Machine Vision for Warehouse Optimization
Project: AI REGIO
Updated at: 25-01-2024
Optimize the Handling
Reduce Technical Checks by Humans
Improve data quality
Project: AI REGIO
Updated at: 25-01-2024
Optimize the Handling
Reduce Technical Checks by Humans
Improve data quality
Project: MARKET4.0
Updated at: 12-01-2024
Simplified and Efficient Selection of Suitable Manufacturing Equipment: For the equipment manufacturer, IDSRAM permits the trusted connection of the equipment repository with external apps (ESS), permitting (in a time-saving process) the best selection of a manufacturing equipment, thanks to accessible data that where not accessible before.
The MARKET4.0 Metal Domain Data Space propose two solutions:
As a customer, easy access to equipment manufacturers repositories or to manufacturing services. As an equipment or service provider, easy access to the market.
IDS RA configuration and implementation not only on a peer to peer basis, but creating a data space.
Project: A4BLUE
Updated at: 22-12-2023
A collaborative robotic cell has been implemented for the deburring operation where the robot executes the most exhausting phases, while the worker focuses on final quality inspection. Regarding the assembly process, an AR solution, using ultra-real animations has been implemented to guide operators through tasks. Additional AR functionalities include the visualization of textual information (tips, best practices…), access to technical documents and voice recording
The introduction of the is perceived by workers as helpful, especially when productive tasks are exhausting and may provoke health issues. They are not received with reluctance but as supportive in workers’ tasks at the workplace. Regarding AR, it is generally considered as very useful, although the HMD (HoloLens) are too heavy for long time tasks.
Project: AI REGIO
Updated at: 17-07-2023
Automation and integration of the manual Quality Control (QC)
Objectiveness and precision of the QC
Productive increase of QC supervisors
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
Project: AI REGIO
Updated at: 04-07-2023
Monitoring and prediction of water consumption
Automatic detection of water leakages
Optimization of the water pipes network maintenance and installation planning
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
Project: AI REGIO
Updated at: 04-07-2023
Improved fault detection
Increased facility availability
Reduced maintenance service costs (for the customer)
Reduced analysis & diagnosis times (of the Maintenance Service provider)
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
Project: AI REGIO
Updated at: 04-07-2023
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
Project: AI REGIO
Updated at: 04-07-2023
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
Project: AI REGIO
Updated at: 04-07-2023
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.
Project: AI REGIO
Updated at: 04-07-2023
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.
Project: AI REGIO
Updated at: 04-07-2023
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
Project: AI REGIO
Updated at: 04-07-2023
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:
Project: AI REGIO
Updated at: 04-07-2023
Project: AI REGIO
Updated at: 04-07-2023
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.
Project: AI REGIO
Updated at: 04-07-2023
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.
Project: AI REGIO
Updated at: 04-07-2023
improved accuracy of robot arm during milling operations
Higher product quality, higher production flexibility, optimized costs
Project: AI REGIO
Updated at: 04-07-2023
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
Project: AI REGIO
Updated at: 04-07-2023
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
Project: AI REGIO
Updated at: 04-07-2023
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.
Project: SERENA
Updated at: 13-10-2022
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.
Project: SERENA
Updated at: 13-10-2022
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.
Project: SERENA
Updated at: 13-10-2022
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.
Project: SERENA
Updated at: 13-10-2022
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:
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.
Project: SERENA
Updated at: 13-10-2022
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.
Project: Factory2Fit
Updated at: 05-10-2022
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).
Project: Factory2Fit
Updated at: 05-10-2022
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.
Project: Factory2Fit
Updated at: 04-10-2022
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).
Project: Factory2Fit
Updated at: 04-10-2022
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.
Project: Factory2Fit
Updated at: 04-10-2022
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.
Project: Factory2Fit
Updated at: 04-10-2022
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 experiment has achieved the following objectives:
Provide a tool able to help operators to find the exact sheet they need for production line.
The tool can significatively decrease time usually needed to find out, handle and pick up the sheet that is needed from the production line.
The tool is expected to self-learn over time.