, it seems to be a process that will take some time before being widely used, made not by few all-embracing algorithms, but instead by many vertical ones and applicable to a restricted set of machines or use cases, thus the most relevant.  

The SERENA system is very versatile accommodating many different use cases. Prognostics need a deep analysis to model the underlying degradation mechanisms and the phenomena that cause them. Creating a reasonable RUL calculation for the VDL WEW situation was expected to be complicated. This expectation became true as the accuracy of the calculated RUL was not satisfactory. Node-Red is a very versatile tool, well-chosen for the implementation of the gateway. However, the analysis of production data revealed useful information regarding the impact of newly introduced products on the existing production line. 
 

All the experiments conducted have been interpreted within their business implication: a reliable system (i.e. robust SERENA platform and robust data connection) and an effective Prediction System (i.e. data analytics algorithm able to identify mixing head health status in advance) will have an impact on main KPI related to the foaming machine performances. In particular:

• Overall equipment effectiveness increased 0.4% with an intended achievement of 15% 
• Mean time to repair reduced from 3.5 hours to 3 hours
• Mean time between failures increased from 180 days to over 360 days
• Total cost of maintenance was reduced from €17400 to €8000.

Besides practical results, SERENA provided some important lesson to be transferred into its operative departments:

Data Quality 

Finding the relevant piece of information hidden in large amounts of data, turned to be more difficult than initially thought. One of the main learnings is that Data Quality needs to be ensured since the beginning and this implies spending some more time, effort and money to carefully select sensor type, data format, tags, and correlating information. This turns particularly true when dealing with human-generated data: if the activity of input data from operators is felt as not useful, time-consuming, boring and out of scope, this will inevitably bring bad data.

Some examples of poor quality are represented by:

a.         Missing data

b.         Poor data description or no metadata availability

c.         Data not or scarcely relevant for the specific need

d.         Poor data reliability

The solutions are two: 1) train people on the shop floor to increase their skills on Digitalization in general and a Data-Based decision process specifically; 2) design more ergonomic human-machine interfaces, involving experts in the HMI field with the scope of reducing time to insert data and uncertainty during data input.

These two recommendations can bring in having a better design of dataset since the beginning (which ensure machine-generated data quality) and reduce the possibility of errors, omissions and scarce accuracy in human-generated data.

Data Quantity

PU foaming is a stable, controlled process and it turned to have less variation: thus, machine learning requires large sets of data to yield accurate results. Also, this aspect of data collection needs to de designed in advance, months, even years before the real need will emerge. This turns into some simple, even counterintuitive guidelines: 

1.         Anticipate the installation of sensors and data gathering. The best is doing it at the equipment first installation or its first revamp activity. Don’t underestimate the amount of data you need to improve good machine learning. This, of course, also needs to provide economic justification since the investment in new sensors and data storing will find payback after some years.

2.         Gather more data than needed. Common practice advice is to design a data-gathering campaign starting from the current need. This could lead, though to missing the right data history when a future need emerges. In an ideal state of infinite capacity, the data gathering activities should be able to capture all the ontological descriptions of the system under design. Of course, this could not be feasible in real-life situations, but a good strategy could be to populate the machine with as many sensors as possible.

3.         Start initiatives to preserve and improve the current datasets, even if not immediately needed. For example, start migrating excel files spread in individuals’ PC into commonly shared databases, making good data cleaning and normalization (for example, converting local languages descriptions in data and metadata to English).

Skills

Data Scientists and Process Experts are not yet talking the same language and it takes significant time and effort from mediators to make them communicate properly. This is also an aspect that needs to be taken into account and carefully planned: companies need definitely to close the “skills” gaps and there are different strategies applicable: train Process Experts on data science, train data scientists on the Subject matter; develop a new role of Mediators, which stays in between and shares a minimum common ground to enable the extreme cases to communicate.

In this case edge analytics performed by a low-cost edge device (Raspberry Pi) proved it is feasible to practice predictive maintenance systems like SERENA. However, performance issues were caused by having a large sampling frequency (16kHz) and background processes running on the device, which affected the measurements. By lowering the sampling frequency, this issue can be reduced. Another way would be to obtain different hardware with buffer memory on the AD-card. In addition, a real-time operating system implementation would also work. It was found that the hardware is inexpensive to invest in, but the solution requires a lot of tailoring, which means that expenses grow through the number of working hours needed to customize the hardware into the final usage location. If there are similar applications thatimplement the same configuration, cost-effectiveness increases. Furthermore, the production operators, maintenance technicians, supervisors, and staff personnel at the factory need to be further trained for the SERENA system and its features and functionalities. 
 

From the activities developed within the SERENA project, it became clearer the relation between the accuracy and good performance of a CMM machine and the good functioning of the air bearings system. It was proved or confirmed by TRIMEK’s machine operators and personnel that airflow or pressure inputs and values out of the defined threshold directly affects the machine accuracy. This outcome was possible from the correlation made with the datasets collected from the sensors installed in the machine axes and the use of the tetrahedron artifact to make a verification of the accuracy of the machine, thanks to the remote real-time monitoring system deployed for TRIMEK’s pilot. This has helped to reflect the importance of a cost and time-effective maintenance approach and the need to be able to monitor critical parameters of the machine, both for TRIMEK as a company and for the client’s perspective.

Another lesson learned throughout the project development is related to the AI maintenance-based techniques, as it is required multiple large datasets besides the requirement of having failure data to develop accurate algorithms; and based on that CMM machine are usually very stable this makes difficult to develop algorithms for a fully predictive maintenance approach in metrology sector, at least with a short period for collection and assessment.

In another sense, it became visible that an operator’s support system is a valuable tool for TRIMEK’s personnel, mainly the operators (and new operators), as an intuitive and interacting guide for performing maintenance task that can be more exploited by adding more workflows to other maintenance activities apart from the air bearings system. Additionally, a more customized scheduler could also represent a useful tool for daily use with customers.

As with any software service or package of software, it needs to be learned to implement it and use it, however, TRIMEK’s personnel is accustomed to managing digital tools.  

ARAG solution was piloted in a factory of United Technologies Corporation (UTC). The validation results reflected the potential of the solution, technicians’ acceptability to solutions specifically designed for supporting them in complex operations. Recent studies have shown that gamification tools can be utilized in industrial AR solutions for reducing technicians’ learning curve and increasing their cognition (Tsourma et al., 2019). 
 

Within Factory2Fit there were 2 use cases for the codesign process piloted at Continental plant Limbach-Oberfrohna. One pilot was carried out for the workplace design and one for the work process design. An evaluation of the method selection and execution showed that there was good acceptance among the workers who contributed to the design process. To reach positive results during the codesign process it is essential to assess the boundary conditions and the group structure very well. 
 

On-the-job learning tool was piloted in a UTC factory producing air handling units. What is learned, is that in order to display the content more understandable, users must be able to interact with it, by viewing the components CAD files and make or read remarks.

SoMeP was piloted at Prima Power, unveiling that the integration of production information and messaging is valuable and time-saving in getting guidance. Gamification can motivate workers to share knowledge (Zikos et al., 2019). The use of social media will require organizational policies e.g. in moderating the content (Aromaa et al., 2019). 

The developed tool could be extended to become a part of a bigger communication platform, between the equipment provider and their customers, aiming at strengthening their relationship. 
 

Further studies are being carried out to employ the virtual training in order to train the customers’ operators without having to wait for the delivery of a newly bought machine, or without having to block a productive machine for training purposes. The ADAPT module will be further developed in order to evaluate integration with the recently released MAESTRO Active HMI, which already incorporates personalization features such as language settings. Finally, discussions are underway with the commercial area in order to verify whether the use of wearables by customer’s workers can be promoted in order to improve their well-being at home. 

Even if robots are well known in Europe there is a lack of knowledge on their real potential and on the existence of tools able to simplify their programming and reconfiguration. Most Industries need to be supported in the process of introducing such tools in the plants. Advanced tools for training, are essential

Both WOS and KIT services have been evaluated at COMAU in real life applications, showing that WOS has been well accepted by both operators and engineers as a valuable tool for eliminating motion waste and improve workplace ergonomics in production lines. The evaluation of KIT showed that the developed solution helps to reduce cognitive load of operators, reduce faults and improve efficiency. 

Evaluation studies carried out at the premises of Airbus showed positive results for both of the Exoskeleton and KIT services developed for this specific use case. Both physical and mental fatigue of workers were reduced, as an outcome of the Exoskeleton and KIT services respectively. Workers were keen enough to adopt the new technologies to their everyday working activities. 

Worker Feedback Dashboard was piloted in three factories with ten workers. For user acceptance, it has been crucial that the workers participated in planning how to use the solution, and what kind of work practices were related to its use. The pilot evaluation results indicate that there are potential lead users for the Worker Feedback Dashboard. Introducing the solution would facilitate showing the impacts and could then encourage those who may be more doubtful to join. 

KIT, Exoskeleton service and OAST have been evaluated at ROYO premises in real working conditions. KIT has been characterized as a valuable tool that reduces cognitive load and helps workers eliminate uncertainties at the assembly process. The combination of Exoskeleton service with OAST has helped to reduce the physical and mental stress of the operators at the palletization area of ROYO.