Academic literature on the topic 'Engine defects mapping'

An improved data clustering algorithm was proposed based on the Fuzzy C-Means (FCM) algorithm for the purpose of clustering the data precisely and effectively, through progressing the performance of the data clustering to afford the element work for the application of fault diagnosis and target recognition and so on. There was fatal weakness for the traditional FCM algorithm that the algorithm is sensitive to initial value and noise. The chaotic differential evolution FCM algorithm was proposed according to the efficient global search capability of differential evolution algorithm and the traversal characteristic of chaotic time series. The improved algorithm used the Logistics chaotic mapping to search for the optimal solution, and the chaos disturbance was introduced into the evolutionary population to make up for the defects of FCM algorithm. The new method can overcome the problems of initial value sensitiveness with FCM and local convergence with genetic algorithm. Because the new method. Three types of typical vibration data of faults engines was taken as the example for the research and application. The simulation and application result shows that the data clustering performance of the improved FCM algorithm is much better than the traditional FCM algorithm, and the accuracy rates of fault diagnosis in the application was increased by more than twenty percent, it shows good application prospect.

As human immunodeficiency virus (HIV) treatment programs expand in Africa, delivery systems must be strengthened to support patient retention. Clinic characteristics may affect retention, but a relationship between clinic flow and attrition is not established. This project characterized HIV patient experience and flow in an urban Kenyan clinic to understand how these may affect retention. We used Toyota’s lean manufacturing principles to guide data collection and analysis. Clinic flow was evaluated using value stream mapping and time and motion techniques. Clinic register data were analyzed. Two focus group discussions were held to characterize HIV patient experience. Results were shared with clinic staff. Wait times in the clinic were highly variable. We identified four main barriers to patient flow: inconsistent patient arrivals, inconsistent staffing, filing system defects, and serving patients out of order. Focus group participants explained how clinic operations affected their ability to engage in care. Clinic staff were eager to discuss the problems identified and identified numerous low-cost potential solutions. Lean manufacturing methodologies can guide efficiency interventions in low-resource healthcare settings. Using lean techniques, we identified bottlenecks to clinic flow and low-cost solutions to improve wait times. Improving flow may result in increased patient satisfaction and retention.

Currently the industry is trying to improve its competitiveness. This makes the manufacturingindustry needs to adapt the Lean-Kaizen concept. Lean-Kaizen means eliminating waste through small improvements made on an ongoing basis. This research takes a case study at PT Inoac Poltechno Indonesia, which is engaged in foam production. The current production target is only 56% of the planned target. Besides that, there are found many defective products and activities that have no added value. Furthermore, a map of the current condition is made to find waste and know the kaizen that will be carried out. Furthermore, a future map was developed with work standardization and Value Stream Mapping to determine activities that have no added value. Calculation of Waste Assessment Model (WAM), shows the biggest waste is defect (26.73%), inventory (15.79%), and waiting (13.35%). This study found that by adding trolley, adding operators, making SOPs, scheduling engine maintenance would make non-value added (NVA) activities decrease from 29.95% to 20.5%. Process Cycle Efficiency increased from 22.31% to 28.25% (thus fulfilling international PCE requirements for the manufacturing industry). Keyword : Lean-Kaizen, productivity, waste

Broaching has long been used for machining of fir-trees in gas turbine discs. The fir-tree arrangement is employed for mechanical attachment of blades to disc. Surface and subsurface microstructure changes induced by broaching affect the fatigue resistance of the disc. Therefore, a reliable and quantitative microstructure mapping of the broached fir-trees is essential for the basic optimization of the broaching process. In this study, the microstructure of the surface and subsurface layers of fir-trees in an industrial gas turbine Inconel-718 disc has been analyzed using optical microscopy and scanning electron microscopy. The focus has been on the characterization of defects generated by broaching at the surface and subsurface layers of fir-trees. Also, characterization of the grain size, γ″, γ′, and δ particles from the broached surface to the parent material has been carried out. Characteristics of these microstructural features are key inputs for the development of material based FEM models predicting the fatigue life of the disc. From microstructure studies, the presence of defects such as plucking and distorted layer was observed. The characteristics of these defects (size and morphology) were compared with those of the design limits determined by gas turbine engine manufacturer. Furthermore, significant variation in the volume fraction of δ particles was observed in the area affected by broaching compared with the parent material. These observations are related to the thermomechanical history of the material during the broaching process. Finally, the impact of microstructure evolution on microhardness variation from the broached surface to the parent material is discussed.

Abstract To improve the quality of a manufactured part in industry, a variety of techniques are used to scan a built geometry to bring it back to the physics based simulation world to assess its true performance. There are various laser and structured light measurement techniques (GOM), Computed Tomography (CT) scan as well as touch-point probes in the form of CMM cloud of data that can provide an estimate for the shape of an object. However, there are many challenges on how to construct the digital geometry from the scan in order not to lose any deviations and defects and yet being able to mesh a solid manifold for simulation purposes. In this paper, a novel method based on multi-layered Artificial Intelligence (AI) is presented to produce a meaningful engineering design space to perturb the design-intent geometry to match the manufactured data cloud. The inverse mapping techniques has been applied to a range of real turbomachinery components to demonstrate its flexibility and robustness, even when the original GOM is not perfect. A case study is presented based on a real modern jet engine bypass outlet guide vane (BOGV) to show how constructing and using its digital twin and high-fidelity simulation can save a significant cost for a fleet of engines/aircraft.