The Prediction and Evaluation of Surface Quality during the Milling of Blade-Root Grooves Based on a Long Short-Term Memory Network and Signal Fusion.

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.

An optional surgical technique for obtaining lamellar donor grafts: a pilot study.

BACKGROUND: To evaluate the surface quality and thickness uniformity of lamellar donor grafts using an optional surgical technique called reversed manual dissection (RMD) in porcine corneas. METHODS: Twenty-four paired porcine corneas (48 eyes) were numbered 1 to 24 and divided into 6 groups. All left corneas were assigned to conventional manual dissection (CMD), and all right corneas were assigned to RMD. Each group contained 8 corneas. For Groups I, II, and III, 30, 50, and 70% of the entire corneal thickness was dissected using CMD. For groups IV, V, and VI, 70, 50, and 30% of the entire corneal thickness was dissected using RMD. The residual stromal thickness was examined by anterior segment optical coherence tomography (ASOCT) to assess the thickness uniformity and scanning electron microscopy (SEM) to assess the surface quality. RESULTS: The thickness uniformity of the lamellar grafts between each paired group was not significantly different (p > 0.05). The qualitative surface roughness grading (QiSR) evaluated by masked observers through SEM was significantly higher in the RMD groups (p < 0.001). The quantitative surface roughness grading (QnSR) acquired from the Mountains software was significantly lower in the RMD groups (p < 0.001). CONCLUSIONS: RMD is an optional surgical technique for obtaining porcine lamellar grafts. The thickness uniformity of RMD is comparable to that of CMD, and a smoother surface with fewer ridges and roughness is achieved compared to CMD.

Micro Electrochemical Milling of Micro Metal Parts with Rotating Ultrasonic Electrode.

With the rapid development of MEMS, the demand for metal microstructure is increasing. Micro electrochemical milling technology (MECM) is capable of manufacturing micro metallic devices or components based on the principle of electrochemical anode dissolution. To improve the capacity of MECM, this paper presents a compound method named ultrasonic vibration-assisted micro electrochemical milling technology (UA-MECM). Firstly, the simulation and mathematical model of UA-MECM process is established to explain the mechanism of ultrasonic vibration on micro electrochemical milling. Then, the effects of ultrasonic parameters, electrical parameters and feedrate on machining localization and surface quality are discussed considering sets of experiments. The surface roughness was effectively reduced from Ra 0.83 to Ra 0.26 µm with the addition of ultrasonic vibration. It turns out that ultrasonic vibration can obviously improve machining precision, efficiency and quality. Finally, two- and three-dimensional microstructures with good surface quality were successful fabricated. It shows that ultrasonic vibration-assisted electrochemical milling technology has excellent machining performance, which has potential and broad industrial application prospects.

Machined Surface Quality Monitoring Using a Wireless Sensory Tool Holder in the Machining Process.

The quality of a machined surface plays a critical role in assembly performance, especially for precise matching parts, and therefore it is necessary to develop a surface quality monitoring system in the machining process. In this paper, an indirect surface quality monitoring approach is proposed with a wireless sensory tool holder. First, experimentation is conducted to collect the machining process signals from the tool holder. Then, the time domain, frequency domain and time-frequency domain features are extracted, and the deep forest algorithm is adopted to identify the surface quality, which is evaluated through the surface average parameter. Finally, the results of the experiment and the comparisons with other approaches demonstrate the effectiveness of the proposed method, which could be applied to ensure the surface quality, improve the machining efficiency and reduce the rejection rate of the machining process.

Objective 3D Printed Surface Quality Assessment Based on Entropy of Depth Maps.

A rapid development and growing popularity of additive manufacturing technology leads to new challenging tasks allowing not only a reliable monitoring of the progress of the 3D printing process but also the quality of the printed objects. The automatic objective assessment of the surface quality of the 3D printed objects proposed in the paper, which is based on the analysis of depth maps, allows for determining the quality of surfaces during printing for the devices equipped with the built-in 3D scanners. In the case of detected low quality, some corrections can be made or the printing process may be aborted to save the filament, time and energy. The application of the entropy analysis of the 3D scans allows evaluating the surface regularity independently on the color of the filament in contrast to many other possible methods based on the analysis of visible light images. The results obtained using the proposed approach are encouraging and further combination of the proposed approach with camera-based methods might be possible as well.

Experimental Study on the Performance of Hydraulic Vibration Assisted Broaching (HVAB) Based on Piezoelectric Sensors.

In order to improve the keyway broaching process and verify the feasibility of vibration-assisted broaching process, an experimental study on a novel hydraulic vibration assisted broaching (HVAB) system with double-valve electro-hydraulic exciter (DVEHE) is proposed in this paper. The performances of HVAB at different excitation frequencies were compared from three aspects: (a) the cutting force under the different vibration frequencies, (b) the surface roughness of the workpiece, and (c) the flank face wear of the tool. For precision on-line measurement of larger broaching forces, four piezoelectric sensors were fixed on the broaching machine. The experimental results show that HVAB can effectively improve the performance of the broaching process, approximately reduce the broaching force by as much as 9.7% compared to conventional broaching (CB) and improve the surface quality of workpiece. Some explanations are offered to support the observations.

Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems.

Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor.

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%.

Influence of midday removal and re-application of a scleral lens on fluid reservoir thickness, pre-lens tear film quality and visual acuity.

PURPOSE: To investigate whether the midday removal and re-application of scleral lenses (SL) influences fluid reservoir (FR) thickness, pre-lens tear film quality and visual acuity. METHODS: Two clinical experiments were conducted. A total of 49 keratoconic eyes were evaluated for Part1(tear film and visual acuity analysis) and 12 keratoconic eyes for Part2 (FR thickness analysis). All subjects were wearing 16.4 mm SL for more than 12-months. Tear Film Surface Quality (TFSQ) was evaluated with Medmont E300 at more than 120 min of SL wear, 10 min after SL removal (pre-corneal TFSQ) and 5 min after re-apply the same SL. High and Low Contrast Visual Acuity (HCVA and LCVA) were also assessed with the SL on eye (before and after re-application). For Part2, Anterior OCT (MOptim MOcean4000, China) measurements were taken with and without the SL (at the same time points of Part1) and three outcomes were evaluated: FR thickness, SL thickness (control measurement) and corneal thickness. RESULTS: Removing and re-applying a SL had a statistically significant positive impact on TFSQ, with an improvement from 0.26 ± 011 to 0.16 ± 0.08 (p = 0.001). This was accompanied by a statistically significant improvement in LogMAR HCVA (from 0.10 ± 0.09 to 0.08 ± 0.08, p < 0.001) and LCVA (from 0.39 ± 0.13 to 0.36 ± 0.13, p < 0.001). Regarding Part2 of the study, a statistically significant increase in FR thickness was observed after SL re-application (from 223.64 ± 48.08 µm to 267.81 ± 80.03 µm, p = 0.007). No changes in corneal thickness were observed. CONCLUSIONS: Midday removal and re-application of a scleral lens positively impacted pre-lens tear film surface quality, although the observed improvement in visual acuity does not constitute a clinically significant change. Clinicians should consider that removing and reapplying a scleral lens may result in an overestimation of the fluid reservoir thickness, which could affect clinical assessments and treatment decisions.

Investigation on the Surface Integrity of 40Cr Steel Machined by Rotary Ultrasonic Flank Milling.

Rotary Ultrasonic Machining (RUM) stands as a crucial method for machining hard and brittle materials. However, for machining hard-to-machine metal, it continues to face many challenges due to the complex vibration of the milling tool. Flank milling is an efficient method for machining complex parts, such as blisks and impellers, which have been widely used in aerospace field. However, current research is more focused on rotary ultrasonic end milling. In this context, we will study the surface integrity of rotary ultrasonic flank milling 40Cr steel using a self-developed RUM system. We delve into exploring the impacts of tool vibration on surface morphology, residual stress, and micro-hardness of the workpiece under various process parameters. The experimental findings reveal that rotary ultrasonic flank milling, in contrast to traditional flank milling techniques, significantly diminishes the surface roughness by about 40%. The reasons for the reduction of surface roughness are analyzed from the point of view of the cutting force. The surface roughness appears to be notably linked to both the average cutting force and the frequency domain characteristics. In addition, the experimental results indicate that rotary ultrasonic flank milling demonstrates the capacity to elevate the micro-hardness of the machined surface.