Machining Line Research Articles

Failure Mechanism of Rear Drive Shaft in a Modified Pickup Truck

This paper investigates the failure mechanism of the rear drive shaft in a modified pickup truck which had operated for about 3000 km. The investigation included macroscopic and microscopic evaluation to document the morphologies of the fracture surface, measurement of the material composition, metallographic preparation and examination, mechanical testing, and finite element modelling and calculations. The results obtained suggest that rotation-bending fatigue was the primary cause of the drive shaft failure. The crack initiation is located in the root of the machined threads on the drive shaft surface and expanded along the side of the machining line surface. The main cause of fatigue cracks is attributable to a high stress concentration owing to a large unilateral bending impact under overload. Meanwhile, the bidirectional torsional force also produces a higher stress concentration and thus accelerates the fatigue crack to expand radially along the surface. Furthermore, the hardness of the central section of the drive shaft was marginally below standard. This deficiency results in harm to the bearings and other mechanical components, as well as expediting the enlargement of cracks. Finite element analysis revealed significant contact stress between the bearing and drive shaft, with stress levels exceeding the fatigue limit stress of the parent material. This highlights the need for reevaluation of the heat treatment process and vehicle loading quality to enhance the drive shaft’s longevity.

The Effect of Seismic Load on TBM Structures for Metro Line 3 in Hanoi, Vietnam

Metro line 3 in Hanoi is being constructed using tunnels, which may affect nearby constructions. In addition, Hanoi’s earthquakes also pose risks to this Tunnel Boring Machine (TBM) line. This paper studies the impact of TBM construction on the surrounding houses along the line, as well as the impact of earthquakes on this line. A 3D numerical model was developed to simulate the static and dynamic behavior of TBM at station 9 in Metro Line 3. The results show that the maximum settlement at the ground surface under seismic load is significantly reduced from 4.6 mm to 0.1 mm when using the jet grouting method.

Account of additional factors for damping torsional oscillations

Some parameters are influenced by the turbine unit's torsional oscillations. The fundamental comes from damping these oscillations, which are brought on by a departure in the turbine blades' speed from the device's prediction of the steam volume, and attenuation of fluctuations due to the distribution of energy in the turbine's productive components. The usual single-machine infinite bus system is used for the analysis. For various turbine-generator shafts and various generator operating situations, rotating mass mechanical system evaluations for small-signal stability and large disturbance are conducted. It is demonstrated that the shaft's “structural' damping (H) and “steam' damping (Kn) coefficients have a considerable impact on the damping of torsional modes. The goal of this work is to determine the effect of changing the damping factors in the mathematical model of the steam turbine shaft on the system's static stability, as well as the extent to which these variables' limits on damping rotational oscillations on the maximum torsional torques generated in the shaft masses. The mathematical model of the steam turbine shaft with a single machine and transmission line to an infinite bus system was simulated using Dymola software, and the static and dynamic effects of damping factors (H) and (Kn) on system stability were demonstrated. By evaluating the best case for parameters with the least influence on the system's stability, the results were obtained by changing the factors (Kn) from 0.005 to 0.5 and (H) from 0.005 to 0.2 and the extent of its effect on the maximum torque of the steam turbine masses and reducing it by 8.4 %, as well as by reducing the settling time of the system after disturbances occur and reaching to Steady state by about 90 %.

Application of Failure Mode and Effect Analysis (FMEA) for Defect Reduction: A Case Study on Scratch Defects in Oil Separator Parts in Machining Line

This study addresses the problem of significant defect rates on Oil Separator Parts in machining line, especially focusing on scratch defects. The defect rate reached 0.495%, exceeding the company's target of 0.1%. In the approach using FMEA, potential failure modes were identified and rated based on Risk Priority Number to plan corrective actions. Effort and Impact Analysis was used to analyze the most effective action recommendations. Implementation of the 5S methodology was selected as the improvement solution, which focused on workplace organization, maintenance, and cleanliness. The results were positive, with the number of defects decreasing dramatically from 540 to 67 units in July 2023, and the defect rate dropping from 0.495% to 0.145%. Although the company's quality targets have not been fully achieved, the FMEA approach and 5S implementation have been shown to significantly improve production quality and reduce defects. This study proves that this approach has the potential to address complex quality issues, although further challenges may be required to achieve the final goals set.

An exact method for machining lines design with equipment selection and line balancing

We consider the context of machining systems design where both equipment selection and line balancing decisions have to be taken in order to minimise the total cost of the system. The designed flow line employs multi-positional machines with rotary tables where vertical and horizontal machining modules can be used for the realisation of machining processes. For this challenging optimisation problem in production research, we develop an innovative mathematical model based on a mixed-integer linear programme and a heuristic algorithm for an approximate solution. An extensive numerical experiment is conducted in order to evaluate the performances of the proposed mathematical model and the developed heuristic. The obtained results show that the decision makers can use the elaborated methods for solving efficiently even large-scale industrial problems.

Towards the Augmentation of Digital Twin Performance.

Digital Twin (DT) aims to provide industrial companies with an interface to visualize, analyze, and simulate the production process, improving overall performance. This paper proposes to extend existing DT by adding a complementary methodology to make it suitable for process supervision. To implement our methodology, we introduce a novel framework that identifies, collects, and analyses data from the production system, enhancing DT functionalities. In our case study, we implemented Key Performance Indicators (KPIs) in the immersive environment to monitor physical processes through cyber representation. First, a review of the Digital Twin (DT) allows us to understand the status of the existing methodologies as well as the problem of data contextualization in recent years. Based on this review, performance data in Cyber-Physical Systems (CPS) are identified, localized, and processed to generate indicators for monitoring machine and production line performance through DT. Finally, a discussion reveals the difficulties of integration and the possibilities to respond to other major industrial challenges, like predictive maintenance.

Laser patterning of large-scale perovskite single-crystal-based arrays for single-mode laser displays

Lead halide perovskites have attracted considerable attention as potential candidates for high-performance nano/microlasers, owing to their outstanding optical properties. However, the further development of perovskite microlaser arrays (especially based on polycrystalline thin films) produced by the conventional processing techniques is hindered by the chemical instability and surface roughness of the perovskite structures. Herein, we demonstrate a laser patterning of large-scale, highly crystalline perovskite single-crystal films to fabricate reproducible perovskite single-crystal-based microlaser arrays. Perovskite thin films were directly ablated by femtosecond-laser in multiple low-power cycles at a minimum machining line width of approximately 300 nm to realize high-precision, chemically clean, and repeatable fabrication of microdisk arrays. The surface impurities generated during the process can be washed away to avoid external optical loss due to the robustness of the single-crystal film. Moreover, the high-quality, large-sized perovskite single-crystal films can significantly improve the quality of microcavities, thereby realizing a perovskite microdisk laser with narrow linewidth (0.09 nm) and low threshold (5.1 μJ/cm2). Benefiting from the novel laser patterning method and the large-sized perovskite single-crystal films, a high power and high color purity laser display with single-mode microlasers as pixels was successfully fabricated. Thus, this study may offer a potential platform for mass-scale and reproducible fabrication of microlaser arrays, and further facilitate the development of highly integrated applications based on perovskite materials.

基于 Arduino 的小型蔬菜播种机远程控制系统设计与性能试验

Facilities greenhouses are relatively narrow and airtight, and the machine operation turns frequently. The existing vegetable seeders is supported by manual hand, and the straightness of operation is poor, Turning around is time-consuming and laborious. This paper designs a remote control system of small vegetable seeder based on Arduino microcontroller, the Arduino single-chip microcomputer remote control technology, multi-motor synchronous drive technology, differential-electric push rod combined with lifting and steering control and other methods are adopted, Through remote control, the forward, speed regulation, lifting of the whole machine and field line change and steering of the seeder during vegetable sowing are realized. The differential steering model and brushless motor speed control model are established, and the android human-computer interaction APP is designed to realize the remote precise control of vegetable seeder. The performance test of the accuracy of the control system was carried out with the accuracy and steering accuracy of the seeder. The results showed that : under the condition of medium speed and medium and low speed operation, the seeding level of vegetable seeder is the most stable and reliable, the speed deviation rate of the seeder is 1.59 %, after starting the drive motor for 2s, the actual speed is close to the target speed, differential steering is relatively accurate, the qualified rate of steering is more than 89.8 %, the synchronous speed error of the lifting mechanism tends to be stable.This study can provide reference for the development of intelligent equipment for facility agriculture.

Intelligent machine plus production line digital twin model construction technology

Digital twin is one of the key technology of intelligent digital manufacturing field, the related research is still in the initial stage This article in complex parts machining production line as the research object, from the geometric physical behavior rules four aspects to explore the digital twin modeling technology of production line Through the geometric model and the size of the physical model visualization expression line shape Based on the three-layer model of geometric physical behavior, the digital twin behavior model is constructed to describe the operation failure rule of the machine and unit. Driven by real-time information collection and fusion technology, closed-loop control and iterative optimization are realized Finally, the application value of digital twin technology in intelligent monitoring information feedback decision control is demonstrated through the intelligent control system of machining production line.

Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems.

Effect of Combined Processing Lines on Production Efficiency and Productivity

The production process usually involves several processes that are divided into production lines. The processes in this production line affect the costs incurred by the company. From the analysis results, the costs arising from production line activities are very high. Therefore, the company strives to reduce production costs by paying attention to the aspects that result in the emergence of waste. The method used is by combining the process on the machining line. This study was conducted to find out the effect of combining process lines on production efficiency. The results of this study are expected to be an input in determining production planning in the enterprise. This study didn’t use sampling. From the results of the study, there was an increase in the daily production of gear A (0.86%) and gear B (1.12%). From this merger, the company was able to optimize manpower and cut production WIP storage areas.

A proposal for improving production efficiency of existing machining line through a hybrid monitoring and optimisation process

In a machining line with a Lean manufacturing environment, such as one used for mass production of automotive components, tool life is determined by a tool counter that is set on the safe side to avoid producing a failed product, so the cutting tools are typically underutilised. On the other hand, cutting parameters optimisation, which has a significant impact on tool life, is not feasible due to the rule that prohibits changing the cutting parameters during mass production to maintain the quality standard. To address both issues, this paper proposes a hybrid monitoring and optimisation process that can be carried out concurrently without interfering with production activities. The monitoring process involves two methods, namely threshold limit-based monitoring and machine learning-based monitoring, for ensuring product quality during the optimisation process. Meanwhile, Bayesian optimisation (BO) is used for the optimisation process due to its capability for autonomous search for the optimum condition of an unknown and expensive objective function. The case study demonstrated BO’s ability to find the optimal condition with a small number of samples, while the machine learning method used can monitor tool wear and surface roughness with average errors of 5.5% and 7.7%, respectively.

THE EFFECT OF SLIVER DELIVERY SPEED ON THE UNEVENNES OF CARDED AND COMBED COTTON YARNS

The purpose of this study is to determine the effect of five different sliver delivery speeds on the yarn unevenness CVm (%). For this purpose, 100% cotton raw material was used, and card sliver samples were produced at five different card sliver delivery speeds (60 m/min, 120 m/min, 180 m/min, 240 m/min and 300 m/min). A total of 600 sample cops were produced in the ring spinning machine, each of which is 10 pieces of knitting and weaving yarn types in the carded and combed machine line, with the yarn counts Ne 20/1, Ne 30/1 and Ne 40/1 from these reed strips produced. The quality characteristics of the raw material used and the produced carded sliver samples were measured using the Uster HVI 1000 and Uster AFIS test equipment. The quality characteristics of the ring yarn cops samples produced on the ring spinning machine were measured with the help of Uster Tester 4 and Uster Tensojet Test devices. The results were analyzed by inferential and descriptive analysis methods. ANOVA analysis method was used for statistical analysis. According to the research findings, in both carded and combed Ne 40/1 yarn samples, an increase in yarn unevenness was observed with the increase in card sliver exit speed, and this increase created a statistically significant difference. The results revealed that the yarn unevenness is directly proportional to the card sliver exit speed and the increase of the card sliver exit speed causes the production of card sliver samples with higher unevenness values (Cvm (%).

Caeski: an assistive technology for the communication of persons with deafblindness

Purpose Caeski is a keyboard with 12 vibrating keys that connects to an application via smartphone. This assistive technology aims to facilitate the communication of persons with deafblindness in presential contexts or with people who can be anywhere in the world. The purpose is to present this assistive technology and analyse the viability of its use through tests with eleven persons with deafblindness. Materials and methods The study design consisted of ten days of testing with eleven persons with deafblindness and five interpreters who had the function of passing the information about the content of the tests. Results The tests showed that most participants were able to communicate through Caeski. In addition, the tests showed the need to add the function of repeating the reception of information via vibration to confirm the understanding. The results demonstrated the need for more training time to improve the learning of accented words and long sentences. Therefore, training time and previous contact with technological devices are factors that influence the result of the tests. Conclusion The use of Caeski is feasible and as future perspectives, this assistive technology can be used in association with similar assistive technologies such as Perkins Machine and Braille Line, preventing possible tactile overloads. In addition, can be applied in the educational context, from literacy to university. Studies with longer training time should be conducted to confirm the results. The implications for rehabilitation: Social interactions, presential and online, with deafblind and non-deafblind persons anywhere in the world. Literacy and cognitive development of persons with deafblindness. Digital inclusion for occupational, school or academic contexts.

Application of Lean Six Sigma for Improve Productivity at The Mechanical Plant. A Case Study

This study focuses on performing the analysis of manipulator operations on the machining line of precision mechanical products using the Human-Machine correlation analysis tool through images collected from the camera, wasteful operations incurred in machining according to Lean Six Sigma (LSS) standards to control fluctuations in the machining line, improving the overall productivity of the line (OEE). Specifically, contributing to improving productivity, quality, and competitiveness of the company in the market, create a good product image for consumers. This paper proposes a 7-step quality control (QC) cycle improvement model, called 10 step QC cycle. In step 5, use Man - Machine correlation analysis tool from video images to identify wasteful activities. In step 6, we propose a Direct Numerical Control (DNC) model to call the machining program for MC machines using a barcode system and a computer vision model for human identification at each processing line according to a controlled fixed layout. The right people have been trained enough to operate the line; the specific result is eliminating the occurrence of accidents in processing from 7 cases to none. A model of a product dimensioning system implemented for fully automated product quality control combined with redesigned machining jig with a vapor sensor system eliminating the reliance on human manipulation Specifically, the result from this improvement activity is the increase in productivity from 115 products per 8 hours to 155 products per 8 hours and the handling time has decreased from 1.3 hours per day to 0.36 hours per day (reduce 0.94 hours per day). The Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to analyze the results of the survey of employees' opinions about the usefulness, convenience, and technical factors after the operation. The results from improvement activities show that user loyalty is highly appreciated in terms of usefulness and convenience. However, in terms of technical factors, it is still necessary to improve the quality of the information network system, the barcode scanning system and the quality of barcodes in the oil environment

Enhanced Hybrid Ant Colony Optimization for Machining Line Balancing Problem with Compound and Complex Constraints

Targeted at the machining production line balancing problem, based on the precedence constraint relation of the present machining task, this article suggests adding practical constraints such as advanced station preparations, post-auxiliary tasks, and tool changing. The study introduced ‘tight’ and ’or’ constraints to bring the problem definition closer to the actual situation. For this problem, a mixed-integer programming model was constructed in this study. The model redefines the machining and auxiliary processing tasks and adds new time constraints to the station. The model considers two optimisation objectives: the number of stations and the machining line balancing rate. In view of the complexity of the problem, heuristic task set filtering mechanisms were designed and added to the ant colony optimisation, to satisfy the above compound and complex constraints. The processing task chain was constructed using the rules of ant colony pheromone accumulation and a random search mechanism. The study designed a Gantt chart generation module to improve the usability and visibility of the program. Ultimately, through an actual case study of a complex box part including 73 processing elements and realising the design and planning of machining production lines that meet complex constraints by substituting algorithms, the balance rates of several groups of optimisation schemes were higher than 90%, which showed that the algorithm is effective and has a good economy and practicability.

Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.

Impact of hybrid FACTS devices on the stability of the Kenyan power system

<p>Flexible alternating current transmission system (FACTS) devices are deployed for improving power system’s stability either singly or as a combination. This research investigates hybrid FACTS devices and studies their impact on voltage, small-signal and transient stability simultaneously under various system disturbances. The simulations were done using five FACTS devices-static var compensator (SVC), static synchronous compensator (STATCOM), static synchronous series compensators (SSSC), thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC) in MATLAB’s power system analysis toolbox (PSAT). These five devices were grouped into ten pairs and tested on Kenya’s transmission network under specific contingencies: the loss of a major generating machine and/or transmission line. The UPFC-STATCOM pair performed the best in all the three aspects under study. The settling times were 3 seconds and 3.05 seconds respectively for voltage and rotor angle improvement on the loss of a major generator at normal operation. The same pair gave settling times of 2.11 seconds and 3.12 seconds for voltage and rotor angle stability improvement respectively on the loss of a major transmission line at 140% system loading. From the study, two novel techniques were developed: A performance-based ranking system and classification for FACTS devices.</p>

Towards Real-Time Machining Tool Failure Forecast Approach for Smart Manufacturing Systems

Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.

Design of a new anti-metal RFID temperature tag antenna based on short-circuit stub structure

With the increasing maturity of the power Internet of things (pIoT), radio frequency identification technology (RFID), is more applied to the fault monitoring of power equipment, of which the temperature monitoring of power equipment is one of the focuses of fault monitoring. When the traditional RFID temperature measurement tag is applied to high-voltage electrical equipment, it often produces metallic interference to the RFID temperature measurement tag antenna because it is attached to the surface of metal conductor, such as large synchronous machine and high-voltage transmission line, which greatly shortens the identification range of the traditional RFID temperature measurement tag on the metal surface and even cannot work normally. To solve the problems above, a new anti-metal RFID temperature tag antenna is designed in this paper. The internal antenna of the RFID temperature measurement tag adopts the anti-metal design based on the short-circuit stub structure, so that the temperature measurement tag can achieve the purpose of good anti-metal performance. Finally, through the simulation of HFSS, this paper verifies that the designed RFID temperature measurement tag can not only resist metal effect, but also be well applied to complex metal environment.