Manufacturing Factory Research Articles

Reconfigurability in Automobiles—Structure, Manufacturing and Algorithm for Automobiles

Review Reconfigurability in Automobiles—Structure, Manufacturing and Algorithm for Automobiles Zheming Zhuang 1, Yuntao Guan 1, Shuangjia Xu 2,4, and Jian S. Dai 3,4, * 1 Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, Centre for Advanced Mechanisms and Robotics, Tianjin University, Tianjin, 300072, China zhuangzheming@tju.edu.cn (Z.Z.); guanyuntao_018@tju.edu.cn (Y.G.) 2 DH Robotics Ltd, Shenzhen, China; Shuangjia.xu@dh-robotics.com 3 Institute for Robotics, Southern University of Science and Technology, Shenzhen, China 4 Centre for Robotics Research, King’s College London, London, UK * Correspondence: jian.dai@kcl.ac.uk Received: 9 November 2022 Accepted: 10 November 2022 Published: 18 December 2022 Abstract: For the automobile design and manufacturing, as a typical representative of the industry, the development and upgrading represent the application of the state-of-the-art technology in the industry. With a period of development, the related technology of traditional manufacturing factories for automobiles are found with some common issues while improving. As such, the reconfigurable intelligent manufacturing factory of the automobiles is fast developed with focus on reconfigurability in structures, manufactures and algorithms, thus advancing the level of the reconfigurable intelligent manufacturing continuously. With a sufficient reconfigurable manufacturing technology as the basis, reconfigurability can be better introduced to the structure design and the driving algorithms of the automobiles. Reconfigurability provides a new bridge for transforming the traditional automobile to the reconfigurable automobile, and a new driving force for the upgrading of the reconfigurable driving algorithms.

Manufacturing Resource Scheduling Based on Deep Q-Network

To optimize machine allocation and task dispatching in smart manufacturing factories, this paper proposes a manufacturing resource scheduling framework based on reinforcement learning (RL). The framework formulates the entire scheduling process as a multi-stage sequential decision problem, and further obtains the scheduling order by the combination of deep convolutional neural network (CNN) and improved deep Q-network (DQN). Specifically, with respect to the representation of the Markov decision process (MDP), the feature matrix is considered as the state space and a set of heuristic dispatching rules are denoted as the action space. In addition, the deep CNN is employed to approximate the state-action values, and the double dueling deep Q-network with prioritized experience replay and noisy network (D3QPN2) is adopted to determine the appropriate action according to the current state. In the experiments, compared with the traditional heuristic method, the proposed method is able to learn high-quality scheduling policy and achieve shorter makespan on the standard public datasets.

ESD-Event Detector for ESD Control Applications in Semiconductor Manufacturing Factories

An electrostatic discharge (ESD)-event detector has been designed and fabricated in a single chip to detect and alarm the ESD events in semiconductor or integrated circuit (IC) manufacturing environments. The experiment measured results showed that the peak-to-peak voltage of the detected signal during an ESD event has a strong correlation with its ESD-stress voltage level. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The proposed ESD-event detector can determine a</b> detected <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signal to be an ESD pulse if its signal amplitude is higher than the settable threshold and its duration time is under 500 ns.</b> The ESD-event detector circuit, including a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">450 MHz</b> logarithmic amplifier, a comparator, and a time discriminator, has been implemented in a single chip with a total silicon area of only 693 × 563 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathbf{m}}^2$</tex-math></inline-formula> and fabricated by 0.18- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m CMOS process. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The detector can detect high-frequency transient signals up to 450 MHz, which has been successfully verified in the field tests by detecting the signals generated from the ESD generators, the human-body model tester, and the field-induced charged-device model tester.</b> The proposed ESD-event detector can efficiently perform the real-time ESD monitoring applications in the IC and semiconductor manufacturing factories.

STUDY OF DEVELOPMENT STRATEGY OF SELF-OWNED BRAND PASSENGER VEHICLE OF DONGFENG UNDER THE NEW ENERGY VEHICLE ERA

With the continuous development of China, energy security and environmental protection gradually become national strategic issues, and the automotive industry as the national economic development foundation and pillar industry is affected by such two national strategic issues. To get rid of the dependence on imported petroleum and achieve energy conservation and emission reduction, it is inevitable for China to strive to develop new energy vehicle. In recent ten years, the new energy vehicle industry has developed rapidly with the policy support of the government of China. The Dongfeng Motor Group as traditional automobile factory, under the tide of new energy vehicle, bears the double attack from the new power of vehicle manufacturing and traditional automobile factory, and confronts the double pressure of product transformation and improvement of self-owned brand's market share. The self-owned brand passenger vehicle of Dongfeng is researched in the PESTE analysis, FIVE FORCES analysis and SWOT analysis, and some new suggestions concerning product strategies and market strategies are offered through interview survey, so that Dongfeng Motor Corporation Passenger Vehicle Company can grasp the development opportunity to twist the adverse situation in new situation and can provide certain consideration and reference for the future development of other motor corporations.

Peran Pabrik Pembuatan Kerupuk Laras Terhadap Ekonomi Masyarakat di Desa Arisan Deras

The purpose of this study was to analyze the role of the Laras cracker manufacturing factory in the community's economy in Arisan Deras Village. The research method used is a qualitative method that is carried out directly. Data collection techniques were carried out using observation, interviews, and documentation. The data analysis technique used is the descriptive analysis technique. The results showed that the existence of the Laras cracker manufacturing factory contributed to the economic welfare of the surrounding community because the community was involved in the manufacture and sale of Lara's crackers. The strategy used by factories in marketing their products uses several strategies, namely a product strategy by producing products that consumers are interested in, a price strategy that is not too expensive, a promotion strategy that is carried out by promoting directly or word of mouth so that it can attract consumers, a plan to empower human resources namely the human labor needed to produce cracker products, and product sales strategies online. All of the strategies implemented by the business owner can affect sales of Lara's crackers so that they can directly prosper the surrounding community who work in the factory.

Comparing Modern Manufacturing Tools and Their Effect on Zero-Defect Manufacturing Strategies

The aim of most manufacturing and production factories can be defined as achieving smart and sustainable long-term production systems, which means moving towards strategies defined by Zero-Defect Manufacturing strategies with many areas of improvement, such as: lowering overall cost and energy consumption, amount of scrapped output and wasting raw material and improving overall lead times, production status overview, and planning abilities. These facts were the motivations behind the writing of this paper. The authors considered if the classical architecture of Zero-Defect Manufacturing can be improved by additional tools conventionally used in modern manufacturing. The authors have selected Advanced Planning and Scheduling software tools. To prove that different scheduling methods can have a serious impact on overall production results, we prepared a simple case base comparing different scheduling rules. The theoretical basis for writing this manuscript was prepared by studying classical ZDM methodology and defining the industry gap. The methodology is based on the Zero-Defect Manufacturing architecture, which is essential for high-level implementation in industrial practice. Adding new tools, such as Scheduling and the Industrial Internet of Things, to the classic ZDM architecture improves overall methodology. The impact of different scheduling strategies, which is also described in this study, depends on industry and working conditions. The scheduling rules were compared by several key performance indicators, such as lead time and the number of late/unfinished orders. The study realized, with the practical accent shown this challenge, further research in connection with digitalization.

Variation in Road Dust Heavy Metal Concentration, Pollution, and Health Risk with Distance from the Factories in a City-Industry Integration Area, China.

Road dust samples around three typical factories, F1, F2, and F3, in the National Zhengzhou Economic and Technology Development Zone (ZETZ), China, were collected to study the variation in heavy metal concentration (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn), pollution, and health risk with distance from the factories. The results indicated that the concentrations of all the elements near F1 were higher than near both F2 and F3. Apart from Co, Mn, and Cu in some dust samples, all the element concentrations were higher than the corresponding background values (BCs), to varying degrees. The spatial distributions of the heavy metals surrounding the factories followed the normal distribution. The peak values of element concentrations occurred at 300~400 m away from the factories, except for Hg, which continued increasing more than 500 m away from the factories. The fluctuation curves of the pollution load index value calculated according to the BCs for F1, F2, and F3 all had two peaks, a "small peak" and a "large peak", appearing at about 30 m and 300 m, respectively. For the hazard index and the total carcinogenic risk, the peak values all appeared at 400 m, with the curves following the normal distribution. Exposure to road dust containing non-carcinogenic and carcinogenic elements around F1 was greater than around F2 or F3. In conclusion, our results provide a reference for pursuing effective prevention of dust heavy metal pollution around modern manufacturing factories.

머신비전 기반의 가전제품 표면결함 자동검출 시스템

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

A Development of Welding Tips for the Reflow Soldering Process Based on Multiphysics

A reflow soldering process (RSP) is generally implemented in advanced manufacturing factories for welding small electronic components together to create a product using heat generated at the welding tip (WT). Improper WT design and operating conditions may lead to defects in some products; therefore, optimizing both is immensely significant in developing the RSP. Accordingly, this article proposes a successful RSP development based on multiphysics in a hard disk drive factory consisting of transient thermal-electric and structural simulations. First, a new shape series WT was designed, and a conventional shape, parallel WT, was considered as a case study. Then, they were assembled and experimented with the RSP actual operating conditions to collect essential data. Next, the heat transfer was determined using a transient thermal-electric simulation (TES). The simulation results showed uneven WT temperatures depending on applied voltages, time, and shapes, which were consistent with the experimental results. The higher the applied voltage, the greater the temperature generated at the WT. Finally, after using TES results as loads, the structural simulation showed WT total deformations, which could be consistent with actually occurring defects. The findings from this research are a new design of series WT and proper multiphysics methodology for developing the RSP.

Ventilation improvement and evaluation of its effectiveness in a Japanese manufacturing factory

A coronavirus disease 2019 (COVID-19) cluster emerged in a manufacturing factory in early August 2021. In November 2021, we conducted a ventilation survey using the tracer gas method. Firstly, we reproduce the situation at the time of cluster emergence and examined whether the ventilation in the office was in a condition that increased the risk of aerosol transmission. Secondly, we verified the effectiveness of the factory’s own countermeasure implemented immediately after the August cluster outbreak. Furthermore, we verified the effectiveness of several additional improvement measures on the factory’s own countermeasures already installed in August. Under the conditions of the cluster emergence, the air changes per hour (ACH) value was 0.73 ACH on average. The ACH value was less than 2 ACH recommended by the Ministry of Health, Labour, and Welfare, suggesting an increased risk of aerosol transmission. The factory’s own countermeasures taken immediately in August were found to be effective, as the ACH value increased to 3.41 ACH on average. Moreover, it was confirmed that additional improvement measures on the factory’s own countermeasures increased the ACH value to 8.33 ACH on average. In order to prevent the re-emergence of COVID-19 clusters due to aerosol infection in the office, it was found that while continuing the factory’s own countermeasure, additional improvement measures should also be added depending on the number of workers in the room. In a company, it is important that workers themselves continue to take infection control measures autonomously, and confirming the effectiveness of the measures will help maintain workers’ motivation. We believe it is helpful that external researchers in multiple fields and internal personnel in charge of the health and safety department and occupational health work together to confirm the effectiveness of conducted measures, such as in this case.

Energy and Carbon Loss Management in an Electric Bus Factory for Energy Sustainability

Increasing energy need brings people one step closer to energy saving and alternative sources. The search for reliable and sustainable resources also contributes greatly to technological progress. Thanks to advanced technology, energy use becomes more efficient. However, the concept of sustainability emerges in the field of energy as in other sectors. It is expected that electric vehicles, which have started to increase in interest in recent years, will make a positive contribution to issues such as environmental protection and sustainable energy. In addition, production centers for electric vehicles should also be sustainable and sensitive to carbon emissions. In this study, energy and carbon loss assessment was carried out in an electric bus manufacturing factory. With the help of linear regression analysis, the data of the last 3 years were evaluated. Moreover, as a result of internal audits, energy loss points were determined. As a result of this study, sustainability, carbon loss, and energy management issues in an electric vehicle production factory were evaluated and guiding qualities for today's and future technology companies were revealed.

An Index to Assess Overwork-Related Adverse Effects on Employees Under the Occupational Safety and Health Act in Taiwan

BackgroundThe present study aimed to digitally evaluate the risk of overwork-related adverse effects (OrAEs) among employees from various occupational categories in Taiwan. MethodsAnonymous data of employees from seven companies/factories providing occupational health services were analyzed. The studied population comprised 5505 employees, and the data analyzed included employment duration, working hours, shift work schedules, and health checkup results. The risk for OrAEs was assessed by an index, Karo index (0-4, the larger the value, the higher the risk for OrAEs) obtained using a risk matrix made up of cardiocerebral and occupational risk factors. Karo index values of 3 and 4 were categorized as at high risk for OrAEs (h-OrAEs). ResultsThe 5505 employees had an average employment duration of 8.5 years and a mean age of 39.4 years. The prevalence rates for h-OrAEs of the seven companies/factories ranged from 3.9% to 34.2%. There were significant differences in prevalence rates for h-OrAEs between employees of retail stores and high-tech manufacturing factories. Multivariate analysis results indicated that workers of high-tech manufacturing factories had significantly higher risk for h-OrAEs compared with retail store workers. ConclusionIn terms of satisfying health risk management and legal requirements in Taiwan, the newly issued Karo index, which covers a wide range of occupational risk factors, can serve as an assessment and a warning tool for managing the risk of OrAEs in workplaces. To reduce risks for h-OrAEs, active and prudent control of cerebrocardiovascular risks and working hours is recommended.

A novel data-driven optimal chiller loading regulator based on backward modeling approach

This paper proposes a new method termed backward modeling approach (BMA) to achieve optimal chiller loading (OCL) for reducing energy consumption in industries running multiple-chillers with different efficiency. The developed OCL regulator (OCLR) based on novel BMA approach is composed of conditional generative network (cGAN) and deep neural network (DNN). Most works on the optimal chiller loading problem are to find out the setting of partial load rate (PLR) for each chiller. However, PLR for each chiller cannot be controlled directly and can only be achieved through setting chilled water supply temperature. A novel feedback control framework was developed to identify the relationship between chilled water supply temperature and the PLR. In light of this, the control instruction for chilled water supply temperature can be set to achieve the desired energy saving. The practical feasibility of loading optimization based on developed OCLR was evaluated by conducting field validation for 1 year in a reputed panel manufacturing factory based in Taiwan running multiple-chiller system. From the experimental results, it is evident that the developed data-driven OCLR based on BMA has very high performance and was able to conserve significant energy in the range of 81.9 MWh to 198 MWh per year.

High-Speed Shift Register with Dual-Gated Thin-Film Transistors for a 31-Inch 4K AMOLED Display.

In this work, a promising dual-gated thin film transistor (TFT) structure has been proposed and introduced in the shift register (SR)-integrated circuits to reduce the rising time. The threshold voltage can be simultaneously changed by the top gate and the bottom gate in the proposed dual-gated TFTs. When the SR circuits start to export the scan signals in the displays, the driving currents in the SR circuits are increased by switching the working station of driving TFTs from the enhancement characterization to the depletion characterization. Subsequently, the detailed smart spice simulation has been used to study the function of the proposed SR circuits. In the next step, the proposed SR circuits have been fabricated in a G4.5 active-matrix organic light-emitting diode manufacture factory. The simulated and experimental results indicate that the shift register pulses with the full swing amplitude can be obtained in the SR circuits. Moreover, in contrast to the conventional SR circuits employing with the single-gated TFTs, it has been found that the rising time of the output signals can be reduced from 3.75 μs to 1.23 μs in the proposed SR circuits with the dual-gated TFTs, thus exhibiting the significant improvement of the driving force in the proposed SR circuits. Finally, we demonstrated a 31-inch 4K AMOLED display with the proposed SR circuits.

Zero-shot surface defect recognition with class knowledge graph

Convolutional neural networks (CNNs)-based methods achieve excellent performance on surface defects that belong to the same base classes from a training set, which plays an essential role in ensuring product quality in manufacturing systems. However, in the real world, novel classes of defects always exit due to the complexity and changes of the manufacturing environment. These novel classes are never seen during the training stage but are required to be correctly classified. This paper proposes a class knowledge graph (ZS-CKG) method to address the zero-shot problem in real-world surface defect recognition. In the proposed ZS-CKG method, the class knowledge graph construction method (CKGC) is proposed to construct a class knowledge graph to establish the relationship between base and novel defect classes. Then learns class features by using a graph convolutional neural network. The ZS-CKG utilizes the transformer encoder with capturing long-range dependencies to extract features of defect samples to obtain discriminative defect image features, due to the fact that industrial defects have different shapes and sizes. The experimental results on the public NEU-CLS dataset and real engineering dataset printed circuit boards (PCB) surface defects collected from an actual manufacturing factory demonstrate that the proposed method can effectively address zero-shot surface defect recognition. The ZS-CKG achieve an accuracy of 60.91% and 50.53% on the NEU-CLS and PCB datasets, respectively, which increase by 33.82% and 2.36% compared to the best competing method.

Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning

In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in real time and each job may contain several special operations in partial-no-wait constraint due to technological requirements. In this regard, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named hierarchical multi-agent proximal policy optimization (HMAPPO) is developed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO contains three proximal policy optimization (PPO)-based agents operating in different spatiotemporal scales, namely, objective agent, job agent, and machine agent. The objective agent acts as a higher controller periodically determining the temporary objectives to be optimized. The job agent and machine agent are lower actuators, respectively, choosing a job selection rule and machine assignment rule to achieve the temporary objective at each rescheduling point. Five job selection rules and six machine assignment rules are designed to select an uncompleted job and assign the next operation of which together with its successors in no-wait constraint on the corresponding processing machines. A hierarchical PPO-based training algorithm is developed. Extensive numerical experiments have confirmed the effectiveness and superiority of the proposed HMAPPO compared with other well-known dynamic scheduling methods. Note to Practitioners—The motivation of this article stems from the need to develop real-time scheduling methods for modern discrete flexible manufacturing factories, such as aerospace product manufacturing and steel manufacturing, where dynamic events frequently occur, and each job may contain several operations subjected to the no-wait constraint. Traditional dynamic scheduling methods, such as metaheuristics or dispatching rules, either suffer from poor time efficiency or fail to ensure good solution quality for multiple objectives in the long-term run. Meanwhile, few of the previous studies have considered the partial-no-wait constraint among several operations from the same job, which widely exists in many industries. In this article, we propose a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO uses three DRL-based agents to adaptively select the temporary objectives and choose the most feasible dispatching rules to achieve them at different rescheduling points, through which the rescheduling can be made in real time and a good compromise among different objectives can be obtained in the long-term schedule. Extensive experimental results have demonstrated the effectiveness and superiority of the proposed HMAPPO. For industrial applications, this method can be extended to many other production scheduling problems, such as hybrid flow shops and open shop with different uncertainties and objectives.

Many-Objective Evolutionary Algorithm With Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job Shop Scheduling With Limited Workers.

Because of COVID-19, factories are facing many difficulties, such as shortage of workers and social alienation. How to improve production performance under limited labor resources is an urgent problem for global manufacturing factories. This work studies an energy-efficient job-shop scheduling problem with limited workers. Those workers can have multiskills. A many-objective model with five objectives, that is: 1) makespan; 2) total tardiness; 3) total idle time; 4) total worker cost; and 5) total energy, is built. To solve this many-objective optimization problem (MaOP), a novel fitness evaluation mechanism (FEM) based on fuzzy correlation entropy (FCE) is adopted. Two construction methods for reference points are proposed to build the bridge between MaOP and a fuzzy set. Based on FCE and cluster methods, an environmental selection mechanism (ESM) is proposed to achieve a balance between solution convergence and diversity. With the proposed FEM and ESM, two many-objective evolutionary algorithms are proposed to solve MaOP. The effect of FCE-based FEM and ESM on the performance of algorithms is verified via experiments. The proposed algorithms are compared with four well-known peers to test their performance. The extensive experimental results show that they are very competitive for the considered many-objective scheduling problem.

Association between shift rotation and 30-year Framingham risk of cardiovascular disease among male workers in a medium-sized manufacturing factory.

Rotating shift work is associated with an increased risk of cardiovascular disease (CVD). This study compared the CVD risk score in 129 male line workers aged 22-49 years on different shifts in a medium-sized metal production factory from 2017 to 2020. We classified workers into four groups: permanent day shift, weekly rotation involving five consecutive nights, weekly rotation involving 3-4 consecutive nights, and monthly rotation involving two consecutive nights. We used the Framingham Risk Score to estimate the 30-yr risks of general and hard CVD (CVD risk estimates). We investigated the differences in CVD risk estimates between different groups using linear mixed models. The average 30-yr Framingham CVD risk estimates of each group ranged from 17.5% to 31.2% for general CVD and from 10.5% to 20.5% for hard CVD. Workers on weekly rotations involving 3-5 consecutive nights had 5%-10% significantly higher CVD risk estimates than workers on the permanent day shift. Workers on weekly rotations also had 6%-8% higher BMI-based CVD risk estimates than those on the monthly rotation involving two consecutive nights. While 24-h shift rotations are unavoidable, our findings underscored the potential CVD risk among workers on weekly rotations involving more consecutive nights.

Energy-saving intelligent manufacturing optimization scheme for new energy vehicles

Abstract With the rapid development of the automobile industry in recent years, China has become a big country of automobile production and consumption. The development of China’s auto industry is currently at an important turning point, and accelerating the development of new energy vehicles has become a major national strategic measure. This research mainly discusses the energy-saving intelligent manufacturing optimization scheme of new energy vehicles. With the rapid growth of the global car ownership, from the perspective of sustainable development, the automobile industry must solve the four major automobile public hazards in the world: energy, pollution, safety and congestion. In view of this, the state regards energy-saving and new energy vehicles as the main strategic direction for the transformation and upgrading of China’s automobile industry. This paper uses industrial engineering theory and lean management concept, and through the application of new intelligent manufacturing mode, realizes the design and manufacture of energy-saving and new energy vehicle lightweight body, and creates a reliable, comprehensive and feasible manufacturing collaborative management MES platform. It solves the problem of integration and interconnection of enterprise information platforms, realizes the intelligence and flexibility of stamping and welding automated production lines, shortens the development cycle, and meets the diverse needs of products. The first is to organize research on lean production theory, analyze the scope of application of theoretical tools, summarize theoretical tools suitable for lightweight intelligent manufacturing of automobiles, and provide theoretical support for lean and intelligent manufacturing research and on-site improvement; the second is to integrate and optimize the production process and process design through quantitative analysis and qualitative analysis, integrating theories such as production takt management and pull production, and analyze and study future on-site improvements, and discuss the lean path of smart factories; the third is to plan and design technical routes such as stamping and welding according to the process flow optimized by digital simulation, propose an overall solution for lean intelligent factory, analyze the advanced and innovative nature of the solution, and compare the comprehensive indicators with the expected goals, to evaluate the research results. During the research process, the energy consumption of output value was reduced by 8% than expected. This research helps to provide new ideas for intelligent manufacturing optimization of new energy vehicles. In the design of the program route of the intelligent manufacturing factory, the concept of lean production is introduced in the whole process, in order to provide a useful reference for similar enterprises in the industry in the application practice of intelligent manufacturing + lean production.

Prevalence of Parkinsonism Among Foundry Workers in an Automobile Manufacturing Factory in Tehran.

BackgroundManganese, as an essential element, has neurotoxic effects on basal ganglia and causes parkinsonism, dystonia, and cognitive symptoms in exposed individuals. Transcranial sonography (TCS) is a noninvasive and easily accessible imaging modality for detecting the accumulation of trace elements in the basal ganglia.MethodologyIn a cross-sectional study of foundry workers of one of the automobile manufacturing companies in 2019, the prevalence of parkinsonism was assessed through neurological examination and brain parenchymal sonography or TCS. The prevalence of parkinsonism according to age, smoking, work experience, marital status, and exposure to manganese was determined.ResultsAmong 83 male workers, the prevalence of parkinsonism according to neurological examination, substantia nigra hyperechogenicity on TCS, lentiform nucleus hyperechogenicity, and totally was 33.7%, 9.6%, 10.8%, and 42.2%, respectively. The association between the prevalence of parkinsonism and age, smoking, work experience, marital status, and manganese exposure was evaluated. Parkinsonism according to lentiform nucleus hyperechogenicity was associated with smoking (odds ratio [OR] (95% confidence interval [CI]) = 26.63 (2.38-178.71)) and work experience (OR (95% CI) = 7.18 (0.84-61.32)).ConclusionsAccording to this study, the prevalence of parkinsonism based on neurological examination or brain sonography findings was 42.2%. The implementation of this combined screening method might facilitate earlier detection of affected individuals among manganese-exposed workers.