Problem description

Vibrations (e.g. due to chatter) are well-known issues in the machining and metal cutting sector. They are responsible for poor surface quality in workpieces. The consequences are:

• The need to finish manually the metal surfaces: time consuming & costly in operations,
• High scrap rates: waste of time and resources.

The main problem of conventional solutions is the separation between quality control process and suppression of machine vibrations.

Vibration suppression is usually based on passive custom-made solutions that are effective only at specific frequencies. Broadband active vibrations control is still uncommon.

In addition, product quality is currently assessed only off-line. In this scenario, if deviations are detected, it is not possible to automatically adjust the CNC process parameters to reduce/compensate such errors. Thus, adjustment procedures rely on machine operator skills and it usually takes a long time to identify the adequate corrective solution.

i4Q Solution

The use case will be implemented in FIDIA’s high speed milling machines production site in Forlì, Italy. The proposed i4Q Solutions will combine advanced vibration monitoring methods, with AI-driven prediction of Quality indicators.

The solution will work at different levels:

• First, a novel add-on kit to monitor the behaviour will be developed and integrated. It integrates accelerometer sensors working very close to the source of vibration.
• The signals will feed AI-driven trained algorithms that predict (in-process) the expected quality of the machined workpieces. The AI algorithms will automatically adjust the process parameters in the case some drift/deviation are detected. The algorithms will also identify equipment components degradation.
• Data coming from the inspection of already machined workpieces will refine the AI models and algorithms. The solution will react in advance to deviations that could cause undesired defects.

The solutions will increase the quality, productivity, and efficiency of the process. The number of rejected parts, time devoted to reworking and manual finishing and costs will be reduced considerably.

Expected results

• Assurance of quality of machined product in finishing stage, through optimization of processing parameters: Final surface roughness under 1 micron
• Reduction of Chatter velocity RMS, through in-process suppression of vibrations:  90% reduction
• Reduced Failed Component Identification Times thanks to equipment degradation patterns identification and forecasting: 50%  reduction
• Quality prediction effectiveness: To be exploited the quality prediction needs to be reliable. Its accuracy needs to be higher than 90%

Woodworking is getting an upgrade

Since 1969, BIESSE has been a stalwart in designing, producing, and marketing technologies and solutions for the woodworking industry, from carpentry to large furniture, windows, and wooden building components. But BIESSE doesn't stop there! They are also expanding into the realm of plastic processing machines, catering to a rapidly growing market. Its latest project has set its sights on revolutionizing CNC (computer numerical control) machines, with the goal of improving data quality, consistency, and integrity. Why, you ask? Because it paves the way for game-changing diagnostic tools that have the potential to:

- Reduce machine breakdowns

- Detect early degradation

- Increase productivity.

- Elevate product quality

The i4Q solution involves additive and virtual sensors, edge computing architecture, and IoT functionalities. This data will power Biesse’s Decision Support System and predictive maintenance algorithms, enabling them to:

- Detect degradation before faults

- Predict critical issues in advance

- Identify inappropriate machine usage

But here's the kicker – these algorithms thrive on reliable data. So, we're conducting rigorous test cycles to acquire quality data from every meaningful device wired into our system. The results are promising, and we're gearing up for:

- Improved data quality

- Enhanced data consistency

- Optimized data integrity

- Advanced diagnostics capability

- Elevated product quality

- Increased machine productivity

The machines will even feature simulation capabilities, allowing us to fine-tune programs before execution, minimizing rejects due to planning or configuration errors. Join the i4Q community and stay tuned for more updates and insights as we work towards a future of precision, productivity, and excellence in woodworking.

Taking White Goods to the Next Level of Quality

Whirlpool Corporation, the global leader in white goods, with an astounding 60 million products sold across 170 countries in 2018. As part of this journey, Whirlpool EMEA plays a pivotal role in distributing approximately 20 million appliances in a dynamic and competitive market. The cornerstone of quality assurance is product conformity testing conducted in laboratories, primarily on pre-series or new variant products. While this phase is crucial for mass production, it is not typically replicated during the product's life cycle due to time and cost constraints. This limitation makes it challenging to identify potential manufacturing process issues, impacting the overall product quality. In the meantime, i4Q is addressing the feasibility of conducting extensive conformity tests across our entire product range. Traditional testing methods are time-consuming and incompatible with our just-in-time production model. i4Q is harnessing the power of data to transform our quality assurance process. By correlating various data sources, such as shop floor metrics, customer service data, and environmental factors, they aim to identify critical correlations that will enable us to focus our testing efforts more effectively.

This approach will lead to:

- Faster problem detection

- Early warning system

- Reduced laboratory costs

The pilot project is underway at the Dishwasher factory in Radomsko, Poland. We are collecting data from various sources within our production process, including assembly lines, equipment, functional tests, and aesthetics control. i4Q will utilize this data to assess product conformity without the need for time-consuming mass testing.

The expected results are remarkable:

- 50% reduction in Time-Cost-Quality expenses

- 15% reduction in ramp-up time 

- 20% reduction in scrap and rework

- 10% decrease in time to market

Join the i4Q community and stay tuned for more updates on this exciting journey for better quality assurance!

Metal Machining like you’ve never seen!

Exciting News from FACTOR! Introducing our pilot episode in the journey to elevate manufacturing excellence! FACTOR, a leading player in metal machining and precision turning, is kickstarting an innovative venture towards perfection in manufacturing quality assurance.

Their goal is to align with the rigorous data quality management requirements of the aerospace industry and become a prominent supplier.

They are on a transformative mission to achieve manufacturing with zero defects by addressing several crucial aspects:

- Eliminating efficiency failures caused by the state of the cutting lubricant.

- Minimizing breakage of cutting tools due to lubricant issues or blocked cooling channels.

- Enhancing diagnosis and evaluation of breakage incidents.

- Ensuring tools work within their optimum range.

- Predicting equipment damage in advance.

- Implementing efficient inspection for all manufactured parts.

Why is this important? Currently, FACTOR lacks a continuous process validation-based manufacturing lines qualification system. This impacts our ability to guarantee the inalterability of products and process data to our customers. i4Q is determined to meet this requirement not only for aerospace and automotive customers but also as a competitive advantage across various sectors.

Expected Results: Our journey aims for impressive outcomes:

- Data accuracy/precision in timestamped sensor capture reduced from 50 to 10 ms.

- CNC machines set-up time reduced by 20%.

- Measurement time for complex parts cut in half, eliminating human error.

- Equipment productivity boosted by 5%.

- Overall equipment effectiveness (OEE) improved by 15%.

- Time to market for new parts reduced by 30%.

Join the i4Q community and stay tuned for more updates and insights on how these solutions are changing Manufacturing for the better!

Ria Stone's Journey Towards Excellence!

Part of the prestigious "Visabeira Group," Ria Stone has been making waves since its inception in 2014. With a groundbreaking factory created to fulfill a monumental contract from IKEA Sweden for manufacturing 486 million tableware products between 2014 and 2026, Ria Stone has been at the forefront of innovation. Ria Stone is on a mission to enhance our Overall Production Effectiveness (OPE) Key Performance Indicators. To achieve this, it’s embracing cutting-edge approaches to production, focusing on innovative defect management and production control methods. This includes the integration of in-line inspection technologies and ICT tools for autonomous, automatic, smart system decision-making.

The i4Q project is implementing an in-line QC spectrometry analysis system that will analyze ceramic pastes from third-party suppliers in real-time, contributing to high manufacturing efficiency and optimal product quality.

Expected Results: What are we aiming for with this transformation? Increased Overall Production Effectiveness, higher First Pass Yield, zero defects in the firing oven, and a remarkable 70% increase in raw material reuse rate!

Join the i4Q community and stay tuned for more episodes like this! 

Farplas is a well-recognized Tier 1 supplier in the automotive industry, specializing in injection-molded parts. The i4Q project offers solutions that enhance quality and operational efficiency. The i4Q Analytical Dashboard enables the monitoring and analysis of sensor-based data via EUROMAP modules and a big data architecture. By training this data using machine learning algorithms, the most critical setup parameters that directly affect quality issues can be identified. This provides production engineers and operators with accurate and valuable insights to make decisions and recalibrate the process when needed, in a proactive manner, ultimately leading to efficient, scrap-free production. Furthermore, fluctuations in sensor value graphs not only help predict defective parts but can also suggest predictive maintenance strategies to minimize malfunctions and downtime, ensuring seamless production.

Considering that painted parts represent the final and most value-added version of injection-molded components, eliminating scrap risks before applying paint is crucial. In this regard, i4Q offers an innovative approach to digitalized quality management. Through graphical analysis of sensor values, along with its key feature of parameter suggestions, potential problems can be addressed even before they occur. This will strengthen the relationship and trust between Farplas and OEMs, further enhancing collaboration.

Generally, i4Q is a Project focusing on zero-defect manufacturing which has a mission about improving data accuracy, reducing waste and enhance productivity for factories. The main impact of i4Q Project is to help manufacturing integrating IoT Technologies, reduce production costs analyzing real-time data, and ensure production quality identifying potential defects early. In addition, the impacts of the Project for Farplas also consist above features beside improving machine performance, reducing downtime by extending the life of parts.

i4QAI is an advanced infrastructure for managing AI models in hybrid a cloud-edge manufacturing environment, featuring a policy-based mechanism for scalable smart deployment. i4QAI is coordinated with i4QEW to ensure that the right set of AI models is made available using Scalable Model Distribution from Cloud to Edge.

i4QAD is a reporting interface that allows to monitoring industrial data with fully flexible visualization drill-down charts and flexible dashboard to provide meaningful analytics to users on real time basis using incremental algorithms.​

i4QBDA provides core functions related to clustering, regression, classification, anomaly detection and temporal correlation of data generated by sensors and other Cyber Physical Systems mounted on industrial facilities. The key property of i4QBDA is speed and ability to support intensive data streams.

i4QBC provides tools to ensure data trustiness and full traceability, enhancing the level of trust by employing a blockchain based data service, improving trust and acceptability by providing security and trust in the data that flows directly to the blockchain, serving as a single point of truth, preserving provenance and supporting non-repudiation.

i4QDIT is a distributed server-based platform with analytic and decision-making services, able to prepare manufacturing data for being efficiently processed by microservice applications. i4QDIT includes all the elements required for manufacturing data stream management: reading, cleaning, storing, indexing, enriching, searching & retrieving, maintaining, and correspondence of open APIs.

i4QDR is a distributed storage system for supporting a high degree of digitisation in companies with most manufacturing devices acting as sensors or actuators and generating vast amounts of data. It is also capable to absorb large volumes of data coming into the systems at high speeds and is elastic to adapt the required computing resources to the existing demand, as well as it is ready to use additional resources, either local to the factory or from remote systems like public or private clouds.

i4QPQ is an intelligent and automated Continuous Process Qualification (CPQ) micro-service for rapid monitoring of stability, capability, and performance of manufacturing processes. i4QPQ is based on real-time data with improved smart data analytics and algorithms, for achieving faster process approval and in-line continuous process validation after process reconfiguration.

i4QDT allows industrial companies to achieve a connected 3D production simulation, with a digital twin for manufacturing enabling virtual validation/visualisation and productivity optimisation using pre-existing and data from different factory levels (small cell to entire factory).

i4QSH is a cloud service that distributes trust across the architecture using a hardware secure module as trust anchor point. Once the trust is distributed, the software enables the mechanisms to expose cryptography operations that other i4Q Solutions can consume, adjusting security and safety policies at different levels to ensure trustability and privacy of data.

i4QIM provides an ensemble of monitoring tools for smart manufacturing workload orchestration and predictive failure alerting, including monitoring the health of workloads and productively alerting and taking corrective actions when a predicted problem is detected. i4QIM supports industrial companies to reach autonomous operation in manufacturing environments.

i4QLRT is a collection of optimisation micro-services that use simulation to evaluate different possible scenarios and propose changes in the configuration parameters of the manufacturing line to achieve improved quality targets. i4QLRT AI learning algorithms develop strategies for machine parameters calibration, line setup and line reconfiguration.

i4QPA is a micro-service consisting of simulation models as a service, taking as input the manufacturing resources, current production planning and process condition, and proposing process configuration parameters, ensuring that non-simulation experts may also exploit the prescriptive analyses.

i4QQE is a web-based software tool for the visualisation of information quality characteristics and quality factors using the Evolutional Data Quality Concept and a Data Life Cycle: data collection, data storage, data analysis, data distribution, data use and data deletion.​​

i4QQD is a micro-service for providing rapid diagnosis of manufacturing line on the possible cause of failures, evaluating data fidelity, product-quality and process condition, and providing action recommendations for sensor/data processing recalibrations, process line/machine reconfiguration or maintenance actions.

i4QDA provides a set of specialised analytic functions and incremental algorithms, operating on data streams with fast incremental updates, suitable for analytic processing of high-speed data streams.

i4QTN is a software-defined industrial interface for data communication, characterized by predictability and determinism, high reliability and trustability and low consumption, while reduce the cost of new communication infrastructures. i4QTN ensures high quality data collection, providing connectivity to industrial data sources through Trusted Networks able to assess and ensure precision, accuracy, and reliability.