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Thousands of users are streamlining their Web interactions through user scripts using special weavers such as Greasemonkey. Thousands of programmers are releasing their scripts in public repositories. Millions of downloads prove the success of this approach. So far, most scripts are just a few lines long. Although the amateurism of this community can partially explain this fact, it can also stem from the doubt about whether larger efforts will pay off. The fact that scripts directly access page structure makes scripts fragile to page upgrades. This brings the nightmare of maintenance, even more daunting considering the leisure-driven characteristic of this community. On these grounds, this work introduces interfaces for scripting. Akin to the JavaScript programming model, Scripting Interfaces are event-based, but rather than being defined in terms of low-level, user-interface events, Scripting Interfaces abstract these DOM events into conceptual events. Scripts can now subscribe to or notify of conceptual events in a similar way to what they did before. So-developed scripts improve their change resilience, portability, readability and easiness to collaborative development of scripts. This is achieved with no paradigm shift: programmers keep using native JavaScript mechanisms to handle conceptual events.

Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine.

Traceability is a critical issue in the manufacturing of aerospace components. However, extracting understandable information from huge amounts of data from manufacturing processes may become a very difficult task. In this paper, a novel proposal for geometrical defect detection in the manufacturing of fir-tree slots for disk turbines using wire electrical discharge machining is presented. Useful data about the wire Electrical Discharge Machining (WEDM) process are collected every 5 ms and each single discharge is classified as a function of ignition delay time. Information from this large amount of data is extracted by using a deep neural network, which includes two hidden dense layers, each with 64 units and Relu activation, and it ends with a single unit with no activation. The average of the per-epoch absolute error (MAE) scores has been used to decide the optimum training situation for the deep learning network. Validation of the method has been carried out by machining a high-precision fir-tree slot for a disk turbine under industrial conditions. Results show that even though a strict tolerance band of ±5 µm has been applied, as many as 80% of the predictions from the network match the results of the conventional measuring method (coordinate measuring machine).

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