What is Z-Fact0r and how does the entire solution work?

Z-Fact0r is a distributed system, consisting of various software and hardware systems, build on the philosophy of zero-defect manufacturing strategy. It is applicable to multi stage production. Z-Fact0r system is a holistic framework applicable both to new and existing manufacturing lines to achieve zero-defects productions, with the ultimate goal, to become a standard solution.

It is a flexible system that aims to address the client’s constantly updated needs, by adopting an enterprise level risk-based philosophy, through support of faster and better decision making at the shop-floor.

Z-Fact0r’s architecture, based on the blackboard’s architectural pattern, supports a message broker model with a message queue for (near) real time data capture and persistence. Additionally it supports a data sync and transformation engine the converts relational data to knowledge. 

It envisages the multi-stage manufacturing line as a system of constantly updated parameters, identifying the parameters that cause the greatest deviations from the the initial target values and lead to defected products. The 5 intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z-REPAIR and Z-MANAGE) are expected to contribute to a crucial improvement of the targeted multi-stage manufacturing lines.

Z-DETECT – targeting the early detection of the defect

Z-DETECT is the first strategy of the Z-Fact0r solution: The detection strategy consists of detecting any machining process anomaly or instability through process monitoring by means of controlled variables called critical process variables (CPVs). In particular, this strategy is invoked when a defect is being generated after the adaptation of the parameters. In such a scenario, an alarm is being triggered to flag the parameters that resulted in a defect. By mapping the true reasons, the system will be able to avoid having more generated defects by weighting the system model.

Apart from the inspection of the product from which the defect is being observed, the strategy involves more actions and processes to deal both with the generation of the detected defect, and its propagation to the next stages.

Z-PREDICT – Prediction of defect generation

Z-PREDICT strategy is triggered when a defect is recognised during the Z-DETECT stage.

 The events detected from the physical layer of the system are engineered into high value data that will stipulate new and more accurate process models. Such an unbiased systems behaviour monitoring and analysis provides the basis for enriching the existing knowledge of the system (experience) learning new patterns, raising attention towards behaviour that cause operational and functional discrepancies (e.g. alarms) and the general trends in the shop-floor.

The more the data pool is being increased the more precise (repeatability) and accurate the predictions will be. The estimations for the future states involve the whole production line, e.g. machine status after x number of operations and/or quality of the products for given set of parameters.

The system will predict with high confidence the expected quality and customer satisfaction, allowing modifications to the parameters before the production of the products. In addition, Z-Fact0r can operate in the reverse mode, i.e. insert a Customer Satisfaction Goal and control the parameters accordingly to achieve this target.

The ability of Z-Fact0r to optimise the manufacturing processes according to certain/target quality levels and/or customer satisfaction is the key innovation to fulfil the industrial requirements.

Z-PREVENT – the prevention of defect generation and propagation in later stages of the production

Z-PREVENT strategy is triggered after Z-PREDICT stage and is about the prevention of defect generation by recalibrating the production line (multi-stage), as well as defect propagation in later stages of the production. It will harmonise the system based on historical, current and future (predicted) data to fine-tune the system to preserve the quality levels inside the acceptable limits.

The prevention of defects strategy is based on the quality control and the inspection tools realised across the shop floor for condition monitoring of machinery and respective produced quality.

The initial estimation of the future states and expected outcomes are taken into account and based on the simulation and modelling of the parameters. For each predicted defect, the responsible parameters are identified and flagged. The system adapts these parameters based on an initial estimation, which after the simulation these are corrected recursively.

The result of this process is to avoid the generation of defects based on each recorded event (defect, no-defect, low quality, high-quality) both from previous and current states.

The system will demonstrate reduced false alarms by combining the future predictions.

Z-REPAIR – Remanufacturing of the product using additive / subtractive techniques

Once a “repairable” defect is detected (Z-DETECT), a proper and customized repairing action must be deployed with the minimum time and effort, assuring the best productivity and production flow. In fact, a major challenge for an effective ZD manufacturing is related with the capability to automatically repair the occurred defects without perturbing the overall production flow.

Z-Fact0r is developing a model-based, supervisory control solution that will be able to interpret the interstage quality control measurements together with the monitoring of the process itself, in order to identify the defect sources and generate a proper and customized repairing action. Additive manufacturing in the form of inkjet or paste printing of various materials (metal, ceramic, polymer resins) can successfully be used to fill a missing spot or correct a damaged part. Upon detection of the defected area, the printing head will deliver the patch material in solution or paste form. In the case of inkjet printing, defect as small as 20 μm can be patched. Post printing treatment of the delivered material include solvent evaporation (e.g. in the case of polymer patches), UV curing (e.g. in the case of epoxy resins) and low temperature laser sintering in the case of metal or ceramic nanoparticles, thermal curable resins or paste where a local reflow process is required.

To facilitate the implementation of the five strategies, Z-Fact0r is supporting a “reverse supply-chain” policy in the context of a multi-stage supply-chain attached to a multi-stage production. As a result, the defected products/parts detected in downstream stages (produced during a stage, or provided from suppliers in a particular stage) could be returned to upstream stages for remanufacturing or recycling.

Z-MANAGE – Management of all strategies through event modeling, key performance indicators monitoring and real-time decision support

The overall supervision and optimisation of the system is achieved after the execution of Z-MANAGE strategy. The defects are processed with Decision support system (DSS) tools and are interfaced with Manufacturing Execution Systems (MES). False positives and false negatives are clustered after each Z-Fact0r strategy, which results into a good filtering of these false alarms. To achieve so, the previous acquired knowledge and incidents are also processed to fine tune the system’s operation. Additionally, the production is optimised by better scheduling, taking into account the environmental impact of each process. The optimised scheduling and adaptability of the manufacturing improves the overall flexibility, placing a premium on the production rates, satisfying the demand, while preserve increased machinery availability. Since, the Knowledge management system will tune the whole production according to certain quality levels and customer satisfaction, it is highly anticipated that the overall performance of the system will suffice the increased needs of the customers.

Z-Manage strategy involves also a Knowledge based decision support system which collects knowledge from all the components and the operators and therefore is able to suggest solution for the tuning the rest of the components.

The strategy involves also the decision making in the event of a defect. The defect will be analysed via the inspection system, from which the defect can be classified and categorised on its severity. In case of “repairable” defects the system will decide for the following; (i) rework on spot, (ii) removal from the production line for further inspection and rework. If the defect is classified as “non-repairable” then the system will decide whether (a) the product will be forwarded to upstream stages, or (b) considered as total failure where it will be recycled.