Machinery Manufacturing Industry Research Articles

The Influence of Digital Transformation on the Reconfigurability and Performance of Supply Chains: A Study of the Electronic, Machinery, and Home Appliance Manufacturing Industries in China

In this era of intense global competition, supply chains are facing challenges in coping with emerging market issues. Within diverse industries worldwide, supply chains are experiencing accelerated reconfiguration, with one of the most notable transformations being the digitalization of supply chain operations. But the literature lacks empirical evidence about how digital transformation effectively contributes to it. Thus, this paper delves into the implications of the supply chain digital transformation (SCDT) and supply chain reconfigurability (SCR) on its overall performance. Cross-sectional data from 379 respondents in the machinery, electronics, and home appliance manufacturing industries were collected through a closed questionnaire. Utilizing a hybrid approach involving the Partial Least Squares Structural Equation Model (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study employs a cross-validation of the conceptual model. Initially, the PLS-SEM is employed to unveil the direct impact of SCDT on supply chain performance (SCP), as well as the intermediary effect of SCR. Building on this foundation, the fsQCA method is further utilized to investigate the configuration paths that enable enterprises to achieve high SCP under the combined influence of SCR and SCDT. The research results affirm the significant influence of SCDT on SCP. Likewise, the findings highlight the crucial intermediary role of SCR between SCDT and SCP. Ultimately, three distinct configurations driving high supply chain performance are identified: technical, management, and flexible configurations, each playing a unique role.

Effect of Yoga in Industrial Workers with Chronic Venous Insufficiency: A Randomized Controlled Trial.

Background and objective: Prolonged standing is one of the significant contributors to chronic venous insufficiency (CVI) in industry workers. Yoga is proven to be an effective therapy in treating occupational hazards. The current study aimed to investigate the effect of yoga on CVI among industry workers. Methodology: Male workers (n = 100) from machinery manufacturing industries in Bangalore meeting the inclusion and exclusion criteria were recruited for the study. The yoga group received a specifically designed yoga module for 6 days a week for 12 weeks, and the control group was offered lifestyle suggestions. Plasma homocysteine was used as the primary outcome variable, whereas Venous Clinical Severity Score, ankle brachial pressure index (ABPI), ankle and calf circumference, CVI questionnaire, and Chalder fatigue scale were assessed as secondary variables at baseline and the end of 12 weeks of intervention. Results: Eighty-eight participants (yoga = 43, control = 45) completed the study. A one-way analysis of covariates (ANCOVA) was used to determine the significant differences between groups in the post-values. A significant difference was found between groups in plasma homocysteine (partial eta squared = 0.34, p < 0.001). All variables, except for ABPI, calf circumference, and ankle circumference, had shown statistically significant differences between the yoga and control groups after 12 weeks of intervention, with moderate to high effect sizes. There were no significant adverse events associated with the intervention. Conclusions: Yoga practices can reduce the symptoms of CVI along with vascular inflammation as indicated by reduced plasma homocysteine. Overall, yoga practices are found to be safe and efficacious for managing CVI. IEC Reference Number: RES/IEC-SVYASA/184/2021 Trial Registration Number (If Clinical Trial): CTRI/2021/02/030944.

Analysis of Tax Risk Management and Control in Port Machinery Manufacturing Industry--Taking Enterprise S as an Example

Port machinery manufacturing has a long history in China and plays an important role in promoting the country's economic progress. Advanced port machinery not only enhances the ability of ports to handle goods, but also effectively cuts down on logistics expenses, thus solidly supporting the country's economic pulse. Against this background, preventing and dealing with the VAT tax risks faced by the port machinery manufacturing industry and improving tax compliance of taxpayers have become key issues. In this paper, a port machinery manufacturing enterprise in the stage of product upgrading and transformation is selected as the research object, and 2020 is taken as the benchmark, focusing on the tax situation of the enterprise in 2021 and 2022. In the case study of this enterprise, this paper adopts a tax-related risk model and longitudinal data analysis within the enterprise to identify in-depth the value-added tax (VAT) and corporate income tax (CIT) risks of the port machinery manufacturing enterprise, as well as the tax-related risks of the enterprise's investment and merger and acquisition business, so as to control the enterprise's tax risks and promote the sustainable development of the enterprise.

The Scaling-up of Agricultural Mechanization Services and the Re-shaping of Small-Scale Household Farming in China

Abstract Taking the case of the agricultural mechanization service industry as an example, this study discusses the dynamics of the transformation of China’s agricultural contracting service industry in the context of the scaling-up of agricultural production, and how the transformation is reshaping small-scale household farming in grain production. With the accelerated growth of farmland transfer, large-scale agricultural producers have been emerging rapidly in different parts of China in the past years. Since their demand for agricultural contracting services is different from that of small-scale household producers, the scaling-up of agricultural production has promoted the scaling-up of agricultural contracting services. Meanwhile, the capital surplus in the agricultural machinery manufacturing industry has created three consequences simultaneously: the excess supply of agricultural mechanization services, the acceleration of agricultural machinery renewal, and the rising cost of machinery purchases. The interactions of these three factors not only raised the threshold of the agricultural machinery service industry, which makes it difficult for part-time service providers to survive market competition, but also shortened the time window for machinery service provision, which makes it difficult for large-scale agricultural producers to balance land operation and machinery service provision. Consequently, large-scale agricultural machinery service organizations are continuously replacing those small-scale part-time service providers. During this process, small-scale household producers engaged in grain production are faced with increasing risks of being excluded from farming, both directly and indirectly, as it is difficult for small producers to deal with large service organizations, and the small-scale part-time service providers that used to serve them are continually being replaced.

General Characteristics of Konya Agricultural Machinery Manufacturing Industry

A survey was conducted in this study to determine the general characteristics of the agricultural machinery manufacturing industry in Konya region. For this purpose, 50 enterprises of different scales, which are thought to represent the sector, were determined and these enterprises were included in the study. When the enterprises were evaluated in terms of size, it was determined that 52% of them were small-scale enterprises. Average age of the business owners is 53 years, their sector experience is 35 years and 47% of them are primary school graduates. While a total of 1204 workers are employed in the enterprises participating in this study, there is an average of 24.5 workers per enterprise. Of the total personnel, 78.3% consisted of workers, followed by engineers with an average of 5.5%, accountants with 5.3%, marketers with 5% and technicians with 4%. Only 38% of the enterprises cooperate with institutions such as KOSGEB and TUBITAK for their R&amp;D activities and receive consultancy from the universities. Present findings shed light on the developments in the sector as well as understanding the situation of agricultural machinery manufacturing industry enterprises in the region.

Analysis of active surveillance results of pneumoconiosis in Jiangsu Province in 2019

Objective: To conduct active surveillance of pneumoconiosis in Jiangsu Province in 2019, to understand the status quo of occupational health of dust-exposed workers, timely detect occupational health damage and occupational hazard factors, and provide scientific basis for the prevention and control of pneumoconiosis in the future. Methods: From January to December 2019, counties (cities and districts) with relatively concentrated dust hazard enterprises in Jiangsu Province were selected as monitoring points to carry out active surveillance of pneumoconiosis, and occupational health examination of dust workers (3540 people) in 255 enterprises were carried out, including chest radiographs and lung function examinations. SPSS 26.0 statistical software was used for statistical analysis of the data, and Chi-square test was used to analyze the difference level of the rate between different groups such as gender, age and service. Results: Among the 255 employers, small enterprises accounted for the highest proportion (186 enterprises, 72.94%), private economy accounted for the highest proportion (225 enterprises, 88.24%), and machinery manufacturing industry accounted for the highest proportion (44 enterprises, 17.25%). Among the 3540 workers exposed to dust, 6 suspected occupational diseases (0.17%), 50 occupational contraindications (1.41%), and 1469 other diseases or abnormalities (41.50%) were detected. Among the 3540 workers exposed to dust, 399 (11.27%) had abnormal chest radiography results, and 848 (23.95%) had abnormal lung function indicators. The abnormal detection rate of chest radiographs in males (12.25%, 359/2931) was higher than that in females (6.57%, 40/609), and the abnormal detection rate of lung function in females (32.18%, 196/609) was higher than that in males (22.24%, 652/2931), with statistical significance (P<0.05). The age of the workers exposed to dust ranged from 17 to 75 (41.78±10.39) years old, and the years of service in contact with dust of the workers ranged from 0.08 to 42 (6.72±7.47) years. There were statistically significant differences in the detection rate of abnormal chest radiography and abnormal lung function among different age and seniority workers (P<0.05). The abnormal rates of forced vital capacity (FVC), forced expiratory volume in the first second (FEV(1)) and FEV(1)/FVC were 13.79% (488/3540), 15.28% (541/3540) and 1.86% (66/3540), respectively. There were significant differences in the detection rate of abnormal chest radiography and abnormal lung function among workers exposed to different dust types (P<0.05) . Conclusion: There are few suspected occupational diseases detected by active surveillance of pneumoconiosis in Jiangsu Province in 2019, and there are differences in occupational health examination results among workers with different dust types, ages, genders and dust exposure years.

Application research of machine manufacturing process monitoring based on neural network

In the process of mechanical manufacturing, parameters such as geometry, size and surface quality are important factors affecting product quality. The existing technology makes it difficult to conduct product monitoring and data monitoring during machine manufacturing. At the same time, the uncertainty of the manufacturing process also brings great challenges. Therefore, it is necessary to design corresponding monitoring schemes according to different situations. Therefore, neural network based machinery manufacturing process monitoring technology is of great significance. The neural network model established in this paper can automatically learn and establish complex mapping relationships, and can predict and detect anomalies according to historical data. In addition, deep learning technology can be used to carry out adaptive feature learning on a large amount of monitoring data, and complete efficient training and real-time decision-making in a distributed environment. Compared with the common mechanical manufacturing process, neural network monitoring has a lower loss rate and error rate. The neural network based machinery manufacturing process monitoring technology will become an important direction of the future development of machinery manufacturing industry, and will have great practical application value in improving production efficiency, reducing production cost and ensuring product quality.

Analysis of the application of intelligent CNC technology in machinery manufacturing

The application of intelligent computer numerical control(CNC) technology enjoys a key strategic position in various countries, and countries that attach importance to the development of CNC machine tools and their fields of innovation branches generally have a solid foundation of intelligent manufacturing and strong production capacity. Combined with big data and artificial intelligence, CNC technology has become an important cornerstone of smart manufacturing. In order to achieve the goal of sustainable development, the machinery manufacturing industry should not only improve product accuracy and enrich product types, but also shorten the production cycle as much as possible and avoid the impact of human errors on product quality. Based on this, this paper mainly analyzes in detail the actual development of machinery manufacturing based on intelligent robot numerical control technology in order to provide some help for the long-term development of the industry. This paper is dedicated to the visualization of relevant data and finally provides a reliable basis for the corresponding decision-making based on the current actual development of intelligent CNC technology. After this paper analyzes the current situation of the industry, we find that intelligent numerical control technology has a vital role in the intelligent transformation of China’s industry, high-tech innovation, and rationalization of AI applications, and we hope that this paper can provide a reference for China’s industrial production industry.

Genetic algorithm-based error correction algorithm for CNC turning machining of mechanical parts

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-integral-differential 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.

Yoga for musculoskeletal pain, discomfort, perceived stress, and quality of sleep in industry workers: a randomized controlled trial.

Work-related musculoskeletal pain and discomfort are due to repetitive, unnatural, continuous movements and postures. Yoga therapy is beneficial for pain and disability in occupational settings. The current study aims to investigate the effect of yoga on musculoskeletal pain, stress, and sleep quality among industry workers. The study was conducted as a parallel randomized controlled open-label trial. An allocation ratio of 1:1 was followed for randomization. A health survey was used to recruit 90 male workers in machinery manufacturing industries from Bengaluru. A specially designed yoga module was administered five days a week for eight weeks to yoga group. The waitlisted control group received lifestyle suggestions. Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), Perceived Stress Scale (PSS), and Pittsburgh Sleep Quality Index (PSQI) were administered at baseline, at the fourth week and eighth weeks. Repeated Measures Analysis of Variance (RM-ANOVA) was conducted to elicit the group*time interactions. Ninety participants (Yoga = 45, Control = 45) with age 40.57 ± 6.85 were randomized. Significant interactions were found in CMDQ, VAS, PSS and PSQI scores. Further, between group comparison demonstrate significant difference between the groups at the end of 8weeks for CMDQ, VAS and PSS scores. Yoga can be used to reduce musculoskeletal pain, discomfort, perceived stress and improve sleep quality among industry workers. CTRI/2022/03/040894.

A series of plasma innovation technologies by the double glow discharge phenomenon

In order to break the limitation of plasma nitriding technology, which can only be applied to a few non-metallic gaseous elements, the “double glow discharge phenomenon” was defined and then the “double glow plasma surface metallurgy technology” was invented. This double glow plasma surface metallurgy technology can use any element of the Periodic Table for surface alloying of metallic materials. Countless surface alloys with special physical and chemical properties have been produced on the surfaces of conductive materials. By using the double glow discharge phenomenon, a series of new double glow plasma technologies have been developed to apply to surface metallurgy, graphene technology, brazing, sintering, arc plasma surface alloying, nanotechnology, cleaning, carburizing without hydrogen, and so on. This article briefly introduces the basic principles, functions, and characteristics of each technology. The application prospects and development directions of plasma in metallic materials and the machinery manufacturing industry will also be discussed.

Lean and Industry 4.0 mitigating common losses in Engineer-to-Order theory and practice: an exploratory study

AbstractCompanies employing an Engineer-to-Order (ETO) manufacturing strategy often develop, design and produce complex goods in single or small batches based on an individual customer order. The increased complexity caused by project business multiplies the engineering, cost, and change management efforts that are often required, resulting in lost productivity and more non-value-adding activities. To improve efficiency and reduce losses, ETO organizations strive to implement Lean practices and Industry 4.0 (I4.0) technologies, but the relevant literature in ETO industrial context is more exploratory and contains less empirical data. Building on the preliminary work categorizing the common losses in ETO organizations, the study examined how these can be reduced by Lean and I4.0 practices. Current literature was analyzed, and empirical data was collected using a survey questionnaire and semi-structured interviews with 16 companies from the construction, shipbuilding, and machinery and plant manufacturing industries. The scientific literature was compared with the empirical data to find out whether Lean methods and I4.0 technologies are known in research but not applied in practice and vice versa. As a result, there are several practices from both domains applied in practice to reduce losses, but the analyzed literature indicates few successful implementations. Future research should aim to provide more empirical data on the application of Lean and I4.0 practices to mitigate losses in companies with an ETO strategy and provide best practices and guidelines.

Applicability Analysis of Carbon Fiber Composite Materials for a Novel Lightweight Designing Lap Bar of Amusement Ride

The machinery manufacturing industry, including amusement rides, has made progress in structural lightweight and increasingly using carbon fiber‐reinforced plastics (CFRP) to achieve modest weight savings. The lap bar, as a key force‐bearing component used to restrain passengers, bears the brunt of becoming an important direction for lightweight design. To address this issue, the study analyzes and calculates its stress and deformation based on finite element analysis, and the results show that the CFRP lap bar does not exhibit obvious strain concentration, expected to have a longer service life. On the basis of these studies, the CFRP lap is prepared, and it is found that this substitution leads to a reduction in weight of 66.7%. In addition, three groups of fatigue tests are used to simulate its running state under nuclear load and overload, and acoustic emission (AE) technology is used to monitor it. The results show that there is no obvious damage in the tests of three different stages, and the reliability is strong, which verifies that the selection of carbon fiber materials can meet the requirements of bumpers.

Preface

This issue of the Journal of Physics: Conference Series contains the proceedings of the 2nd International Conference on Electronic Information and Mechanical Manufacturing (EIMM 2022). The conference took place in Guilin, China during November 18th-20th, 2022 (virtual conference), with the attendance of about 150 prominent delegates in the fields related to electronic information and mechanical manufacturing all over the world.The electronic information engineering profession is pivotal in the development of China’s information technology industry, and in the information age, the integration of big data, artificial intelligence, and the Internet of Things with the manufacturing industry is also deepening, leading to the constantly changing and upgrading of machinery manufacturing industry in the direction of automation and intelligence. EIMM 2022 created an international academic for scholars, specialists and researchers to present their research activities, exchange insights and new ideas, and discuss the key challenges and research directions facing the development of relative fields.To make this conference on schedule, we had a three-part plenary conference including a keynote speech part delivered by three speakers from different countries. Among them, Prof. Yaping Zhao from Northeastern University, China performed a dramatic keynote speech. His research domains lie in Meshing Theory for Mechanical Transmissions, Research and Development of Novel Types of Gear Drive, Engineering Differential Geometry and so on. He published more than 30 papers in the international SCI journals as corresponding or first author, and won many academic titles like New Century Excellent Talent Awarded by Chinese Ministry of Education, Excellent Talent in Institutions of Higher Learning in Liaoning Province, Jianlong specially-appointed professor of Northeastern University China, etc. We are very optimistic that all participants will acquire new ideas and knowledge from this conference and each keynote speech to enhance the capability toward electronic information and mechanical manufacturing.We have received tremendous paper submissions from all over China and abroad. Through a rigorous peer-review process, all submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility. Based on the expertise review comments, some excellent papers were accepted and the corresponding authors were invited to submit their papers for final publication. The accepted papers included topics on: Electronic Information Engineering, Flexible Electronics, Numerical Control Technology, Industrial Engineering Technology, etc.We would like to thank the Conference General Chairs and the Technical Program Committees for their hard work that lead to the organization of this event, to the members of the Journal of Physics: Conference Series for their patience while waiting for the final versions of the papers and to every author who has contributed with an article to these proceedings. We firmly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.The Committee of EIMM 2022List of Committee member is available in this Pdf.

A Deep Neural Network-Based Intelligent Detection Model for Manufacturing Defects of Automobile Parts

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data.

Single neuron PID based method for deformation suppression during CNC machining of parts

AbstractCNC machining realizes the automatic control of machine tools with digital information, which is an advanced technology widely used in today's machinery manufacturing industry. In the process of NC lathe machining of parts, thin‐walled workpieces are prone to deformation due to poor rigidity and thinner wall thickness. Therefore, it is necessary to study the deformation suppression of NC parts. By combining fuzzy control with single neuron PID and introducing fast non dominated sorting genetic algorithm, the parameters of thin‐walled workpiece milling are optimized. The results show that the neuron PID algorithm of fuzzy control has no overshoot in the environment with or without interference, and has fast response speed and strong anti‐interference ability. In the case verification, the cutting force controlled by fuzzy neuron PID can quickly reach the reference 240 N and remain stable. The removal rate fluctuates less in the range of 8000–12,000. It can improve the metal removal rate while maintaining a constant cutting force, so as to restrain the machining deformation of parts. At the same time, the introduced fast non dominated sorting genetic algorithm can increase the maximum rotational speed to 10,486.5, the maximum feed rate per tooth from 0.075 to 0.112, and the rotational speed is nearly doubled, effectively improving the processing quality, providing a technical reference for the stable control of the NC machining system, and providing a new idea and method for part deformation suppression.

Lean, green, and smart manufacturing: An ingenious framework for enhancing the sustainability of operations management on the shop floor in industry 4.0

In the present competitive landscape industry, individuals have emphasized fulfilling the consumer needs in available resources and limited constraints. However, the industry individuals face several problems in achieving operational efficiency. These problems include ergonomics issues, environmental waste, work processes waste, inobservance on the shop floor, and workers’ apathy. The operations management teams use various approaches to improve operational excellence on the shop floor, including lean manufacturing, smart manufacturing, green manufacturing, and artificial neural network. These approaches are used to enhance productivity by eliminating problems in the operations management on the shop floor. The present research aims to develop a framework for improvement in operational excellence and sustainability in operations management on the shop floor in an industry 4.0 environment using lean, green, and smart manufacturing concepts. In the study, a strategic ergonomics assessment and environmental assessment have been used to assess working and operational performance on the shop floor. The adaptability of the developed framework has been investigated by implementing it in mining machinery manufacturing industry. The framework has been developed with insights gained from previous research, industry individuals, and brainstorming analysis on problems faced in operations management on the shop floor in industry 4.0 working environment. The novelty of the present study lies in the fact that no model has been developed for achieving operational excellence on the shop floor in industry 4.0 using the smart, lean green manufacturing concept. The developed framework significantly improved productivity by 85%, improved machinery utilization by 21%, reduced production time by 65%, reduced manufacturing defects by 96%, and production cost by 75%. The present research work strongly believe that the developed model would benefit industry individuals in enhancing sustainability in operational excellence on the shop floor in industry 4.0 environment by establishing a more competent and cleaner working environment with available resources.

Preparation of sodium alginate-polyvinyl alcohol@PVDF hybrid membrane for efficient oil-water separation

Oily sewage is widespread in petrochemical and machinery manufacturing industries. Direct discharge not only wastes water and oil resources, pollutes the ecological environment, but also affects the survival and health of humans and other organisms. The traditional oil-water separation method has strong limitations, poor economy and low separation efficiency. Sodium alginate (SA) and polyvinyl alcohol (PVA) were uniformly coated on the surface of PVDF membrane, and superhydrophilic SA-PVA@PVDF hybrid membrane was prepared by multiple cyclic self-assembly. The oil-water separation experiment was carried out by using the membrane. The initial separation efficiency was 99.8%, and the overall separation efficiency remained above 98.3% after 10 cycles of regeneration experiments. This study provides a new idea for the low-cost, green and simple preparation of superhydrophilic-underwater superoleophobic separation materials.

Service-Oriented Manufacturing Mode for Agricultural Machinery Manufacturing Industry

Service-Oriented Manufacturing Mode for Agricultural Machinery Manufacturing Industry

Application in testing courses of milling force signal processing

The course of testing technology plays an extremely important role in the informatization of machinery manufacturing industry and the cultivation of innovative talents. In order to better let students to grasp the sensor output signal how to obtain clean signal through signal processing, this paper puts forward the cutting force signal processing case in milling experiment, help students to combine theory with practice.