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In the presents research an experimental investigation was conducted on the resistance seam welding of thin sheets of thickness 0.2 mm for packaging application. The most difficult issues in such sectors are reducing product weight without compromising weld strength. The machines used in such industries are of certain specifications. Major modifications in the machine are very costly. Thus, the industrial need is to regulate the machine in the given specification for better productivity and high quality of the product. Twenty sets of experiments were conducted on the industrial grade resistance seam welding machine in order to investigate the versatility of the machine and possible effect on the mechanical and microstructural properties of the resistance seam welded joint. The experiments were conducted in two parts. In the first part only current was varied (i.e. single factor experiment) keeping other parameters such as electrode wheel speed and welding voltage unchanged. In the second set of experiments, both current and electrode wheel speed were changed keeping voltage constant. Mechanical properties such as tensile strength, yield strength, breaking load, elongation at breaking were studied as the function of current and wheel rotational speed. The yield strength firstly increases up to 84 A and then decreases with increase in the welding current keeping other parameters fixed. The elongation at break also decreases with increasing current value. Microstructural properties were also analysed for the proper understanding of the process and potential effect on the various mechanical properties. [ABSTRACT FROM AUTHOR]

An energy-saving control strategy for wheel loaders is proposed in this paper to address the issue of high energy consumption during their operation. The strategy is based on the intelligent identification of working stages, allowing for staged power matching and resulting in reduced energy consumption. Each work stage of the loader is identified by matching it to the main pump pressure waveform and actuator pilot pressure waveform. Using a sliding time window method, pressure waveforms from each working stage are subjected to feature extraction. A bidirectional long short-term memory neural network (BILSTM) algorithm is then used to establish an intelligent recognition model. Based on work stage identification, an energy-saving control strategy based on power matching is proposed for the shoveling stage of the loader, and the Grey Wolf optimization (GWO)-PID algorithm is utilized for control parameter tuning. Finally, the effectiveness of the energy-saving control strategy based on work stage identification is verified through experiments. The research results indicate that the BILSTM recognition model outperforms other models with a recognition accuracy of 96.1%. The optimal time window width is 0.6 s, and the proposed energy-saving control strategy achieves a fuel-saving rate of 6.81%. This method provides feasibility for reducing energy consumption in construction machinery and achieving energy-saving and carbon-reduction goals. [ABSTRACT FROM AUTHOR]

Customer churn remains a critical concern for businesses, highlighting the significance of retaining existing customers over acquiring new ones. Effective prediction of potential churners aids in devising robust retention policies and efficient customer management strategies. This study dives into the realm of machine learning algorithms for predictive analysis in churn prediction, addressing the inherent challenge posed by diverse and imbalanced customer churn data distributions. This paper introduces a novel approach—the Ratio-based data balancing technique, which addresses data skewness as a pre-processing step, ensuring improved accuracy in predictive modelling. This study fills gaps in existing literature by highlighting the effectiveness of ensemble algorithms and the critical role of data balancing techniques in optimizing churn prediction models. While our research contributes a novel approach, there remain avenues for further exploration. This work evaluates several machine learning algorithms—Perceptron, Multi-Layer Perceptron, Naive Bayes, Logistic Regression, K-Nearest Neighbour, Decision Tree, alongside Ensemble techniques such as Gradient Boosting and Extreme Gradient Boosting (XGBoost)—on balanced datasets achieved through our proposed Ratio-based data balancing technique and the commonly used Data Resampling. Results reveal that our proposed Ratio-based data balancing technique notably outperforms traditional Over-Sampling and Under-Sampling methods in churn prediction accuracy. Additionally, using combined algorithms like Gradient Boosting and XGBoost showed better results than using single methods. Our study looked at different aspects like Accuracy, Precision, Recall, and F-Score, finding that these combined methods are better for predicting customer churn. Specifically, when we used a 75:25 ratio with the XGBoost method, we got the most promising results for our analysis which are presented in this work. [ABSTRACT FROM AUTHOR]

Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the telecommunications business. Accurate churn forecasting is essential for successful client retention initiatives to combat regular customer churn. We used innovative and improved machine learning methods, including Decision Trees, Boosted Trees, and Random Forests, to enhance model interpretability and prediction accuracy. The models were trained and evaluated systematically by using a large dataset. The Random Forest model performed best, with 91.66% predictive accuracy, 82.2% precision, and 81.8% recall. Our results highlight how well the model can identify possible churners with the help of explainable AI (XAI) techniques, allowing for focused and timely intervention strategies. To improve the transparency of the decisions made by the classifier, this study also employs explainable artificial intelligence methods such as LIME and SHAP to illustrate the results of the customer churn prediction model. Our results demonstrate how crucial it is for customer relationship managers to implement strong analytical tools to reduce attrition and promote long-term economic viability in fiercely competitive marketplaces. This study indicates that ensemble learning models have strategic implications for improving consumer loyalty and organizational profitability in addition to confirming their performance. [ABSTRACT FROM AUTHOR]

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design's performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration. [ABSTRACT FROM AUTHOR]

Soft robots often draw inspiration from nature to navigate different environments. Although the inching motion and crawling motion of caterpillars have been widely studied in the design of soft robots, the steering motion with local bending control remains challenging. To address this challenge, we explore modular origami units which constitute building blocks for mimicking the segmented caterpillar body. Based on this concept, we report a modular soft Kresling origami crawling robot enabled by electrothermal actuation. A compact and lightweight Kresling structure is designed, fabricated, and characterized with integrated thermal bimorph actuators consisting of liquid crystal elastomer and polyimide layers. With the modular design and reprogrammable actuation, a multiunit caterpillar-inspired soft robot composed of both active units and passive units is developed for bidirectional locomotion and steering locomotion with precise curvature control. We demonstrate the modular design of the Kresling origami robot with an active robotic module picking up cargo and assembling with another robotic module to achieve a steering function. The concept of modular soft robots can provide insight into future soft robots that can grow, repair, and enhance functionality. [ABSTRACT FROM AUTHOR]

Modular hobs are tools with very complex geometry. Regardless of the material of the gear wheels, they determine the accuracy of the gears made in the hobbing machining process. Gears are made of various, often innovative materials depending on the requirements. Sometimes, the materials are characterised by very high hardness (over 65 HRC). The mathematical basis for describing the faces of a hob presented in the article allows for modifying the rack profile shaping the gear wheel's teeth. The model's universality makes it possible to perform numerical simulations of the influence of individual parameters of the hob creation process (geometry of the grinding wheels and their setting in the shaping process) on the profile of the rake and flank surfaces. The cutting edge (rack edge) is the locus of points belonging to both of these surfaces and thus directly impacts the accuracy of the gear wheel that is shaped in the hobbing process. The article summarises the authors' long-term cooperation with the industry, resulting in a series of articles devoted to hobs. The issues presented in the article are significant to the machinery industry and hob manufacturers. [ABSTRACT FROM AUTHOR]

Clamping precision as a key factor of precision injection molding of high-quality plastic parts is restricted by low-precision clamping systems. Herein, a novel clamping method capable of sensing mold misalignment and self-adjusting mold position and attitude through floating structures is proposed to eliminate the restriction of clamping system, thereby achieving high clamping precision. The innovative clamping method possesses prestress steel wire-winding frames and rubber capsules filled with hydraulic oil, which ensures the floating structures work. Meanwhile, the hydraulic oil provides isostatic pressure to clamp the mold. The working space of the floating structures and the loading of the prestress steel wire-winding frames have been modeled to confirm their feasibility in the self-adaptive clamping method. Based on the modeling activities, a prototype injection molding machine was developed to evaluate the clamping precision. The experimental results indicate that the parallelism between the mold plates is 0.031 mm, far superior to that in China Machinery Industry Standards, and the uneven clamping force rate on mold cavity surface is 1.3 %. Moreover, the clamping force repeatability is only 0.14 %, superior to toggle and two-platen clamping systems. This method offers a new chance for the development of precision injection molding. • A novel clamping method capable of sensing mold misalignment and self-adjusting mold position and attitude. • The clamping method utilizes capsule with hydraulic oil to provide isostatic pressure and ensure floating structures work. • The prestress steel wire-winding frames in the clamping system cannot restrict degree of freedom of floating structures. • The developed clamping system is verified that the clamping precision is superior to traditional clamping systems. [ABSTRACT FROM AUTHOR]

This study aimed to determine the risk level of noise, which is an important physical risk, in small and medium-sized furniture industry enterprises. The noise levels of the circular sawing machines, edge banding machines, and mitre cutting machines, which are among the main processing machines of the sector, were measured. The study was carried out in 32 furniture businesses. The possible risks of noise on the operators of the machines in question and other employees were evaluated. Noise level measurements were made with the help of TESTO 815 measuring device. Dunnett's T3 test was used to detect differences in noise levels for machine operators and other employees. It was determined that the edge banding machine does not pose an occupational health and safety risk in terms of noise risk factors. However, the mitre cutting machine and the circular sawing machine pose a risk for the machine operator in active production by creating noise above the established exposure limit value. The mitre cutting machine carries the same risk for the machine operator when it is in operation but in passive production. The results revealed the need for personal protective equipment for machine operators for mitre cutting and circular sawing machine. [ABSTRACT FROM AUTHOR]

Purpose: This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks. Design/methodology/approach: The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation. Findings: The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items' criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level. Originality/value: This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to prevent occupational accidents occurring in the manufacturing industry by means of touch sensors. When the occupational accidents occurring in the manufacturing industry around the world are examined, it is seen that approximately 88% of occupational accidents occur from "dangerous movement" and 10% from "dangerous situation." Although some studies related to safety culture studies, safety studies in design and collective or personal protective measures have been started, they have not been brought to an adequate level. It is observed that studies on dangerous movements continue even in many developed countries. In this study, first of all, a literature study was conducted. Occupational accidents experienced in the manufacturing sector in Turkey have been examined. In line with these investigations, a prototype circuit protection system has been developed that can prevent accidents caused by dangerous movement. With the circuit, its applicability and effectiveness were measured by conducting experiments on different manufacturing machines. The prototype circuit applied in this paper was made based on the logic of protective measures made on sawstop machines used in different sectors. In the experimental study conducted, it was observed that in 30 experiments conducted with a prototype on ten separate manufacturing machines, it stopped the machines 26 times at minimum and 29 times at maximum. On average, when looking at the system efficiency values, it was seen that the system was 81.6% effective, and it was observed that positive results could be obtained when converted into a real product. Design/methodology/approach: In this study, their contribution to the prevention of work accidents caused by presses and rotary accents from machines used in the manufacturing industry by means of touch sensors used in Industry 4.0 was examined. Findings: With Industry 4.0, different automation systems began to be switched in many areas and sectors. Studies have started on different sensors used also in Industry 4.0 in occupational health and safety studies, but it is seen that they have not been applied at an adequate level. It should be designed in such a way as to prevent errors or stop these errors in the studies performed. Today, sensors are produced at much lower costs than before. In addition, the constantly developing technology provides great convenience for these applications. Research limitations/implications: This study was applied for press and cylinder machines from manufacturing machines. This study has been tried for machines producing a maximum pressure of 300 tons. Originality/value: A prototype was designed. Trials were done on some machines by prototype. There could be improve and find different solutions for safety problems in the industry with this perspective. [ABSTRACT FROM AUTHOR]

The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection. [ABSTRACT FROM AUTHOR]

In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified. [ABSTRACT FROM AUTHOR]

In this paper, the authors present an extension of the scope of the previously conducted research to the full three-dimensional computer simulation (using the finite element method), which takes into account the interaction between: heavy caterpillar tracks system - working platform - weak subsoil. The article presents a computer model considering two caterpillars, resting on elastic-plastic sub-soil, with standard Mohr-Coulomb yield conditions, allowing for computer simulation of the behavior of the system up to achievement of ultimate limit state. The results of the above model are treated as the reference for a simplified Analytical Models of estimating the limit state, which might be used in design procedures. In turn, these Analytical Models are enhancements of previously presented one. The most important results concluding form the Analytical Model are simple interaction formulas, in the space of moments acting on the machine-subsoil system, limiting a domain of safety in given soil conditions. [ABSTRACT FROM AUTHOR]

This paper discusses how to improve the machining precision in the turning of slender shaft. The main cause of dimensional error in slender shaft machining is analyzed by establishing dimensional error model and using genetic algorithm to optimize cutting parameter selection. Based on this, the proportional-integral-differential control error compensation is proposed to reduce the error in the turning process of slender shaft. Through the simulation experiment, the machining size error of slender shaft under different cutting parameters is obtained. It is found that the increase of back blowing and feed rate will aggravate the dimensional error, while the increase of CS will reduce the dimensional error. The error after the proportional-integraldifferential control error compensation is much smaller than that without the error compensation. The experimental results show that the method is reliable in reducing the errors in the turning of slender shaft, and can realize the machining mode with higher precision and efficiency. This is of great significance to the development of machinery manufacturing industry. [ABSTRACT FROM AUTHOR]

Recently, the clean air support system (CAPSS) pronounced that construction machinery is the second-largest source of pollutants in Non-road mobile machinery (NRMM) after ship. Three types of vehicles, forklifts, excavators, and loaders, account for 80 % of pollutants emitted by all construction machinery. In addition, domestic construction emission regulation has been reinforced by adopting the EU Stage V regulations. Therefore, it is necessary to analyze the domestic and overseas construction machinery regulations and perform emission test under real operation condition. In this study, three construction machineries (1 unit each of forklift, excavator, and loader) were selected as test vehicles. The emissions were measured using a portable emissions measurement system (PEMS) installed on the test vehicles. The real operation test was conducted in accordance with EU Stage V ISM policy emission measurement procedure. The real operation test cycles were developed for each vehicle type to meet the minimum test duration requirement. Additionally, the test data were evaluated to distinguish valid working events and analyze emission characteristics using the moving average window (MAW) analysis method. [ABSTRACT FROM AUTHOR]

The paper evaluates the possibility of using dissimilar materials joined by welding technology in the construction of agricultural machinery. The desire to design larger and more efficient structures requires designers to combine materials with different mechanical and structural properties. In such a case, it is very important to properly select welding parameters so that, on the one hand, the quality of the joint meets the standard requirements, and on the other, the welding process is not too energy-intensive. In this paper, overlay joints connecting S355 steel with Strenx 700 steel were analyzed in terms of strength for three different values of welding parameters and different thicknesses. The starting point was the reference parameters recommended by the company's welding technologists, which were reduced by 10 and 20% according to the linear welding energy. The study compared the strength, ductility and macrostructure of the joints, as well as the energy intensity of the process. The proposed dissimilar joints achieved approximately a 10% increase in the strength limit of the components in comparison to the previously recommended welding parameters. Additionally, finite element analysis calculations of the improved designs showed significant weight reduction (up to 40%) for the relevant agricultural machinery components. [ABSTRACT FROM AUTHOR]

Driverless technology refers to the technology that vehicles use to drive independently with the help of driverless system under the condition of unmanned intervention. The working environment of construction machinery is bad, and the working conditions are complex. The use of driverless technology can greatly reduce the risk of driver operation, reduce labor costs and improve economic benefits.Aiming at the problem of the GPS positioning signal in the working environment of construction machinery being weak and not able to achieve accurate positioning, this paper uses the fusion SLAM algorithm based on improved NDT to realize the real-time positioning of the whole vehicle through reconstruction of the scene. Considering that the motion characteristics of crawler construction machinery are different from those of ordinary passenger cars, this paper improves the existing pure pursuit algorithm. Simulations and real vehicle tests show that the algorithm combined with the fusion SLAM algorithm can realize the motion control of driverless crawler construction machinery well, complete the tracking of the set trajectory perfectly and have high robustness. Considering that there is no mature walking method of driverless crawler construction machinery for reference, the research of this paper will lay a foundation for the development of driverless crawler construction machinery. [ABSTRACT FROM AUTHOR]

In this study, the surfaces of AISI 1050 Medium Carbon Steel and D2 Cold Work Tool Steel, which are commonly used in the machine manufacturing industry, were processed using the burnishing method. In the burnishing experiments, cylindrical materials with dimensions of Ø50x30mm and a Ø20mm hole drilled in the center were used. For the turning process, the cutting speed was selected as 60 m/min and the feed rate as 0.25 mm/rev. The burnishing process was applied in a single pass. The properties such as microhardness, surface morphology, circularity, cylindricality and surface roughness of the materials by the burnishing process were examined in detail. After burnishing, the hardness of AISI 1050 and D2 cold work tool steel materials was measured to be 292 HV and 302 HV, respectively. The results obtained showed that the burnishing process increased the hardness value of AISI 1050 steel proportionally more than the hardness value of D2 steel. It was determined that the circularity and cylindrical properties of the hole surfaces were improved. The obtained results show that the burnishing process will increase the efficiency of the machine elements working in contact and the damage that may occur during the operation of these machine elements can be minimized by burnishing process. [ABSTRACT FROM AUTHOR]

This article examines the trade relationship between Mexico and South Korea through the lens of trade in value-added perspective. Using the global input–output model, we dissect gross exports into their value-added components to analyze the sectors benefiting most from value-added generation in both countries. To do this, we use the multiregional input–output matrices developed by the Asian Development Bank (ADB). The results show a growing bilateral relationship between 2000 and 2021, with South Korea having a stronger position by generating a larger share of value-added linked to the bilateral relationship. Nevertheless, the gap has narrowed over time. Notably, South Korean exports to Mexico have substantially contributed to value-added generation in technology-based manufacturing sectors such as machinery, equipment, electronics, electrical, and optical products. Similarly, Mexico's mining sector increasingly relies on exports to South Korea for value-added generation. The trends observed in this study's findings persist even amidst the reconfiguration of Global Value Chains, indicating that a free trade agreement between Mexico and South Korea has the potential to further enhance value-added generation. [ABSTRACT FROM AUTHOR]

It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines. [ABSTRACT FROM AUTHOR]

There is a permanent and strong need for energy recovery to improve the efficiency of the hydraulic system in the field of the construction machinery. In addition, the digital pump will become powerful and versatile by employing different configurations and intelligent control of the flow distribution valves. Considering this case, we have proposed a novel digital pump in which every plunger is equipped with two flow distribution valves. By controlling these two valves, external hydraulic energy can be directly reused without other components. Based on the structure and working principle of the digital pump, the mathematical model is established and three working modes are detailed. To verify the feasibility and correctness of control methods, a performance simulation testing platform including a digital pump, load module, hydraulic energy to be recovered, and controller module was developed in AMESim R15 software. The pressure, flow rate, and torque simulations of the digital pump in three working modes were carried out. The simulation results have shown that the digital pump not only can be used as an ordinary pump but also has the function of recovery and immediate reutilization of another hydraulic energy. Meanwhile, the corresponding variable displacement control strategy is effective and the positive torque required to drive the digital pump can be reduced, which verified the energy-saving of this scheme. The ideas and contents in this paper can offer significant references for energy conservation technology of various engineering machineries and the intensive study of digital hydraulics. [ABSTRACT FROM AUTHOR]

One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations. [ABSTRACT FROM AUTHOR]

Summary: With ever growing urbanization, the environmental noise is becoming hazardous. Vehicular traffic, locomotives, heavy machinery in industry, and construction processes are the major sources of noise pollution. It has adverse effects on the health of humans as well as that of the wild life. World Health Organization (WHO) puts noise pollution as the second major cause of illness due to environmental reasons. The effects of noise pollution on the quality of life are usually ignored. Due to this reason it is common, even in the first world countries, to have the WHO's peak noise standards violated in residential areas. Therefore, there is a need to have a real time, portable and easy to replicate, mechanism to monitor the noise sources. In this work, we propose a novel architecture of a deep neural network to classify a 10‐class environmental noise data called URBANSOUND8K. This network is comprised of three components, (1) one dimensional two level Discrete Wavelet Transform (DWT) component, (2) branched component for feature extraction through auto‐encoders, and (3) LSTM and fully‐connected layers based classification component. With all components combined, we call this network DWTNet. By embedding the DWT component as a part of network, we eliminate the need of prior data conversion into spectral and/or spectro‐temporal domains. The efficiency of DWTNet is comparable to the state of the art networks with significantly lower number of trainable parameters. We analyze the contribution of classification accuracy. We further study some of the classification results individually and show that some of the mis‐classifications are actually multi‐class classifications with distributed decision confidence. [ABSTRACT FROM AUTHOR]

A seal is a mechanism or a piece of material that securely shuts a hole so that air, liquid, or other substances cannot enter or exit the system. Seals are an essential component of practically all machinery and engines and have several applications in industry. The development of novel materials for sealing applications is essentially required on these days. In this research, an attempt is made to find the polymer material for the said application. Poly vinyl rubber material has been taken, and the specimens are prepared for testing the tensile properties and hardness. The specimens were prepared by using die with various temperatures and curing time. Sixteen specimens were prepared by changing the curing temperature, curing time, postcuring temperature, and postcuring time. The curing temperature 150°C and 170°C, postcuring temperature 100°C and 50°C, curing time 14 mins and 18 mins, postcuring time 120 mins and 60 mins, and the pressure of 150 kg/cm2 for all the specimens were maintained. The tensile strength and hardness analysis were done as per the ASTM standard, and it was found that the specimen prepared on 150°C curing temperature, 18 min curing time, 50°C postcuring temperature, and 120 min postcuring time provides the higher tensile strength. DOE analysis is also done to determine the best values of the factors impacting the mechanical characteristics of the seal material. Simple regression analysis is used to find the influence of curing temperature and curing time on the tensile strength and hardness for every 1°C temperature rise and 1 sec curing time. [ABSTRACT FROM AUTHOR]

The quick and accurate characterization of commercial electrochemical double-layer capacitor (EDLC) cells, especially their capacitance and direct-current equivalent series internal resistance (DCESR), is of great significance for the design, maintenance, and monitoring of EDLCs used in areas of energy, sensors, electric power, construction machinery, rail transit, automobile transportation, and military. In this study, the capacitance and DCESR of three commercial EDLC cells with similar performance were determined and compared by following the three commonly-used standards of IEC 62391, Maxwell, and QC/T741-2014, which are significantly different in test procedures and calculation methods. The analysis of the test procedures and results demonstrated that the IEC 62391 standard has the disadvantages of a large testing current, long testing time, and a complex and inaccurate DCESR calculation, whereas the Maxwell standard has the disadvantages of a large testing current, a small capacitance, and large DCESR testing results, and furthermore the QC/T 741 standard has the disadvantages of a high resolution requirement for the equipment and small DCESR results. Therefore, an improved method was proposed to determine the capacitance and DCESR of EDLC cells by short-time constant voltage charging and discharging interruption methods, respectively, with the advantages of high accuracy, low equipment requirements, short testing time, and the easy calculation of DCESR over the original three standards. [ABSTRACT FROM AUTHOR]

Spindle speed optimization is important for the machining industry to improve the material removal rate (MRR). However, low spindle speeds are always used to avoid inaccuracy caused by the chatter and forced vibration. Especially for the five-axis flank milling, the selection of spindle speeds is conservative since the complexity of the tool-workpiece engagement (TWE) and the corresponding dynamics. This paper presents a spindle speed optimization method to improve MRR with the consideration of chatter and forced vibration for five-axis flank milling. Chatter and forced vibration models of five-axis flank milling are developed based on the general cutting dynamics model. The root-mean-square (RMS) values of the forced vibration displacement are employed to evaluate the surface quality. The relationship between the stable spindle speed and the forced vibration reveals that the surface quality is positively associated with the spindle speed in some specific ranges, and MRR is improved by increasing the spindle speed in those ranges without the risk of increasing inaccuracy. The simulation results and experimental work show that the proposed method can be used to optimize spindle speeds for improving MRR without inducing chatter and aggressive forced vibration. [ABSTRACT FROM AUTHOR]

Industrial high-speed rotating machines entail constant and consistent monitoring to prevent downtime, affecting quantity and quality. Complex machines need advanced intelligent fault diagnosis showing minimal errors. This work offers a MATLAB-based fault diagnosis for sugar industry machines. The vibration behavior of physical industrial machines is obtained, and the signals are provided to a MATLAB program to identify the fault. The information helps to suggest remedies to include in the maintenance schedule. The ease and comprehensible nature of the method reduce time and enhance the reliability of condition monitoring for industrial machines. [ABSTRACT FROM AUTHOR]

A double-coil inductive displacement transducer is a non-contact element for measuring displacement and is widely used in large power equipment systems such as construction machinery and agricultural machinery equipment. The type of coil excitation signal has an impact on the performance of the transducer, but there is little research on this. Therefore, the influence of the coil excitation signal on transducer performance is investigated. The working principle and characteristics of the double-coil inductive displacement transducer are analyzed, and the circuit simulation model of the transducer is established. From the aspects of phase shift, linearity, and sensitivity, the effects of a sine signal, a triangle signal, and a pulse signal on the transducer are compared and analyzed. The results show that the average phase shift, linearity, and sensitivity of the sine signal were 11.53°, 1.61%, and 0.372 V/mm, respectively; the average phase shift, linearity and sensitivity of the triangular signal were 1.38°, 1.56%, and 0.300 V/mm, respectively; and the average phase shift, linearity, and sensitivity of the pulse signal were 0.73°, 1.95%, and 0.621 V/mm, respectively. It can be seen that the phase shift of a triangle signal and a pulse signal is smaller than that of a sine signal, which can result in better signal phase-locked processing. The linearity of the triangle signal is better than the sine signal, and the sensitivity of the pulse signal is better than that of the sine signal. [ABSTRACT FROM AUTHOR]

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine's disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine's health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine's health. [ABSTRACT FROM AUTHOR]

To study the emission characteristics of typical construction machinery in Chengdu, 12 construction machinery (excavators, bulldozers, loaders, and forklifts) under idling mode, moving mode, and working mode, were tested using a portable emission measurement system (PEMS). Under three operating modes, the typical construction machinery in the working mode was higher in the fuel-based average emission factors of PM 2.5 and NO x, while the fuel-based average emission factors of HC and CO were higher in idling mode. Integrated the results of investigation on ownership and activity levels of construction machinery, an exhaust emission inventory of typical construction machinery of Chengdu in 2018 was established according to the recommendation method. The annual emission of PM 2.5 , NO x , HC, and CO were 1.67 × 106, 1.61 × 108, 3.83 × 106, and 1.26 × 107 kg, respectively, and the excavator contributed the maximum emissions, accounting for an average proportion of 43.95%. The emission of construction machinery in Chengdu exhibited a clear monthly trend, with the highest from April to October and the lowest from November to March. In addition, the exhaust emissions presented an obvious spot-like characteristics, and the high-value areas were mainly concentrated in the surrounding suburban counties such as Shuangliu Wenjiang etc. To reduce pollution from construction machinery and improve the quality of the atmospheric environment, more effective measures on housing construction and municipal construction should be taken in those districts in Chengdu. [Display omitted] [ABSTRACT FROM AUTHOR]

The prediction of time-series data is a challenging and complex issue. For many practical applications, network topology information and text information can play a perfect role in time-series prediction. This article takes stock data as an example by constructing a graph, connecting each stock's upstream and downstream industries, and obtaining useful text features and topological features to predict the stock time-series. Based on the time-series data features, text features, and the topological features of the stock industry chain of machine learning, we compared our prediction model with other fuzzy time-series prediction methods, which are only based on historical features. The experiment shows that our method is better than the other methods in terms of the performance of multiple stocks in each stock's time-series prediction. The experimental results show that the stock topology based on the industrial chain effectively improved time-series forecasting accuracy. [ABSTRACT FROM AUTHOR]

In the face of the global energy crisis, the use of hybrid technology is an important way to achieve efficient energy storage and utilization. Hybrid systems have been successfully applied to automobiles and construction machinery, but no research study has been conducted on the design and application of energy recovery systems for reversible rolling mills. This article proposes a new type of noncoaxial parallel electrohydraulic hybrid drive system configuration for reversible rolling mills to save energy. It is proposed to collect the braking energy of the previous pass of the reversible rolling mill roll and the surplus power of the original primary drive motor and then start or accelerate the process for the next roll pass. A test experiment was established and the energy-saving efficiency of the proposed system was verified. By rolling 14 passes, 255.892 kJ of energy can be recovered. The overall energy efficiency rate is around 71.547%. The system will significantly promote energy saving, low carbon, green, and other aspects of the reversible rolling mills. [ABSTRACT FROM AUTHOR]