Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model.

Automated platform for consistent part realization with regenerative hybrid additive manufacturing workflow

Nowadays, the role of hybridization within the wider manufacturing ecosystem gains significant momentum with multiple commercial solutions already available on the market. Despite the very promising benefits of combining and selectively exploiting the advantages of additive and subtractive technologies on the same machine, hybrid additive manufacturing is far from reaching its full potential. One of the central limitations of existing hybrid process chains is the lack of a harmonized, structured and automated workflows to support an adaptive manufacturing strategy. This work is motivated by the need to bridge this gap and to capture the logic behind an adaptive hybrid process chain with the aim to support the achievement of enhanced product quality and improved operational efficiency in hybrid additive manufacturing. The paper discusses the implementation of a hybrid CAx platform and the underlying methodology aiming at the dynamic reduction of variabilities associated with the laser metal deposition process. The hybrid workflow identifies the most adapted sequence and planning of additive and subtractive operations while considering part inspection as an in-envelope functionality to quantify the geometrical and dimensional part deviations and to trigger the regenerative mechanism. The methodology is demonstrated on a hybrid machine by deploying laser ablation for the in situ removal of build deviations and an adapted deposition operation as part of a regenerative strategy leading to higher part confidence.

Imaging sensor data modelling and evaluation based on optical composite characteristics

Automated Fibre Placement is a common manufacturing technique for composite parts in the aero-space industry. Therefore, a visual part inspection is required which often covers up to 50% of the actual production time. Moreover, the inspection quality of this manual step fluctuates significantly. A camera-based automated inline inspection is capable of increasing the inspection efficiency and accuracy. However, the interpretability of the acquired data strongly depends on the sensor configuration and the inspected material. Thus, this paper introduces methods for modelling and assessing an imaging sensor on the example of a composite material reflecting a spot laser to a camera sensor. In this context, the reflection properties of the material are incorporated into a simulation and validated in comparison to real camera images from the experimental setup. The EMVA 1288 sensor model in combination with the Cramér–Rao lower bound indicates a feasible estimability of the beam propagation, but shows limitations in the predictability of the number of incident photons. The laser spot analysis indicated that the laser spot can deviate from an exact oval shape but its peak value is suitable for robust spot identification in an image. The outlined methodology is also adaptable to other imaging sensors, illumination sources and materials. Thus, the findings can be useful for other fields and manufacturing processes.

Touch Probe Measurement in Dimensional Metrology: A Review

Coordinate measuring machines (CMMs) are the standard displacement systems used for measurements in dimensional metrology. Since measurement with a touch probe mounted on a CMM provides high accuracy, repeatability, and reliability, it has been widely used for mechanical part inspection in manufacturing. The inspection process requires the use of several sensor orientations and optimal positioning of the part in order to measure all features. Recently, the field of probing path planning has become a huge and active research field. In this paper, various techniques aimed at generating the probe paths for part inspection are reviewed. Multiple issues related to the positioning of the part to maximise accessibility, analysis of probe accessibility to measure all inspection features, optimisation of the measurement sequence, distribution of measurement points, and collision avoidance are mentioned. The common research approaches and potential algorithms in this field are also discussed in this paper.

A Case Study- Monitoring and Inspection Based on IoT for STEP-NC Data Model

Cyber-Physical Machine Tools (CPMT) is currently recognized as a new generation of machine tools that align with Industry 4.0 needs as a smart, well connected, advanced accessibility, more adaptive and autonomous solution. It can be achieved through standardized design method and communication protocols. This article presents a case study on monitoring and inspection based on Internet of Things (IoT) for STEP-NC data model toward a CPMT. More specifically, the monitoring approach utilizing an IoT based monitoring architecture for machining process monitoring, while the inspection approach using a coordinate measuring machine for machined part inspection. The monitoring and inspection approaches can achieve high accuracy of machining process condition detection and enable measurement of machined parts to fulfil CPMT needs and I4.0. The case study validated that the developed monitoring approach performed well and was highly sensitive to any changes during the machining process, specifically on the tool condition. As per the inspection approach, the reliability of the machined model was 99.97%. Based on the results of both the approaches, it is confirmed that both tasks can be designed and support digital factory and other manufacturing process stages in the future such as preventive maintenance, inspecting, sizing, assembling, and others.

A Systems Approach to Estimating the Uncertainty Limits of X-ray Radiographic Metrology

Micro- and nano-manufacturing capabilities have rapidly expanded over the past decade to include complex 3d structure fabrication; however, the metrology required to accurately assess these processes via part inspection and characterization has struggled to keep pace. X-ray Computed Tomography (XCT) is considered an ideal candidate for providing the critically needed metrology on the smallest scales, especially internal features or inaccessible regions. XCT supporting micro- and nano-manufacturing often push against the poorly understood resolution and variation limits inherent to the machines which can distort or hide fine structures. We develop and experimentally verify a comprehensive analytical uncertainty propagation Signal Variation Flow Graph (SVFG) model for X-ray radiography in this work to better understand resolution and image variability limits on the small scale. The SVFG approach captures, quantifies and predicts variations occurring in the system that limit metrology capabilities, particularly in the micro/nano domain. This work is the first step to achieving full uncertainty modeling of computed tomography (CT) reconstructions and provides insight into improving X-ray attenuation imaging systems. The SVFG methodology framework is applied to generate a complete basis set of functions describing the major sources of variation in radiographs. Five models are identified, covering variation in Energy (0DE), Intensity (0DI), Length (1DL), Blur (1DB), and Position (2DP). Radiographic system experiments are defined to measure the parameters required by the SVFGs. Best practices are identified for these measurements. The SVFG models are confirmed via direct measurement of variation to predict variation within 30% on average.

Path Planning and Setup Orientation for Automated Dimensional Inspection Using Coordinate Measuring Machines

Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.

Hybrid subtractive–additive manufacturing processes for high value-added metal components

Hybrid process chains lack structured decision-making tools to support advanced manufacturing strategies, consisting of a simulation-enhanced sequencing and planning of additive and subtractive processes. The paper sets out a method aiming at identifying an optimal process window for additive manufacturing, while considering its integration with conventional technologies, starting from part inspection as a built-in functionality, quantifying geometrical and dimensional part deviations, and triggering an effective hybrid process recipe. The method is demonstrated on a hybrid manufacturing scenario, by dynamically sequencing laser deposition (DLM) and subtraction (milling), triggered by intermediate inspection steps to ensure consistent growth of a part.