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Considering the intricate structure, challenging maintenance requirements, and the interdependent nature of the detection parameters within a wind turbine gearbox, this study employs a stacked auto-encoder model for the offline analysis and modeling of standard operational data from the gearbox. The deviation in health factors post-model reconstruction serves as a metric for monitoring the gearbox’s operational status, with the unit’s health score being derived from an enhanced encoder architecture.

The anti-foaming protection is designed to prevent the oil from overheating due to intense oil churning and to reduce torque losses. The numerical simulation will be performed on gearbox housing in the SolidWorks Flow module in two variants: without anti-foaming protection and with anti-foaming protection, both for 3 values of the speed: 1000, 1200, 1400 rpm and 3 values of the oil height: 70, 100, 126 mm. The aim of the paper is to quantify by simulation the torque losses in the gearbox housing for different values of speed and oil level.

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

The present study shows that Chromium molybdenum steel 415 (CMB415) has historically been used for the production of gears by the leading international manufacturers due to its exceptional hardness and superior mechanical properties after carburization. However, in order to meet the demand for the reduction gearbox components, the limited availability of Chromium Molybdenum Steel 415 in India necessitated the need to explore alternative materials. Carbon 45 (C-45), steel 410 (SS410) and emergency number 19 (EN-19) are often used as alternatives which offer similar mechanical properties in India. As these materials are readily available on the Indian market, manufacturers experiencing shortages of SCM415 can consider them as a viable option. These materials shall be subjected to a series of tests, such as tensile, hardness and microstructure tests, before and after two different thermal treatment procedures to assess their suitability for use in the process. The EN 19, C45 and SS 410 were found to have different mechanical and microstructural properties as viable alternatives to SCM 415. C45, significantly higher than EN-19 and SS410, achieves a hardness of 61 HRC after induction-firing.

This paper provides a novel application of a multi-criterion decision-making (MCDM) method to the multi-objective optimization problem of designing a two-stage helical gearbox. This study’s goal is to identify the ideal primary design elements that increase gearbox efficiency while reducing the gearbox cross-section area. In this work, three primary design parameters were selected for investigation: the gear ratio of the first stage and the coefficients of wheel face width (CWFW) of the first and second stages. The multi-objective optimization problem was further split into two phases: phase 1 solved the single-objective optimization problem of minimizing the gap between the variable levels, and phase 2 solved the multi-objective optimization issue of identifying the ideal key design factors. Moreover, the multi-objective optimization problem was handled by the SAW method as an MCDM approach, and the weight criteria were computed using the entropy approach. This study’s significant characteristics are as follows: First, a multi-objective optimization problem was successfully solved using the MCDM approach (SAW technique) for the first time. Second, the power losses in idle motion were investigated in this work in order to determine the efficiency of a two-stage helical gearbox. From this study’s findings, the ideal values for three major design parameters can be determined for the design of a two-stage helical gearbox.

With the rapid development of smart manufacturing, some challenges are emerging in the production management, including the utilization of information technology and the elimination of dynamic disturbance. A digital twin-driven production management system (DTPMS) can dynamically simulate and optimize production processes in manufacturing and achieve real-time synchronization, high fidelity, and real-virtual fusion in cyber-physical production. This paper focuses on establishing DTPMS for production life-cycle management. First, we illustrate how to integrate digital twin technology and simulation platforms. Second, a framework of DTPMS is proposed to support a cyber-physical system of production workshop, including product design, product manufacturing, and intelligent service management. Finally, the proposed DTPMS is applied to the production process of a heavy-duty vehicle gearbox. The experimental results indicate that the defective rate of products and the in-process inventory are reduced by 34% and 89%, respectively, while the one-time pass rate of product inspection is increased by 14.2%, which demonstrates the feasibility and effectiveness of the DTPMS.

In order to address the Multi-Objective Optimization Problem (MOOP) in building a two-stage helical gearbox, this work presents a novel application of the Multi-Criterion Decision-Making (MCDM) method. The aim of the study is to determine the optimal primary design factors that will increase gearbox efficiency while decreasing gearbox volume. Three main design parameters were chosen for assessment in this work: the first stage’s gear ratio, and the first and second stages’ Coefficients of Wheel Face Width (CWFW). In addition, the MOOP is divided into two phases: phase 1 solves the single-objective optimization problem to reduce the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the Entropy approach was picked to compute the weight criteria, and the MARCOS method was chosen as an MCDM method to handle the multi-objective optimization issue. The following are important characteristics of the study: Firstly, the MCDM method (MARCOS technique) was successfully applied to solve a MOOP for the first time. Secondly, this work has looked into power losses during idle motion to calculate the efficiency of a two-stage helical gearbox. The results of the study were used in the design of a two-stage helical gearbox in order to identify the optimal values for three important design parameters.

Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. An illustration of a digital twin city

Efficiency is a concept that evaluates the optimal utilization of resources, including time, energy, finances, or materials, in order to accomplish a particular goal or objective. As widely acknowledged, energy losses occur in systems involving relative motion between interacting machine elements due to friction. In the case of a gearbox, these losses can arise from tooth friction in the gear mechanism, friction in sealing elements, friction in roller bearings, and the influence of the lubricant used in the system, all of which are subject to environmental conditions. This study aims to experimentally determine the efficiency of the gearbox under various operating conditions by considering the gearbox as a comprehensive system encompassing all its components. A measurement system was designed in order to obtain the efficiency of a gearbox. Experiments and measurements were carried out via software support. The measurement system contains two torque transducers, electrical resistive load device, an electrical motor with temperature measurement thermocouple, and two stage helical gearbox. In experiments conducted through computer commands, input revolutions were incrementally increased with 400 rpm intervals within the range of 700–2700 rpm. Moreover, experiments were carried out at different lubricant levels in the gearbox. At the same time lubricant temperature was measured and effects to the gearbox efficiency were investigated. Subsequently, different lubricant with distinct viscosity indices were employed. As a result of this experimental design, regime efficiency values were obtained for each case. Thus, power loss of the gearbox system has been determined. These results were examined using a general full factorial design. Analysis of variance (ANOVA) tables were created and the effects of the parameters on the system and the efficiency results were determined by checking whether the parameters were interacting or not. Finally, regression analysis was performed and the regression function was obtained in order to develop a predictive model to estimate the efficiency of a gearbox.

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications.