Errors In Manufacturing Process Research Articles

Inverse design of skull osteoinductive implants with multi-level pore structures through machine learning.

How to accurately design a personalized matching implant that can induce skull regeneration is the focus of current research. However, the design space for the porous structure of implants is extensive, and the mapping relationships between these structures and their mechanical and osteogenic properties are complex. At present, the forward design of skull implants mainly relies on expert experience, leading to high cost and a lengthy process, while the existing inverse design approaches face challenges due to data dependence and manufacturing process errors. This study presents an efficient inverse design method for personalized multilevel structures of skull implants using a machine learning pipeline composed of a finite element method, topological optimization, and neural networks. Based on the mechanical response of the human body falls, this method can tailor multi-level structures for implants in various defect positions. The results show that the proposed method establishes a bidirectional relationship between topological parameters and mechanical properties, enabling the customization of mechanical behavior at low computational cost while accounting for manufacturing errors in the 3D printing process. Additionally, the design results are also mutually consistent with analytical relationships between lattice parameters and the elastic modulus obtained from experiments and finite element simulations. Thus, this study provides a general and practical approach to rapidly design skull osteoinductive implants.

Analysis of Spring Damage in the Chamber Actuator

Spring, which has the function to help the valve operate so that it can be fully closed and fully open, has a very vital role in the continuous operation of the chemical industry. When a problem occurs, muddy fluid is found in the spring actuator chamber which causes the valve not to operate and not be in full close condition. The purpose of this study is to find the causes of the Spring Actuator failure in the chemical industry. This research was specifically directed at a broken Spring Actuator to determine the cause of the failure/damage. The tests carried out were: fractography, metallography (microstructure), chemical composition analysis, hardness test, and scanning electron microscope (SEM) energy dispersive x-ray (EDX) analysis. The results of the macrographic examination showed that the spring fracture had 2 fracture surface patterns. Spring B has a brittle fracture where the shape of the fracture is more even and does not show residual fracture. While springs A, C, and D experienced ductile fracture. The results of metallography found corrosion products in the surface area of the spring. There are non-metallic inclusions in the form of sulfides and microvoids. The microstructure is tempered martensite. From the results of the chemical composition of the spring material, it still meets the JIS G 4801 grade SUP 12 standard with the addition of Silicon and Chrome. The hardness test results have a hardness value of 518.4 HV. The spring hardness value is above the maximum hardness limit of SUP 12 material. The SEM test results of the spring sample show the presence of non-metallic inclusions with elongated morphology and micro-sized voids. The results of the EDX test showed a Sulfur content of 4.71 wt% in non-metallic inclusions and the content of Fe and O elements in the defective holes which were corrosion products. From this study, it is known that the factors causing damage to the springs are manufacturing process errors, material defects, and corrosive environments.

A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

AbstractThe vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults.

Uncertainty analysis of photovoltaic cells to determine probability of functional failure

In engineering practice, extensive uncertainties exist in photovoltaic (PV) cell material parameters owing to manufacturing process errors and in PV cell working environment parameters owing to environmental uncertainties. Ignoring the uncertainty of these parameters will greatly reduce the reliability of photovoltaic cells. In this study, a PV cell model was used to conduct an uncertainty analysis based on functional failure. Functional failure is defined as output power fluctuation beyond the specified range, and functional safety region is defined as the allowable fluctuation range of output power during operation. A global sensitivity analysis method based on the Monte Carlo method was employed to study the influence of parameter fluctuation on the functional failure probability of a PV cell. An uncertainty analysis method based on Latin hypercube sampling was applied to study the influence of parameters on the probability of PV cell operation in the functional safety domain. The results indicate that the uncertainty of surface temperature had the most significant influence on the failure probability, followed by the ideality factor, radiation intensity, and current temperature coefficient, the series and parallel resistances had the smallest impact. Optimal values of 510.200 W/m2, 284.600 K, and 1.446 were obtained for radiation intensity, surface temperature, and ideality factor, respectively, which ensured that system functional failure probability remained in the safety zone with values of 93.763 %, 99.211 %, and 98.856 %, respectively. Additionally, the lower series resistance and the higher parallel resistance, the more likely the system to operate in the functional safety domain. The results of study expand the evaluation standard for PV cell operation and ensure the desired system output power.

Analytic Solution for Nonlinear Impact-Angle Guidance Law with Time-Varying Thrust

This paper presents an impact-angle guidance law of unmanned aerial vehicles (UAVs) with time-varying thrust in a boosting phase. Most current research on impact-angle guidance law assumes that UAV speed is constant in terms of controlled thrust. However, the UAV speed and the acceleration in a boosting phase keep changing because of time-varying thrust. Environmental factors and manufacturing process error may prohibit accurately predicting vehicle-thrust profiles. We propose a nonlinear impact-angle guidance law by analytically solving second-order error dynamics with nonlinear time-varying coefficients. The proposed analytic solution enables one to update guidance gains according to initial and current states so that desired impact angle is met while the miss-distance error is reduced. We prove the finite-time error convergence of the proposed guidance law with the Lyapunov stability theory. Various simulation studies are performed to verify the proposed guidance law.

Prediction and measurement of acoustic transmission loss of acoustic window with composite sandwich structure

Underwater acoustic detection sensors are mounted on the outside of the submarine; the acoustic window for protecting these sensors must be structurally robust while also minimizing any deterioration of sensor's sound detection performance. These two conditions are typically satisfied simultaneously by using composite materials with acoustic window materials. However, since such composite material is manufactured by laminating fibers, there is the probability that delamination occurs, in which an air layer is formed inside, due to manufacturing process errors. Delamination inside the acoustic window degrades the sensor's acoustic performance and results in a failure of military operations. In the case of composites composed of sandwich structures located in the central part, the possibility of internal delamination is higher than in a single composite material. Therefore, it is very important to discriminate the presence or absence of internal delamination after producing an acoustic window. This article uses numerical and analytical methods to determine the internal delamination of the acoustic window fabricated with a sandwich structure. In addition, the results were analyzed and compared through ultrasonic measurement and acoustic transmission loss test.

Design and simulation of fourth order low-pass Gm-C filter with novel auto-tuning circuit in 90 nm CMOS

A tunable high-frequency operational transconductance amplifier (OTA) is presented along with its application in the implementation of a Gm-C filter. The OTA is tuned by varying the negative resistance produced by a positive feedback at the output. Post-layout simulation results (using TSMC 90 nm CMOS technology and a 1-V supply voltage) show that the differential DC gain, common-mode gain and OTA unity gain frequency are 34 dB, −26 dB and 10 GHz, respectively. Moreover, for precise control of filter performance, an auto-tuning circuit is presented to adjust the filter cutoff frequency at low power consumption (i.e., 0.6 mW, about 16.3% of the total circuit power consumption). The filter has a cutoff frequency of 1 GHz with a group delay variation less than 6% up to 1.3 fc. The size of filter is 0.040 × 0.023mm2 and the third order intermodulation (IM3) value at cutoff frequency is −37 dB. The Monte Carlo simulation results are presented for predicting the manufacturing process errors.

Statistical analysis of wear of biplanar deterministically-arrayed surfaces for load bearing applications

The use of microscale deterministic surface features has been investigated in many bearing and seal applications in recent research. This paper considers a class of surface textures that are comprised of periodic arrays of deterministic geometric shapes created using photolithography-based manufacturing processes. These features are unique in that they can be modeled as biplanar: the topography of the entire surface ideally lies on one of two parallel horizontal planes. A total of eight such surface textures are analyzed: three comprised of electroplated nickel cavities and five comprised of metal-impregnated polymer asperities. It is shown that for this class of surface textures, closed form solutions for perfectly-manufactured surfaces exist for eleven areal surface parameters—including amplitude (Sa, Sq, St and Sz), spatial (Ssk, Sku and Std) and functional (Sbi, Sci and Svi)—and these are derived and presented for each case. These surface parameters are then used to evaluate manufacturing process errors for the surface textures and to quantify and evaluate the impact of wear on the performance of such surface textures when placed in a tribological application, in this case a radial lip seal. In particular it was found that useful correlations exist between the surface parameters of Sq and Svi and the operating torque in the lip seal application.

Defect Detection in Optical Disk Substrate by Mach-Zehnder Interferometer

In recent years optical disks have been very popular and in manufacturing process error detection in optical disks becomes very important, especially for the defects whose size of a few millimeters in length and several hundred nanometers in depth. The authors have developed a new system to measure the defects of disk substrates using a Mach-Zehnder interferometer with a phase shifting method. But it was found that optical disks had undulation of the optical thickness and it disturbed the detection of defects. To solve the problem moving-average method was developed and introduced in data processing to distinguish the defect from the undulation, and with modified unwrapping program the defect was visualized by binary image processing.

A Method for Integrating Form Errors Into Geometric Tolerance Analysis

In this research, we describe a computer-aided approach to geometric tolerance analysis for assemblies and mechanisms. A series of as-manufactured component models are generated within a NURBS-based solid modeling environment. These models reflect errors in component geometry that are characteristic of the manufacturing processes used to produce the components. The effects of different manufacturing process errors on product function are tested by simulating the assembly of imperfect-form component models and by measuring geometric attributes of the assembly that correspond to product functionality. A tolerance analysis model is constructed by generating and testing component variants that represent different manufacturing precision levels. The application of this approach to tolerance analysis is demonstrated using a case study that is based on a high-speed stapling mechanism.

Intelligent Error Compensation in CNC Machining through Synergistic Interactions Among Modeling, Sensing and Learning

Industry is looking for practical means to improve the accuracy of the parts machined on CNC machines. Some artificial intelligence (AI) systems have been applied in modeling and compensating manufacturing process errors in CNC machining. However, these systems are not capable of predicting the results of a new operation if no sufficient data on a number of similar operations is available. A generalized AI approach named synergistic interactions amongst modeling, sensing and learning is proposed in this paper. Based on the AI approach, a new strategy of error compensation of workpiece dimension in CNC machining is developed and applied in a CNC turning center. Error compensation results are illustrated the effectiveness of the error compensation strategy. The learning curve shows that the error compensation confidence gradually progresses towards 100% confidence from zero along with the CNC machine operation time increasing.

Manufacturing Process Error in Heated Wire Breathing Circuits Exposes Metallic Conductor to Environment Within Circuit

Manufacturing Process Error in Heated Wire Breathing Circuits Exposes Metallic Conductor to Environment Within Circuit