Z-Fact0r mapped on Technologies and enablers

Within the Z-Fact0r, the proposed (higher level) DSS, with the support of the knowledge base and the online inspection module (1st level decision support at single stage), produce, verify and validate decisions aligned with the quality control policies, production targets, desired product specifications and maintenance management requirements. Key functional characteristics of the envisioned DSS incorporates among others, techniques for monitoring and predicting product quality, action prioritization, root cause analysis, and mitigation planning algorithms (at product and workstation level). Moving beyond existing solutions that focus only on specific aspects of the production procedure, or that are restrained to diagnosis, the proposed DSS system incorporates autonomous, hierarchical decision support, based on process analytical technologies and newly developed suitably adjusted knowledge-based systems, and  combines product monitoring models and data analytics from heterogeneous sources. The envisioned DSS  takes into account a wide set of multiple factors and criteria, such as data uncertainty, lack of information and information quality, involvement of multiple actors, and real-time response. Thanks to the 5 intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z-REPAIR and Z-MANAGE) the overall solution presents a significant contribution to a spectacular improvement in the overall performance and reliability of the targeted multi-stage manufacturing systems and in the production agility (response to continuous adjustments in production targets).

Additive manufacturing (AM) is a widely used set of techniques used to build objects by adding layer-upon-layer of material. While materials typically used are plastic, metal or concrete, nowadays AM technologies are expanding to include all kind of materials such as ceramic, nanocomposites, glass, and other. 

In Z-Fact0r, we exploited AM-based technologies as a tool for repairing of components in a production line. Thanks to the ability for local deposition, i.e. precision placement of material at desired position, AM was the optimum choice to correct or repair a defect. Moreover, AM combined with subtracted manufacturing techniques for the effective repairing. In context, in the case of a defect, material can be subtracted by means of laser ablation or classical machining, thus removing of the problematic area cleaning or preparing the surface. Then, AM is used to fill the defect with the desired material. A final step of sintering or other processing used to finalize the repairing action. 

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 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.

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.