Scanning Electrochemical Probe Lithography for Ultra-Precision Machining of Micro-Optical Elements with Freeform Curved Surface.

Two challenges should be overcome for the ultra-precision machining of micro-optical element with freeform curved surface: one is the intricate geometry, the other is the hard-to-machining optical materials due to their hardness, brittleness or flexibility. Here scanning electrochemical probe lithography (SECPL) is developed, not only to meet the machining need of intricate geometry by 3D direct writing, but also to overcome the above mentioned mechanical properties by an electrochemical material removal mode. Through the electrochemical probe a localized anodic voltage is applied to drive the localized corrosion of GaAs. The material removal rate is obtained as a function of applied voltage, motion rate, scan segment, etc. Based on the material removal function, an arbitrary geometry can be converted to a spatially distributed voltage. Thus, a series of micro-optical element are fabricated with a machining accuracy in the scale of 100 s of nanometers. Notably, the spiral phase plate shows an excellent performance to transfer parallel light to vortex beam. SECPL demonstrates its excellent controllability and accuracy for the ultra-precision machining of micro-optical devices with freeform curved surface, providing an alternative chemical approach besides the physical and mechanical techniques.

A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining.

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining.

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

An End-to-End Inclination State Monitoring Method for Collaborative Robotic Drilling Based on Resnet Neural Network.

The collaborative robot can complete various drilling tasks in complex processing environments thanks to the high flexibility, small size and high load ratio. However, the inherent weaknesses of low rigidity and variable rigidity in robots bring detrimental effects to surface quality and drilling efficiency. Effective online monitoring of the drilling quality is critical to achieve high performance robotic drilling. To this end, an end-to-end drilling-state monitoring framework is developed in this paper, where the drilling quality can be monitored through online-measured vibration signals. To evaluate the drilling effect, a Canny operator-based edge detection method is used to quantify the inclination state of robotic drilling, which provides the data labeling information. Then, a robotic drilling inclination state monitoring model is constructed based on the Resnet network to classify the drilling inclination states. With the aid of the training dataset labeled by different inclination states and the end-to-end training process, the relationship between the inclination states and vibration signals can be established. Finally, the proposed method is verified by collaborative robotic drilling experiments with different workpiece materials. The results show that the proposed method can effectively recognize the drilling inclination state with high accuracy for different workpiece materials, which demonstrates the effectiveness and applicability of this method.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring.

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard.

The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss functions within the eXtreme Gradient Boosting (XGBoost) algorithm, which is a critical step in enhancing the algorithm's performance for specific applications. Our research is motivated by the need for precision and efficiency in the industrial domain, where the implications of misclassification can be substantial. We focus on the drill-wear analysis of melamine-faced chipboard, a common material in furniture production, to demonstrate the impact of custom loss functions. The paper explores several variants of Weighted Softmax Loss Functions, including Edge Penalty and Adaptive Weighted Softmax Loss, to address the challenges of class imbalance and the heightened importance of accurately classifying edge classes. Our findings reveal that these custom loss functions significantly reduce critical errors in classification without compromising the overall accuracy of the model. This research not only contributes to the field of industrial machine learning by providing a nuanced approach to loss function customization but also underscores the importance of context-specific adaptations in machine learning algorithms. The results showcase the potential of tailored loss functions in balancing precision and efficiency, ensuring reliable and effective machine learning solutions in industrial settings.

A Robotic Solution for Precision Smoothing and Roughening of Precast Concrete Surfaces: Design and Experimental Validation.

Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot's escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction.

The Effect of Annotation Quality on Wear Semantic Segmentation by CNN.

In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation.

Longitudinal-Torsional Frequency Coupling Design of Novel Ultrasonic Horns for Giant Magnetostrictive Transducers.

The use of advanced brittle composites in engineering systems has necessitated robotic rotary ultrasonic machining to attain high precision with minimal machining defects such as delamination, burrs, and cracks. Longitudinal-torsional coupled (LTC) vibrations are created by introducing helical slots to a horn's profile to enhance the quality of ultrasonic machining. In this investigative research, modified ultrasonic horns were designed for a giant magnetostrictive transducer by generating helical slots in catenoidal and cubic polynomial profiles to attain a high amplitude ratio (TA/LA) and low stress concentrations. Novel ultrasonic horns with a giant magnetostrictive transducer were modelled to compute impedances and harmonic excitation responses. A structural dynamic analysis was conducted to investigate the effect of the location, width, depth and angle of helical slots on the Eigenfrequencies, torsional vibration amplitude, longitudinal vibration amplitude, stresses and amplitude ratio in novel LTC ultrasonic horns for different materials using the finite element method (FEM) based on the block Lanczos and full-solution methods. The newly designed horns achieved a higher amplitude ratio and lower stresses in comparison to the Bezier and industrial stepped LTC horns with the same length, end diameters and operating conditions. The novel cubic polynomial LTC ultrasonic horn was found superior to its catenoidal counterpart as a result of an 8.45% higher amplitude ratio. However, the catenoidal LTC ultrasonic horn exhibited 1.87% lower stress levels. The position of the helical slots was found to have the most significant influence on the vibration characteristics of LTC ultrasonic horns followed by the width, depth and angle. This high amplitude ratio will contribute to the improved vibration characteristics that will help realize good surface morphology when machining advanced materials.

Development of a Small-Footprint 50 MHz Linear Array: Fabrication and Micro-Ultrasound Imaging Demonstration.

Compact high-frequency arrays are of interest for clinical and preclinical applications in which a small-footprint or endoscopic device is needed to reach the target anatomy. However, the fabrication of compact arrays entails the connection of several dozens of small elements to the imaging system through a combination of flexible printed circuit boards at the array end and micro-coaxial cabling to the imaging system. The methods currently used, such as wire bonding, conductive adhesives, or a dry connection to a flexible circuit, considerably increase the array footprint. Here, we propose an interconnection method that uses vacuum-deposited metals, laser patterning, and electroplating to achieve a right-angle, compact, reliable connection between array elements and flexible-circuit traces. The array elements are thickened at the edges using patterned copper traces, which increases their cross-sectional area and facilitates the connection. We fabricated a 2.3 mm by 1.7 mm, 64-element linear array with elements at a 36 µm pitch connected to a 4 cm long flexible circuit, where the interconnect adds only 100 µm to each side of the array. Pulse-echo measurements yielded an average center frequency of 55 MHz and a -6 dB bandwidth of 41%. We measured an imaging resolution of 35 µm in the axial direction and 114 µm in the lateral direction and demonstrated the ex vivo imaging of porcine esophageal tissue and the in vivo imaging of avian embryonic vasculature.

Reaction Mechanisms of High-Rate Copper Electrochemical Machining in Nitrate Electrolytes.

The high-rate electrochemical dissolution of copper in nitrate electrolytes is investigated primarily via polarization curves, while varying parameters such as the electrolyte flow velocity, the electrolyte resistance, the anode geometry, and the temperature. This study focuses on the re-rise in current at high voltages after the limiting current plateau. As a result of the studies, a change in the complexation mechanism from hydration to "solvo-nitration" is proposed, which requires an additional potential drop within the electrochemical double layer.

Discussions on Some Key Issues of Two-Dimensional Rotational Ultrasonic Combined Electro-Machining of Composite Materials.

In order to improve the surface forming quality and machining efficiency of composite materials and reduce tool wear, a two-dimensional rotary ultrasonic combined electro-machining (2DRUEM) technology with low electrical conductivity and low current density was proposed in this study. Additionally, a gap detection unit of the machining system was designed with the integration of grinding force and gap current, and the average errors and maximum errors of the model were 5.61% and 12.08%, respectively, which were better than single detection. Furthermore, the machining parameters were optimally selected via NSGA-II, and the maximum machining surface roughness error was 5.9%, the maximum material removal rate error was 5.5%, and the maximum edge accuracy error was 8.9%, as established through experiments.

Investigation of the Information Possibilities of the Parameters of Vibroacoustic Signals Accompanying the Processing of Materials by Concentrated Energy Flows.

Creating systems for monitoring technology processes based on concentrated energy flows is an urgent and challenging task for automated production. Similar processes accompany such processing technologies: intensive thermal energy transfer to the substance, heating, development of the melting and evaporation or sublimation, ionization, and expansion of the released substance. It is accompanied by structural and phase rearrangements, local changes in volumes, chemical reactions that cause perturbations of the elastic medium, and the propagation of longitudinal and transverse waves in a wide frequency range. Vibrational energy propagates through the machine's elastic system, making it possible to register vibrations on surfaces remotely. Vibration parameters can be used in monitoring systems to prevent negative phenomena during processing and to be a tool for understanding the processes' kinetics. In some cases, it is the only source of information about the progress in the processing zone.

A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

Experimental Investigation of Tip Wear of AFM Monocrystalline Silicon Probes.

AFM has a wide range of applications in nanostructure scanning imaging and fabrication. The wear of AFM probes has a significant impact on the accuracy of nanostructure measurement and fabrication, which is particularly significant in the process of nanomachining. Therefore, this paper focuses on the study of the wear state of monocrystalline silicon probes during nanomachination, in order to achieve rapid detection and accurate control of the probe wear state. In this paper, the wear tip radius, the wear volume, and the probe wear rate are used as the evaluation indexes of the probe wear state. The tip radius of the worn probe is detected by the nanoindentation Hertz model characterization method. The influence of single machining parameters, such as scratching distance, normal load, scratching speed, and initial tip radius, on probe wear is explored using the single factor experiment method, and the probe wear process is clearly divided according to the probe wear degree and the machining quality of the groove. Through response surface analysis, the comprehensive effect of various machining parameters on probe wear is determined, and the theoretical models of the probe wear state are established.

Estimation of Shape Error with Monitoring Signals.

Recently, extensive research has actively been conducted in relation to intelligent manufacturing systems. During the machining process, various factors, such as geometric errors, vibrations, and cutting force fluctuations, lead to shape errors. When a shape error exceeds the tolerance, it results in improper assembly or functionality issues in the assembled part. Predicting shape errors before or during the machining process helps increase production efficiency. In this paper, we propose a methodology that uses monitoring signals and on-machine measurement (OMM) results to predict machining quality in real time. We investigate the correlation between monitoring signals and OMM results and then construct a machine learning model for shape error estimation. The developed model implements a tool offset compensation strategy. The performance of the proposed method is evaluated under various sliding window sizes and the compensation weights. The experimental results confirmed that the proposed algorithm is effective for obtaining a uniform machining quality.

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning.

The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions and demanding machining processes lead to a high relevance of machining domain experts and a low degree of manufacturing flow automation. In order to increase the degree of automation, online process monitoring and the prediction of the quality-related remaining cutting tool life is indispensable. However, the varying process conditions complicate this as the correlation between the sensor signals and tool condition is not directly apparent. Furthermore, machine learning (ML) knowledge is limited on the shop floor, preventing a manual adaption of the models to changing conditions. Therefore, this paper introduces a new method for remaining tool life prediction in individualized production using automated machine learning (AutoML). The method enables the incorporation of machining expert knowledge via the model inputs and outputs. It automatically creates end-to-end ML pipelines based on optimized ensembles of regression and forecasting models. An explainability algorithm visualizes the relevance of the model inputs for the decision making. The method is analyzed and compared to a manual state-of-the-art approach for series production in a comprehensive evaluation using a new milling dataset. The dataset represents gradual tool wear under changing workpieces and process parameters. Our AutoML method outperforms the state-of-the-art approach and the evaluation indicates that a transfer of methods designed for series production to variable process conditions is not easily possible. Overall, the new method optimizes individualized production economically and in terms of resources. Machining experts with limited ML knowledge can leverage their domain knowledge to develop, validate and adapt tool life models.

Compact Wideband Groove Gap Waveguide Bandpass Filters Manufactured with 3D Printing and CNC Milling Techniques.

This paper presents for the first time a compact wideband bandpass filter in groove gap waveguide (GGW) technology. The structure is obtained by including metallic pins along the central part of the GGW bottom plate according to an n-order Chebyshev stepped impedance synthesis method. The bandpass response is achieved by combining the high-pass characteristic of the GGW and the low-pass behavior of the metallic pins, which act as impedance inverters. This simple structure together with the rigorous design technique allows for a reduction in the manufacturing complexity for the realization of high-performance filters. These capabilities are verified by designing a fifth-order GGW Chebyshev bandpass filter with a bandwidth BW = 3.7 GHz and return loss RL = 20 dB in the frequency range of the WR-75 standard, and by implementing it using computer numerical control (CNC) machining and three-dimensional (3D) printing techniques. Three prototypes have been manufactured: one using a computer numerical control (CNC) milling machine and two others by means of a stereolithography-based 3D printer and a photopolymer resin. One of the two resin-based prototypes has been metallized from a silver vacuum thermal evaporation deposition technique, while for the other a spray coating system has been used. The three prototypes have shown a good agreement between the measured and simulated S-parameters, with insertion losses better than IL = 1.2 dB. Reduced size and high-performance frequency responses with respect to other GGW bandpass filters were obtained. These wideband GGW filter prototypes could have a great potential for future emerging satellite communications systems.

Surface Quality Improvement for Ultrasonic-Assisted Inner Diameter Sawing with Six-Axis Force Sensors.

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model's force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.

[Research on Digital Manufacturing Technology of Key Components of Artificial Knee Joint].

The surface of the artificial knee joint manufactured by 3D printing technology is rough. This study uses advanced multi-axis machining technology to study how to improve the manufacturing accuracy of the artificial knee joint surface, and uses five-axis machining CAM software to analyze the process of three-dimensional model of the artificial knee joint and to compile tool path, create a five-axis special post-processor, convert the tool path point file into the NC program for machine tool processing. In order to ensure the safety and efficiency of the processing procedure, a Mikron HEM-500U machine tool simulation platform was built in the VERICUT simulation system for simulation processing verification, and then the blank prototype obtained by 3D printing was processed in a five-axis machine tool to meet medical clinical use requirements.