Mechanical Manufacturing Process Research Articles

Application of the FMECA method on the electro-hydraulic system for drilling machine type NKH45

This article focuses on the implementation of a preventive maintenance strategy, responding to a major challenge: the search for optimization in minimizing downtime of the radial drilling machine (NKH45). Crucially positioned at the heart of the mechanical manufacturing process, this machine holds significant influence on overall production performance and availability. In consideration of the strategic importance of this equipment within the framework of the mechanical manufacturing activity, an advanced methodology, in particular FMECA (Failure Mode Effects and Criticality Analysis), is implemented. FMECA represents one of the most recommended approaches in this context, based on reliability, allowing the meticulous identification of the main causes of functional failures of equipment. FMECA is a risk prevention method. It is used to improve the reliability of a product, a process or even a means of production. It makes it possible to assess the criticality of potential failures in a system. According to the recommendations of the maintenance department of the company specializing in the construction of agricultural equipment, this study will focus on a subsystem of the electro-hydraulic part of the NKH45 radial drilling machine, we use the FMEA Analysis method (Failure Mode Effects and Criticality Analysis). Thanks to this approach, it is possible to identify the main reasons for equipment breakdowns that affect production and to classify these breakdowns in order to develop an optimal maintenance strategy aimed at: reducing the number of breakdowns, preventing breakdowns, improving the preventive maintenance, reduce downtime, improve corrective maintenance, therefore the ultimate objective of this approach is to design an optimal maintenance action plan in order to significantly minimize downtime, which slows down production flow.

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.

Effects of shearing processing on the quality and magnetic properties of electric steel sheet

Mechanical manufacturing process of electric motor induces a residual stress, which increases the iron loss, and affects the efficiency and performance of machine. In this paper, a shear test was carried out on a non-oriented electrical steel sheet with a thickness of 0.50 mm and a relative clearance range of 2–12% of the thickness, and the shear force was detected during shearing process. The sheared surface morphology, microhardness distribution and magnetic properties after the shearing processing were also tested. The obtained experimental results indicate that: as the shear clearance increases, the shear force increases, the plastic deformation zone enlarges, and the flow of microstructure becomes intense. In addition, as the shear clearance increases, the depth and work-hardening degree of the work-hardened layer increase, resulting in degraded magnetic properties of the electrical steel. Moreover, when the lateral clearance is 5%, a complete and smooth shear section can be obtained.

Innovative Mechanical Manufacturing Process of Black Sulphide of Mercury vis’-a-vis’ Kajjali, an Official Ayurvedic Formulation, Augmented with Physicochemical Characterisation

Innovative Mechanical Manufacturing Process of Black Sulphide of Mercury vis’-a-vis’ Kajjali, an Official Ayurvedic Formulation, Augmented with Physicochemical Characterisation

Automated image-based geometric modelling of thick laminates with three-dimensional ply wrinkles

Ply wrinkles are commonly induced during thermal–mechanical manufacturing process of laminated composite structures or components. Fibre misalignment due to ply wrinkles compromises the structural integrity of a composite structure. The damage criticality can be assessed numerically based on non-destructive testing results. Geometric modelling is usually required and it is time-consuming if complex wrinkles have to be incorporated into the finite element model. This study presents an automated image-based three-dimensional geometric reconstruction method for thick laminates with complex ply wrinkles. The geometry of a thick laminate is reconstructed based on the limited inspection images from the sides of laminates. The two-dimensional geometric reconstruction of the detected wrinkled plies is done using a multiple Gaussian functions model or linear interpolation, while the intermediate plies are defined by distance-based interpolation. Based on the reconstructed two-dimensional geometries, the three-dimensional ply wrinkles are modelled via an inverse distance weighting interpolation method. To verify the proposed method, a thick glass fibre laminate with artificially induced wrinkles is manufactured and the cross-sectional geometries of the reconstructed model are compared well with and the realistic inspection data. Progressive failure analysis of a two-dimensional and a three-dimensional reconstructed model are compared in terms of damage progression and load versus nominal strain response to highlight the relevance of modelling three-dimensional wrinkles to obtain more accurate and efficient damage criticality assessments.

Moisture-Sensitive Response and High-Reliable Cycle Recovery Effectiveness of Yarn-Based Actuators with Tether-Free, Multi-Hierarchical Hybrid Construction.

Yarn-based muscle actuators are highly desired for applications in soft robotics, flexible sensors, and other related applications due to their actuation properties. Although the tethering avoiding release of inserted twist, the complex preparation process and harsh experimental conditions make tether-free structures yarn actuator with reliable cycle recovery effectiveness is needed. Herein, a tether-free, multi-hierarchical hybrid construction of a moisture-sensitive responsive yarn-based actuator with the viscose/PET ratio (VPR) = 0.9 exhibited a contraction stroke of 83.15%, a work capacity of 52.98 J·kg-1, and an exerting force of 0.15 MPa. Additionally, the maximum cycle recovery rate of 99% is comparable to that of human skeletal muscles, confirming the advantages of a two-component hybrid structure. The underlying mechanism is discussed based on geometric characterization and energy conversion analysis between the actuation source and the spring frame. The mechanical manufacturing process makes it simple to expand the structurally stable yarn muscles into fabric muscles, opening up new opportunities to advance the usage of yarn-based actuators in smart textiles, medical materials, intelligent plants, and other versatile fields.

Cascading trade‐off studies for robotic deburring systems

Advanced manufacturing technologies play an important role in keeping production in high-cost countries. Due to their flexibility, robot-based solutions have been one of the major enablers in establishing advanced manufacturing capabilities in the traditional high-cost countries. This paper concerns the problem of automated deburring of cast parts. If performed manually, this operation introduces major health, safety, and environmental (HSE) concerns. As such, removal of highly variable casting burrs in the Norwegian context requires a solution based on robots, smart sensors, and advanced algorithms to tackle the problem in a flexible and cost-effective way. Due to the complexity of the task, one is confronted with a breadth of various alternatives to choose from when realizing the desired functionality. These alternatives expand as one considers a pipeline of the subtasks involved in the process. The decisions made in each step cascade throughout the whole pipeline. To tackle the complexity while developing a robotized deburring system, a systemic approach based on cascading trade-off studies is proposed in this paper. This paper is also a contribution to the gap in the literature for cases of trade-off studies in the domain of mechanical engineering. The SPADE (stakeholders, problem formulation, analysis, decision making, and evaluation) methodology has been used as a framework to resolve automation of complex mechanical engineering manufacturing process decisions. The systems engineering (SE) approach proved useful to identify and prioritize the stakeholders and their needs, as well as analyzing the different alternatives in a complex engineering system when dealing with cascading trade studies.

A Manufacturing Driven Design Methodology to Lightweighting of the Structural Elements of a Permanent Magnet Electrical Machine Rotor

It is common practice for electrical machines to be designed with a focus predominantly on the electromagnetic performance. The mechanical design is then subsequently undertaken to meet various mechanical performance criteria, with this finalised design then passed on for manufacture. In many cases, the manufacturing constraints have not been taken fully into consideration during the electromagnetic or more particularly the mechanical design and hence some changes are required to the design to make it manufacturable. This in turn can lead to a non-optimal machine being manufactured. If manufacturing constraints and opportunities are regarded as key quantitative inputs to the design process, not only can these post-design changes be minimised, but benefits of the manufacturing process can be exploited to produce a superior product. This paper proposes a methodology to fully integrate the mechanical design and manufacturing process routes into electrical machine design and illustrates its utility within the context of light-weighting of a permanent magnet rotor for an aerospace electrical machine. Within the proposed methodology, several alternative configurations of the rotor are considered with applicable manufacturing routes identified at the initial stages of design and an analytical design procedure to fulfil the application and performance criteria is developed. Different manufacturing materials and constraints as well as the impact of manufacturing process are identified to be embedded into the design procedures. Finally, the paper demonstrates an application of this methodology together with a discussion of the features which offer promise in achieving economically lighter weight design through integration of manufacturing into the design procedure.

Intelligent Malfunction Identification Method in Mechanical Manufacturing Process Based on Multisensor Data

Current technology trends have been gradually integrated into the production of all walks of life, which play an indispensable part in promoting the intelligent development of enterprises, and have brought a greater impact on production and reformation. With the rapid development of the economy and technology, the manufacturing industry has played a very important role. For this reason, the introduction of artificial intelligence into machinery manufacturing can not only improve production efficiency but also save labor and reduce labor costs. The application of artificial intelligence in machinery manufacturing has a critical good role in promoting industrial upgrading and transformation. This time, through the application of smart algorithms in machinery manufacturing and its automation, we expect that such a technological revolution can provide a new development prospect for the development of manufacturing intelligence and automation. Taking the malfunction identification of string striking machinery as an example, this paper studies the smart identification method of mechanical malfunction based on multisensor. In the process of malfunction identification of keyboard stroke machinery, the accuracy of malfunction identification results is low due to the influence of the identification model. Moreover, a malfunction identification and analysis method for keyboard stroke machinery based on BP optimized by GA is proposed. The mechanical data of keyboard chords are acquired by sound‐sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. The results show that the intelligent identification method proposed has exhibited strong adaptability and superiority compared with the traditional method.

Application of Artificial Intelligence Technology in Decision-Making of Mechanical Manufacturing Process

The traditional mechanical manufacturing process is to transform all raw materials into the final materials and products and directly into the international market all the production process, in this process we involved a lot of problems about decision-making methods, decision-making process is a most basic production technology activity, it is widely exists in the whole social life and each link of enterprise production. This paper studies the decision-making method of mechanical manufacturing process based on artificial intelligence, optimizes the process parameters of plastic integrated mechanical manufacturing process, and compares it with the traditional decision-making method. Finally, the experimental results are obtained that the traditional decision-based method is reduced by more than 10% in size error. But several experiments, the AI decision-making method appeared deviation, the error results are higher than the traditional decision-making method, which may be objective factors, but also reflects the possibility of instability, in the result of deformation. AI-based decision method performance is higher than the traditional decision-making method, reduce the deformation amount by 3.5%

Analysis of Rational Mechanical Design in Mechanical Manufacturing Process

Analysis of Rational Mechanical Design in Mechanical Manufacturing Process

Quality characteristics of the Masi Peruvian mechanical ventilator manufacturing process.

Three hundred and ten rapid-manufactured mechanical ventilators, named Masi, were produced and validated in Peru, according to applicable standards. From these, a sample of 30 was taken and two ventilation parameters, tidal volume and peak inspiratory pressure, were statically analyzed using control charts and histograms. Results show that several points were outside estimated limits for Shewhart means and ranges charts, which could possibly be due to the quantity of equipment used for data recollection and the fact that the Masi team had over 20 engineers. Nevertheless, Masi ventilators met the tolerance required by their user´s manual and MHRA standard and Peruvian DIGEMID for every parameter.Clinical Relevance-This article shows the performance in the validation stage of the peruvian mechanical ventilator MASI built as an emergency response for the COVID-19 crisis.

Post Chemical Mechanical Planarization (CMP) Cleaning Using Hydrogen Dissolved Water

In current semiconductor manufacturing process, especially chemical mechanical manufacturing (CMP) process, ceria nanoparticle dispersed slurry is well known consumable for various CMP processes such as shallow trench isolation (STI), inter layer dielectric (ILD) CMP process, and etc. After CMP process, the ceria nanoparticles which are the sources of defects must be removed surely before performing the other processes. Typically, Standard clean solutions such as SC-1, SC-2, and etc. are applied to the post CMP cleaning process. These cleaning solutions are still effective to remove the residual ceria particles but there are several disadvantages such as quite harmful for health and need of additional treatments for wastewater. In this study, cleaning efficiency of the H2 gas dissolved water was evaluated comparing with DIW and other standard cleaning solutions. 4x4 cm2 of silicon dioxide film wafers were polished supplying ceria slurry and then immersed into the cleaning solutions such as DIW, standard clean solutions, and H2 dissolved water, respectively. Finally, we quantitively analyzed the residual cerium dioxide on the cleaned wafer surface by using inductively coupled plasma mass spectrometer (ICP-MS). H2 dissolved water removed residual ceria nano particles from the silicon dioxide film considerably comparing to DIW. Figure 1. show residual ceria ion concentration after cleaning process and cleaning efficiency calculated. Figure 1

Assessment of micro-abrasive wear tribological properties of H10 tool-steel under conditions of “constant normal force ⇒ variable pressure” and “constant pressure ⇒ variable normal force”

In industrial applications, tools of H10 tool-steel used in mechanical and metallurgical manufacturing processes, such as rolling, extrusion, forging, sheet metal stamping, milling and turning, frequently present significant levels of micro-abrasive wear, which occurs under specific circumstances of “constant normal force”, resulting in a “variable pressure” physical condition, or “constant pressure”, resulting in a “variable normal force” physical condition. Consequently, results of micro-abrasive wear generated under these tribological conditions can help engineers design materials and tools with a longer working life span when they are working under mechanical and metallurgical manufacturing processes. Observing the relevance of this subject, the purpose of this work is to assess the micro-abrasive wear tribological properties of H10 tool-steel submitted to experimental conditions of “constant normal force ⇒ variable pressure” or “constant pressure ⇒ variable normal force”. Ball-cratering wear tests were conducted on quenched and tempered AISI H10 tool-steel; the counter-body was a quenched and stress-relieved AISI 52100 bearing steel sphere and the abrasive slurries were prepared mixing abrasive particles of silicon carbide with distilled water. During the micro-abrasive wear tests, the abrasive slurry was, continuously, agitated and fed between the sphere and the tested material; values of coefficient of friction were, simultaneously, acquired, as well. After the tests, each wear crater was analyzed by optical microscopy, SEM and CAD software. The results were discussed based on the behavior of the “Steady-State Wear Regime”, “wear volume”, “wear rate”, “micro-abrasive wear modes”, “pitch between grooves”, “coefficient of friction” and, finally, validated by ANOVA. With the attainment of the Steady-State Wear Regime, it was possible to present the following conclusions: (i) wear volume and wear rate increased under conditions of “constant pressure ⇒ variable normal force”, (ii) there was the predominance of “grooving abrasion”, with the action of “micro-rolling abrasion” wear phenomenon, (iii) the pitch between grooves presented, approximately, the same value of the average abrasive particles size and (iv) the coefficient of friction remained in a tribological condition classified, statistically, as “constant”.

Noise and vibrations in machine tools

Mechanical manufacturing processes and their production machinery are noisy. The noise always stems from transient forces that excite the structure to vibrations, emitting sound waves into the environment due to vibrations on the surface. Noise emission is today considered as a quality defect of machinery and thus methods and means are sought to mitigate the noise emission to a level compatible with human operators. On the other hand as the noise origin is the process itself, the emitted noise has a signal content that directly indicates the status of the process and sometimes also the health state of the machine and its elements. Noise reduction today is still fairly experience based, which requires generating a good understanding of noise generation, noise transmission and noise radiation and is discussed in this paper. Measurement of noise and its analysis are indispensable for the traceback to the origin to focus the countermeasures but also to reveal the signal content out of the sound waves. Noise prediction is still at the threshold of industrial application, but will in future play a major role for including noise topics into the design from the beginning and not only as corrective measures after the machine is built. Available simulation tools are presented in this paper. The greatest progress in this respect stems from room acoustics and the automotive industry, where the requirements are much stricter than in the manufacturing environment. Ways and achievements in the utilization of noise signals for process and machine health monitoring will also be presented. The industry seeks here for broad band sensors, able to carry large amounts of easy to retrieve signals to reduce the number of additional sensors. Typical standards today collect countermeasures being applied for spreading the knowledge generated for intuitive approaches. The paper also shows future trends and developments and the research required in that field.

Research on Mechanical Design and Manufacture Based on Multi-Agent and Its Automation Application

As far as China's current economic development is concerned, economic development depends heavily on machinery manufacturing. Machinery manufacturing has provided great help for China's economic development, but the traditional machinery design and manufacturing can no longer meet the requirements of social development in the new era. For traditional mechanical design and manufacture, there has also been a historic change. The impact of new technical methods on traditional mechanical design and manufacture has also promoted the development of mechanical design and manufacture towards automation, which has become a mainstream trend of mechanical design and manufacture at present. In this paper, the theme of mechanical design and manufacture and its automation application based on multi-agent is discussed. Firstly, the background of this paper is briefly introduced, and then the advantages of mechanical design and manufacture and its automation application are analyzed. Then, the application of automation technology in mechanical manufacturing process is analyzed from different aspects, and the future development is prospected.

Modeling and Key Technologies of Intelligent Assembly System for Mechanical Products

Manufacturing is country's pillar industries and the core driving force of national economic development. Among them, the machinery manufacturing industry provides technical equipment for the development of the whole country, which plays a decisive function of the development of the national economy and the progress of science and technology. As the increased competition in the global market, the machinery manufacturing industry is facing unprecedented severe challenges. Assembly has a great impact on product quality and the characteristics of complex operating processes, as it is the last part of the mechanical product manufacturing process and therefore often determines the level of mechanically manufactured products. The purpose of this paper is to improve the intelligence level of assembly systems through the development and application of Internet of Things technology. Take the mechanical product assembly system as the research object, the research methods are as follows: First, on the basis of analyzing the connotation and characteristics of manufacturing associations, proposed the manufacturing environment of the Internet of Things. Then, constructed the overall operation framework, network physical environment and topology structure are of the system, and refined the key technologies of system modeling. Object-oriented Petri nets and VisObjNet simulation techniques are used for abstract modeling and simulation systems. Finally, developed the prototype of the intelligent assembly system of mechanical products under the Internet of Things background, and further verified the feasibility and effectiveness of the method proposed in this paper. The research results show that the prediction accuracy of the online monitoring system can reach 98.8% by collecting acceleration and noise in real time. The efficient and green processing parameter optimization system greatly optimizes the original processing efficiency. The method proposed in this paper effectively improves the transparency of the mechanical product assembly process. It provides a reference model and implementation path for building a smart assembly system for mechanical products under the IoT environment.

Design, manufacturing and plasma nitriding of AISI-M2 steel forming tool and its performance analysis

Mechanical manufacturing processes aim to transform metallic materials into mechanical parts and components. In these processes, tools are used for cutting, punching and stamping. The ending of the useful lifetime of these tools happens with wear of its cutting edge. Such forming tools can be manufactured by several materials including AISI-M2 high-speed steel steels, and their surface properties can be modified by thermal and thermochemical treatments. In this study, an attempt is made to design and manufacture a forming tool in the laboratory without usage of computer numerical control machines. Afterwards, the manufactured forming tools are plasma nitrided at various temperatures (400–500 °C) and obtained results are compared with existing tool provided by nails manufacturing factory. The forming tools after plasma nitriding show a significant hardness improvement, increased nails production (used in civil construction), and better wear resistance as compared to the existing tool, particularly at 400 °C. The forming tool obtained in this study is easy-to-manufacture, eco-friendly plasma treatment and cost-effective, and thus it is expected that it can resolve company's technical issue regarding premature wear and tear, and rapid replacement of tool in nails manufacturing.

Research on Mechanical Design Based on Rationalization of Mechanical Manufacturing Process

The advancement of science and technology in China has promoted the economic take-off. As an important part of the secondary industry, machining and manufacturing play an irreplaceable and important role, which is widely used in major fields. Many industries are moving towards the production of fully automobile machinery. The use of advanced automobile mechanical manufacturing technology has greatly improved the productivity, reduced manpower input and costs while ensuring product quality, and achieved the goal of maximizing economic benefits.

A Review of Manufacturing Process Control

AbstractSmart manufacturing concepts are being integrated into all areas of manufacturing industries, from the device level (e.g., intelligent sensors) to the efficient coordination of business units. Vital components of any manufacturing enterprise are the processes that transform raw materials into components, assemblies, and finally products. It is the manufacturing process where smart manufacturing is poised to make substantial impact through process control, i.e., the intelligent manipulation of process variables to increase operation productivity and part quality. This article discusses three areas of manufacturing process control: control-oriented modeling, sensing and monitoring, and the design and construction of controllers. The discussion will center around the following manufacturing processes: machining, grinding, forming, joining, and additive. While many other important processes exist, the discussions of control of these mechanical manufacturing processes will form a framework commonly applied to these processes and the discussion will form a framework to provide insights into the modeling, monitoring, and control of manufacturing processes more broadly. Conclusions from these discussions will be drawn, and future research directions in manufacturing process control will be provided. This article acknowledges the contributions of two of the pioneering researchers in this field, Dr. Yoram Koren and Dr. Galip Ulsoy, who have made seminal contributions in manufacturing process control and continued to build the body of knowledge over the course of many decades.