Industrial Machine Research Articles

Performance of advanced ceramic round tools when turning Inconel 718

Nickel-based superalloys, like Inconel 718, are very attractive metals for aerospace applications due to their excellent mechanical properties and thermal stability. However, these materials pose a significant challenge to the metal machining industry due to their very low machinability, especially in terms of reduced tool life because of the high heat generated during cutting. Nevertheless, it can be improved using high-performing tools that retain their hardness when exposed to high temperatures, such as ceramic tools. In this context, the study aims to compare the performance of two advanced ceramic tools, namely an alumina ceramic tool reinforced with SiC whiskers and a Bidemics™ one, when turning Inconel 718 at different cutting speeds, using as a baseline a conventional coated cemented carbide tool. First, the three inserts were characterized in terms of topography, thermal conductivity, and hot hardness to fully understand their operational behavior. Then, turning trials were conducted showing that both the ceramic tools outperform the coated carbide one in terms of tool life, with Bidemics™ demonstrating the best performance among all. This can be ascribed to its very high hardness maintained even at high temperatures, as well as its moderate thermal conductivity.

Combined simple adaptive and integral terminal sliding mode control for industrial feed drive systems

In the manufacturing industry, feed drive systems are utilized for various applications in industrial machines. These systems are crucial components for ensuring precision in motion control and are, therefore, expected to offer high tracking performance. This study focuses on application of integral terminal sliding mode control (ITSMC) to a feed drive system because of its robustness against disturbances and convergence of the tracking error in finite-time. However, its design requires plant dynamics model. To alleviate this concern, a solution of modifying the original ITSMC law to adaptive ITSMC law based on simple adaptive control (SAC) technique is used. To implement SAC-like adaptive law to a real plant, a property of “almost strictly positive real (ASPR)” is required to be satisfied. ASPR property is satisfied using velocity-based augmented output signal that ensures the stability. In experimental results, the adaptive controller follows the reference trajectory more precisely than conventional methods.

Vision-based multi-view reconstruction for high-precision part positioning in industrial robot machining

Traditional industrial robot machining typically involves offline programming of machining trajectories, with part positioning accomplished through robotic tools. However, this method has limitations, particularly when dealing with complex workpieces and dynamic environments. To tackle this problem, this paper introduces a vision-based multi-view reconstruction approach for part positioning in industrial robot machining. Multiple cameras capture multi-view images of the part, reconstruct its 3D point cloud, and match it with the CAD model of the part to achieve high-precision positioning. This method effectively translates offline programmed robot trajectories to real-world scenarios, improving the flexibility and adaptability of robot machining. Experimental results confirm the high accuracy and robustness of the proposed vision-based reconstruction method, achieving a relative error of less than 2 mm for 85.7% of points, with an overall root mean square error of 1.09 mm.

The Practice ofHeutagogy, Peeragogy andCybergogy Approach among Vocational College Instructors

This study investigates the implementation levels of Heutagogy, Peeragogy, and Cybergogy approaches among instructors at vocational colleges in Malaysia. Employing a quantitative survey research design, data were collected from a sample of 241instructors across 15 vocational colleges, representing various Technology fields such as Automotive, Electrical, Electronic, Construction, Welding, Industrial Machining, Refrigeration & Air Conditioning, and Computer Systems & Network Technology. The sample was selected using a cluster stratified convenientsampling method.Demographic information, including gender, age, race, institutional zone, vocational college, program, experience, qualification, certificate, and class, was gathered. A structured questionnaire comprising 53 items served as the primary data collection instrument. Data analysis was conducted using SPSS software to derive frequency values, percentages, mean scores, and standard deviations. The Kruskal-Wallis and Mann-Whitney U tests were employed to identify differences in the implementation of Heutagogy, Peeragogy, and Cybergogy approaches among the instructors.The findings indicate a high level of implementation for Heutagogy and Peeragogy approaches, while the Cybergogy approach is practiced at a moderate level. Significant differences were observed in the implementation of these approaches based on the instructors’ fields and teaching experience. To enhance the practice of Cybergogy, it is recommended that vocational college administrations provide training workshops or courses to improve the teachingskills of their academic stafffor 21stcentury learning environment.

Impact of 6S (5S+Safety) Implementation in Machine Workshops on Occupational Safety

In this current study, the impact of implementing 6S (5S+Safety) in the machining industry on occupational safety was investigated by selecting the chip machining section of a factory producing flywheels. To facilitate the comparison of risk levels before and after the implementation of 6S, a Fine-Kinney risk evaluation was conducted in the sawdust department before the application of 6S. The 6S implementation process took 28 days, and subsequently, a Fine-Kinney risk evaluation was performed in the sawdust department, revealing a 77.4% reduction in risks. As a result, the 77.4% reduction in accident risks indicates that the implementation of 6S, which emphasizes safety within the framework of the 5S principles, leads to a more organized, cleaner, and safer machine workshop environment. We can indicate that this approach not only reduces the risk of accidents but also enhances work efficiency, preventing time loss and material waste.

Perancangan Perancangan Sistem Trasmisi Mesin Pres Hidrolik Kapasitas 3 Ton Dengan Penggerak Motor listrik

Technological advances have encouraged improvements in the industrial sector in developing countries, including Indonesia. This is of course supported by several factors, including the use of modern equipment as a substitute for human labor. It can be said here that the more modern an industry is, the more machine power is used while the less human power is needed. As human needs become increasingly greater, the machine industry is currently also developing very rapidly. Developments in the industrial sector are marked by the creation of various types of machines that can assist human activities. The use of machines can help human activities or work to be more efficient in terms of completion time, as well as reducing the risk of work accidents. Currently, machines are used as tools that have high automation and precision. Therefore, many people use machines for everything, one of which is a pressing machine. A pressing machine is a tool made to compress or press an object, the power source can come from hydraulics, human power, electric motors and others. The aim of this final project is to design a transmission system for a 3 Ton Semi-Automatic Hydraulic Press Machine with an electric motor drive. It can increase the author's knowledge, insight and experience regarding planning a machine. Conclusion Ball bearings with type 6203 require an installation time of 1 minute 35 seconds and removal of 1 minute 32 seconds. Ball bearings of type 6305 receive a pressure of 400 psi to be installed on the shaft, and when released the pressure produced is the same, namely 400 psi to be able to leave the shaft. A type 6206 ball bearing with a shaft length of 77 mm takes 3 minutes 32 seconds to install, while the same shaft length takes 3 minutes 22 seconds to remove from the shaft.

Sustainable hard machining under zirconia nano-cutting fluid: A step towards a green and cleaner manufacturing process

The present study deals with the machinability (flank wear, surface integrity, cutting temperature and power consumption) and sustainability (Carbon emission and noise emission) investigations during hard machining under dual nozzle assisted novel ZrO2 based nano fluid MQL condition considering Taguchi L27 OA design of experiment. 0.3 % wt. ZrO2 nanofluid MQL medium effectively reduce cutting temperature and found as 65 °C, which shows the effective cooling and lubrication properties of zirconia nanofluid. Improvement in surface quality has been observed with lower rate of flank wear. Depth of cut and feed are found to be the most influential factor affecting power consumption and carbon emission with contribution of 47.213 % and 60.861 % respectively. Noise emission mostly affected by the depth of cut with a contribution of 43.40 %. Optimal parametric condition yields d: 0.1 mm, f: 0.1 mm/rev and v: 80 m/min through WASPAS and sustainable towards manufacturing industries for cleaner hard machining.

Energy consumption modeling in industrial sewing operations: A case study on carbon footprint measurement in the apparel industry

In the textile supply chain, the apparel manufacturing industry heavily relies on energy consumption (EC), contributing to greenhouse gas emissions. Understanding and optimizing energy usage in apparel manufacturing is crucial for sustainable and energy-efficient manufacturing. The EC in the industrial sewing operations is directly linked with sewing parameters, including fabric seam length, standard sewing speed, and stitching time. In general, the carbon footprint of an apparel industry is measured by considering the standard minute value (SMV) and specific energy consumption (SEC). However, these metrics include non-productive time and do not accurately measure actual EC or assess the industry’s carbon footprint. To address these limitations, this case study tackles this technical issue by calculating the stitching time of an industrial sewing machine based on its actual EC derived from the sewing machine’s motor power. This study employs EC modeling for industrial sewing parameters, including fabric seam length, standard sewing speed, and stitching time. This proposed model utilizes real-time data collected during the garment-making process, including fabric spreading, cutting, sewing, and ironing. The study allocates eleven sewing machines for a specific garment style to stitch a basic knit fabric t-shirt. The total EC for the entire garment-making (t-shirt) process at a single line is 0.1052 kWh, equivalent to 59.97 g of CO2 emissions (Bangladesh’s CO2/kWh emission factor). This study explores that a direct-drive servo motor consumes approximately five times less than its maximum motor load power, making it more energy-efficient than other sewing machines. Therefore, this study demonstrates the effectiveness of EC modeling by incorporating industrial sewing parameters and selecting energy-efficient sewing machines to reduce carbon footprints and enhance environmental sustainability in the apparel manufacturing industry.

Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Electromyographic analysis of bilateral upper trapezius muscles at different levels of work-pace among sewing machine operators

Workers are driven to work faster in the industrial work environment to meet high productivity targets. An increased work pace leads to increased muscle activation. However, the effect of work pace on bilateral upper trapezius muscles during sewing machine operation in an industrial work environment has not been thoroughly investigated in experimental studies. Therefore, this research aims to conduct an experimental study to analyze the bilateral upper trapezius muscle activity of industrial sewing machine operators at different levels of work pace. Thirty subjects (15 males, and 15 females) continuously performed the sewing operation for two hours in an industrial work environment. Experiments were conducted for two levels of work pace i.e. low pace (100% of standard cycle time) and high pace (120% of standard cycle time). Electromyographic signals were recorded from the bilateral upper trapezius muscles. The EMG amplitude (RMS) among the muscles was computed. A statistically significant (p < 0.05) increase in muscle activity was observed with an increased work pace. In this study, right upper trapezius muscle activity increased by 30.4% during high work pace tasks compared to low pace, while the left upper trapezius showed a 24.12% increase. The right upper trapezius showed a mean difference of 0.696 (%MVC), and the left upper trapezius showed 0.399 (%MVC), both indicating greater activity during high-pace tasks. The increase in muscle activity with time indicated the presence of muscle fatigue among sewing machine operators. Furthermore, higher muscular activity was observed among females than males. This research highlights the critical need to balance productivity goals with the health and safety of workers, reducing the risk of muscle fatigue and associated work-related musculoskeletal disorders.

Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis

Varying components and operating conditions in industrial machines lead to different distribution characteristics and fault states of monitoring data for critical rotating machinery. Adequate and accurate labelling of monitoring data is challenging to achieve. Existing data-driven unsupervised domain adaptation methods exhibit poor interpretability and low confidence in the target domain’s pseudo-labelling. Therefore, a physics-informed unsupervised domain adaptation framework is proposed. Data and physical information are combined to enhance the reliability and interpretability of the model while ensuring its generalization capability. First, physical information is extracted by merging the knowledge of the mechanical component fault characteristics with indicators like the signal spectrum’s energy value. Subsequently, the physical label and loss are created. The physical loss is then embedded in the loss function of the source domain model. Next, by aligning the physical labels and the clustering results of the spectral clustering, the target domain samples’ hard pseudo-labels are obtained. The probability values of the sample belonging to each category as determined by the Wasserstein criterion comprise the soft pseudo-label of the sample. Furthermore, the confidence of hard pseudo-labels is continually increased by iteratively updating the probability values of soft pseudo-labels using the multi-label as a guide and the inverse improved sigmoid function as a gradient. Finally, unsupervised cross-machine diagnostic models with high robustness can be obtained. Three datasets are employed in the experimental section, and five sub-datasets are created and crossed for cross-machine experiments to confirm the suggested approach’s efficacy and superiority.

Развитие теории и принципов планирования многономенклатурных технологических процессов механообработки и сборки

Instructional guidelines for planning a multiproduct manufacturing activity (MA) for machining and assembly aimed at solving currently existing problems of machining industries process design, i.e. subjectivity of making design decisions, insufficient automation, and inability of the observing of current production situation are presented. The objective reasons for the need to improve approaches to the development of MA are the long cycle of the process design period, the low quality of the processes operation, and the impossibility to adjust MA at the implementation stage. The main structural elements of multiproduct manufacturing activity planning are described. Based on a systematic approach, all stages of work on technological support of production systems are combined, making possible to use the information arrays of data on the real state of the production system and operational information on production tasks in a short time frame. The results of theoretical work provide for the process of making MA as a system that combines the design and implementation of technology, taking into account the impact of changes in the production situation. In the process of approbation and use of the presented methodological approaches, significant areas of research were identified, smoothing away difficulties connected with the performance targets, enabling the development path of the system. Among them: the interaction with the design preparation of production; detailing data on the initial workpieces in connection with the choice of the MA structure and the appointment of a rational set of technological equipment; determining the requirements for technological equipment, focused on ensuring the flexibility of their use; determining the correlations between the multiple-path design of parts manufacturing process and the requirements for accuracy indicators in the assembly of products. The developed formalized models for the implementation of the stages of technological preparation of machining industries are a background for full automation of MA design and highly efficient functionality of machine-building complexes.

Optimizing the Die-Sink EDM Machinability of AISI 316L Using Ti-6Al-4V-SiCp Electrodes: A Computational Approach

Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi’s L27 orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use.

Influence of Temperature on Brushless Synchronous Machine Field Winding Interturn Fault Severity Estimation

There are numerous methods for detecting interturn faults (ITFs) in the field winding of synchronous machines (SMs). One effective approach is based on comparing theoretical and measured excitation currents. This method is unaffected by rotor temperature in static excitation SMs. However, this paper investigates the influence of rotor temperature in brushless synchronous machines (BSMs), where rotor temperature significantly impacts the exciter excitation current. Extensive experimental tests were conducted on a special BSM with measurable rotor temperature. Given the challenges of measuring rotor temperature in industrial machines, this paper explores the feasibility of using stator temperature in the exciter field current estimation model. The theoretical exciter field current is calculated using a deep neural network (DNN), which incorporates electrical brushless synchronous generator output values and stator temperature, and it is subsequently compared with the measured exciter field current. This method achieves an error rate below 0.5% under healthy conditions, demonstrating its potential for simple implementation in industrial BSMs for ITF detection.

ANALISIS STUDI KOMPARATIF KEBERHASILAN BERWIRAUSAHA PADA PENGUSAHA VCO (VIRGIN COCONUT OIL) DI KABUPATEN BULELENG

This study aims to determine how the analysis of Comparative Studies of Entrepreneurial Success in VCO (Virgin Coconut Oil) Entrepreneurs in Buleleng Regency. This research uses a qualitative approach, with data collection techniques using structured interview techniques to extract information and documentation techniques to collect a number of documents as information data material, namely sound recordings and photographs to make conclusions and data analysis. The source triangulation technique is used as a data validity technique by asking several questions to the sources so that conclusions can be drawn from the answers that have been given. Data analysis techniques used in this study using SWOT analysis, namely strengths, opportunities, weaknesses, and threats. The results of the study analyzed the entrepreneurial success of VCO products in Buleleng Regency from internal and external factors, which is said to be successful is Bali Pure VCO. From the S-O strategy where this strategy uses the strengths of the company, to take advantage of its great opportunities. This can be seen from the situation and condition of the company that is being run, which has followed the development of technology by using large industrial machines and unique packaging techniques and maintained sterilization. In addition, Bali Pure products have also innovated a lot by utilizing coconut fruit not only as pure coconut oil but also as a bath soap product with a variety of natural aromas

Optimization of Wood-Based Birch Plywood CO2 Laser Engraving Process Parameters with Taguchi Method

In this study, the optimization of the parameters used in the engraving process of wooden birch plywood material with a CO2 laser machine was investigated using the Taguchi method. An industrial laser machine with a 150W glass tube was used during the experimental examination process. The basic parameters for engraving wooden surfaces include laser power (P), engraving speed (S) and laser head height (F). The engraving depth (D) and engraving width formed on the surface during the engraving process are the main factors that determine the aesthetics of the product. Taguchi L25 orthogonal array was used in the experiments to determine and optimize the highly important parameters. The optimum combination of parameters in the laser engraving process was then evaluated. The research results showed that the effect of P and S factors played a leading role, and the F parameter had a small effect on the depth of wood scraping. Optimization results found that F:5, S:100 and P:30 gave the best engraving depth optimization, while F:6, S:300 and P:10 gave the lowest engraving depth optimization result.

Hybrid predictive maintenance model – study and implementation example

Abstract In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag-nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa-rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat-ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re-search conducted, it was determined which machine operating parameters are characterised by varia-bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime.

Customer Satisfaction Based on Service Quality and Price

Industrial machine repair services is one of the service industries where new company actors have emerged and innovated as a result of the growth of the service sector. One strategy to outperform the competition is to prioritize customer satisfaction. A few important factors that affect customer satisfaction are price and the quality of the services rendered. The purpose of this study is to ascertain how pricing and service quality at PT. Karya Utama Wijaya Sakti relate to each other in terms of customer satisfaction. The sample size is one hundred clients, and multiple linear regression analysis is the method employed. The study's findings show that, albeit slightly concurrently, both the pricing and the quality of services at PT. Karya Utama Wijaya Sakti have a positive and significant impact on customer satisfaction.

Bridging minds and machines in Industry 5.0: neurobiological approach.

In transitioning from Industry 4.0 to the forthcoming Industry 5.0, this research explores the fusion of the humanistic view and technological developments to redefine Continuing Engineering Education (CEE). Industry 5.0 introduces concepts like biomanufacturing and human-centricity, embodying the integration of sustainability and resiliency principles in CEE, thereby shaping the upskilling and reskilling initiatives for the future workforce. The interaction of sophisticated concepts such as Human-Machine Interface and Brain-Computer Interface (BCI) forms a conceptual bridge toward the approaching Fifth Industrial Revolution, allowing one to understand human beings and the impact of their biological development across diverse and changing workplace settings. Our research is based on recent studies into Knowledge, Skills, and Abilities taxonomies, linking these elements with dynamic labor market profiles. This work intends to integrate a biometric perspective to conceptualize and describe how cognitive abilities could be represented by linking a Neuropsychological test and a biometric assessment. We administered the brief Neuropsychological Battery in Spanish (Neuropsi Breve). At the same time, 15 engineering students used the Emotiv insight device that allowed the EEG recollection to measure performance metrics such as attention, stress, engagement, and excitement. The findings of this research illustrate a methodology that allowed the first approach to the cognitive abilities of engineering students to be from neurobiological and behavioral perspectives. Additionally, two profiles were extracted from the results. The first illustrates the Neuropsi test areas, its most common mistakes, and its performance ratings regarding the students' sample. The second profile shows the interaction between the EEG and Neuropsi test, showing engineering students' cognitive and emotional states based on biometric levels. The study demonstrates the potential of integrating neurobiological assessment into engineering education, highlighting a significant advancement in addressing the skills requirements of Industry 5.0. The results suggest that obtaining a comprehensive understanding of students' cognitive abilities is possible, and educational interventions can be adapted by combining neuropsychological approaches with EEG data collection. In the future, it is essential to refine these evaluation methods further and explore their applicability in different engineering disciplines. Additionally, it is necessary to investigate the long-term impact of these methods on workforce preparation and performance.

Industrial equipment optimization for combustion performance enhancement: a real-world case study

To address the issue of high NOx emission from the combustion chamber, this work optimized the industrial machine structure to enhance the combustion performance. The analysis results indicated that the flue gas recirculation (FGR) could effectively reduce the combustion temperature and the distribution of high-temperature regions in the machine chamber, thereby suppressing NOx formation without affecting the gas velocity inside the chamber. Based on the simulation analysis, the FGR technology was applied to modifying the machine structure and evaluated the modification effect in real-world application. It is found that after adding FGR, the oxygen content at the furnace outlet decreased from 13.8% to 10.5%, the NOx emission from the furnace decreased from 80 mg/m3 to 18 mg/m3, and the natural gas consumption decreased by more than 17%. These results demonstrate a significant impact on energy saving and emission reduction after optimizing the machine structure, which can provide a reference basis for subsequent researchers in this field.