Optimization of manufacturing complex-shaped gas turbine blades

Boulila, Atef; Boujelbene, M.; Fekiri, Cheima; Hammami, Amal

doi:10.1016/j.measurement.2018.12.040

Cited by 14 publications

(2 citation statements)

References 32 publications

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“…The analysis of variance (ANOVA), with a significance level of p = .05, was performed to determine whether the perceived discomfort of operators is related to the nature of the maintenance tasks performed in each workshop (Boulila, Boujelbene, Fekiri, & Hammami, 2019;Wallenstein, Zucker, & Fleiss, 1980). All statistical analyses were performed with SPSS Statistics 20.0.…”

Section: Screening Stagementioning

confidence: 99%

Ergonomics risk assessment among maintenance operators in a Tunisian railway company: A case study

Sebti

Boulila

Hamza

2019

Hum Ftrs & Erg Mfg Svc

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Railway truck maintenance plays a crucial role for rail transport. It aims at ensuring the availability of the equipment and the safety of the operations. However, maintenance activities often involve uncommon work and nonroutine tasks and it is obvious that maintenance operators are more threatened than other employees to be exposed to various hazards in their workplace. In Tunisia, the prevalence of occupational accidents and injuries among maintenance workers is not well documented. The target of this cross‐sectional study is to assess ergonomics risks associated with maintenance activities in a railway company. Data collection was carried out through a self‐administrated questionnaire and via direct observations. The Body Parts Symptom Survey (BPSS) was used to identify the discomfort level of the workers. More than 100 maintenance operators working in nine different workshops were involved in this study. From BPSS results, this paper proposed a new method to calculate the discomfort index related to each body part in order to calculate the global discomfort index (GDI) for each workshop. Results showed that the most hazardous workshop was rail‐car maintenance workshop (GDI = 43.06). Four main tasks were assessed with normalized score. Final scores were found to be extremely high. To remedy the raised problems, new adjustable workstations were proposed, which showed a considerable improvement on workers’ postures.

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Section: Screening Stagementioning

confidence: 99%

Ergonomics risk assessment among maintenance operators in a Tunisian railway company: A case study

Sebti

Boulila

Hamza

2019

Hum Ftrs & Erg Mfg Svc

Self Cite

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“…Caudill J et al [13] developed a three-dimensional finite element model for milling Ti-6Al-4V under low temperature and a cutting fluid environment was developed based on Deform, which predicted cutting forces and temperature fields during machining, with results showing that milling at low temperatures can extend tool life and improve machining efficiency. Boulila A et al [14], according to AISI 304 stainless steel blade milling process, studied the effects of the cutting parameters on tool deflection, tracking errors, and surface roughness; the results have shown how much the tool deflection and the tracking errors were mainly influenced by the axial depth and the cutting speed.…”

Section: Introductionmentioning

confidence: 99%

Aero-Engine Blade Cryogenic Cooling Milling Deformation Simulation and Process Parameter Optimization

Chen,

Xu,

Huang

et al. 2023

Materials

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For the machining of aero-engine blades, factors such as machining residual stress, milling force, and heat deformation can result in poor blade profile accuracy. To address this issue, simulations of blade milling were completed using DEFORM11.0 and ABAQUS2020 software to analyze blade deformation under heat-force fields. Process parameters such as spindle speed, feed per tooth, depth of cut, and jet temperature are used to design both a single-factor control and BBD test scheme to study the influence of jet temperature and multiple changes in process parameters on blade deformation. The multiple quadratic regression method was applied to establish a mathematical model correlating blade deformation with process parameters, and a preferred set of process parameters was obtained through the particle swarm algorithm. Results from the single-factor test indicated that blade deformation rates were reduced by more than 31.36% in low-temperature milling (−190 °C to −10 °C) compared with dry milling (10 °C to 20 °C). However, the margin of the blade profile exceeded the permissible range (±50 µm); therefore, the particle swarm optimization algorithm was used to optimize machining process parameters, resulting in a maximum deformation of 0.0396 mm when the blade temperature was −160 °C~−180 °C, meeting the allowable blade profile deformation error.

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The Effects of Technological Parameters on the Accuracy and Surface Roughness of Turbine Blades When Machining on CNC Milling Machines

Hung

2022

Lecture Notes in Mechanical Engineering

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Optimization of manufacturing complex-shaped gas turbine blades

Cited by 14 publications

References 32 publications

Ergonomics risk assessment among maintenance operators in a Tunisian railway company: A case study

Ergonomics risk assessment among maintenance operators in a Tunisian railway company: A case study

Aero-Engine Blade Cryogenic Cooling Milling Deformation Simulation and Process Parameter Optimization

The Effects of Technological Parameters on the Accuracy and Surface Roughness of Turbine Blades When Machining on CNC Milling Machines

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