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In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry. [ABSTRACT FROM AUTHOR]

The design of three-dimensional printing based conformal cooling channels (CCCs) in injection molding holds great significance. Compared to CCCs, conformal cooling (CC) cavity solutions show promise in delivering enhanced cooling performance for plastic products, although they have been underexplored. In this research, CC cavity is designed within the mold geometry, reinforced by body-centered cubic (BCC) lattice structures to enhance mechanical strength. Three distinct BCC lattice variations have been integrated into the CC cavity: the BCC structure, BCC with cubes, and BCC with pillars. The thermal performances of the BCC lattice-added CC cavity are assessed numerically after experimental validation. To provide feasible solutions from viewpoints of thermal performances, various BCC lattice structure thicknesses are analyzed in the range of 0.8–1.2 mm. Thermal simulation outcomes reveal that thicker lattice structures enhance mechanical strength but simultaneously lead to an increase in cooling time. Upon examining all the proposed CC cavity solutions supported by BCC, the cooling times range from 2.2 to 4 s, resulting in a reduction of 38.6% to 66.1% when compared to conventional straight-drilled channels. In contrast to CCCs, CC cavities have the potential to decrease the maximum temperature non-uniformity from 8.5 to 6 K. Nevertheless, the presence of lattice structures in CC cavity solutions results in an elevated pressure drop, reaching 2.8 MPa, whereas the results for CCCs remain below 2.1 MPa. Graphic Abstract [ABSTRACT FROM AUTHOR]

In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry.

This article discusses a new approach to develop a machine learning model for the cooling profile prediction of plastic parts produced through injection molding to estimate the cooling time. Design parameters related to the cooling stage of injection molding, such as distance between the cooling channel and plastic part, pitch distance, channel diameter, and part thickness, as well as material properties including density, mass, thermal conductivity, and specific heat are considered. A wide range of scenarios is created by considering the design parameters, ensuring a comprehensive analysis. To manage this extensive collection of cases, scripts are used to automate the design and simulation process. The scripts first generate the required cooling channels for each scenario, then simulate the cooling stage of the injection molding process and collect the relevant results. The obtained results are thoroughly analyzed to evaluate the impact of each design parameter on the cooling profile. Then, the physics of the cooling is discussed to estimate the time-dependent temperature of the parts in the plastic injection process. A long short-term memory machine learning model is used to forecast the simulation results, and a regression model is used to predict the cooling profile of the parts. Validation of the regression model is done by calculating the cooling time of an industrial part, produced by plastic injection, and the cooling time of the part is calculated with a 2.67% error. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyse the causes of partial setup work that is not optimal and provide solutions to the sub-optimal setup work on family-type moulds in type A. Methodology/Approach: This study uses an 8-Step improvement approach combined with several methods, including Focus Group Discussion (FGD), Quality Cost Delivery Safety Moral (QCDSM), and Specific Measurable Achievable Relevant Timebase (SMART) method. Findings: This study found the cause of the partial setup work being less than optimal due to problems with the Man, Method, and Machine factors. The results of this study show that the setup time decreased from 240 minutes to 73.4 minutes or 68%. Meanwhile, the process cost for the partial setup itself decreased from $75 to $3 or decreased by 97%. Research Limitation/implication: This research has limitations in the partial setup process, which is less than optimal in plastic injection family-type moulds in the Motorcycle Industry. Originality/Value of paper: This research has value in optimising the production of plastic injection family-type moulds by reducing setup time and operational costs for making spare parts in the Motorcycle Industry. Category: Case study. [ABSTRACT FROM AUTHOR]

It is known that plastic materials, separated from the circular economy, are divided into small pieces over many years and create risks by mixing with nature, seas, freshwater resources, and terrestrial ecosystems in micro dimensions. It is thought that micro-size powder material and production scraps not used in Additive Manufacturing (AM) production processes will turn into a waste problem in the future in parallel with the increasing usage intensity. In this direction, this study presents a new and sustainable usage model within the scope of recycling Laser Powder Bed Fusion (LPBF) wastes. In the study, granule materials obtained from AM waste material mixtures with different parameters are recommended to be recycled by using them to produce functional plastic parts in the automotive industry plastic injection systems. In this context, materials recycled with different methods and function tests in automotive company acceptance standards are shared. [ABSTRACT FROM AUTHOR]

The plastic injection moulding industry has been growing and expanding rapidly. Cost evaluation is important to business operations and numerous aspects influence part pricing. However, the intricacy of production data often includes distinctive factors that result in inaccurate and a long waiting time. Therefore, the objective of this research is to propose a cost evaluation approach that combines a 3-dimension convolutional neural network (3D-CNN) with an artificial neural network (ANN) to improve the accuracy of complex geometry from a dense voxel of convolutional neural network (CNN) that can disentangle the difficulty of primary cost evaluation. The methodology consists of 3D-voxelization adopted to 3-dimension convolutional neural network (3D-CNN) and the feature extraction of complex geometry to feature parameters using the learning ability of artificial neural network (ANN) to achieve better accuracy. Then, bulk price analysis is developed for multi-price in-depth for multi-volumes. These results can predict cost evaluation at about 98.65% accuracy for parts costs, 95.17% accuracy for mould costs, and 96.83% of multi-price for multi-volume. The contribution of this research is based upon a new hybrid deep learning using 3-dimension convolutional neural network (3D-CNN) with artificial neural network (ANN) that is practical and accurate in performing cost evaluation multi-prices for multi-volumes for decision-making in the plastic injection industry. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the effect of mold release agent on the Environmental Stress Cracking (ESC), of plastics under injection molding application has been investigated for crack and craze initiation. Recently, polycarbonate which is used as a polymer in this study has attracted great attention due to its advantages such as high durability, impact resistance, and is more economical. During the plastic injection production process, plastics parts experience failure due to severe causes. ESC, which occurs when metallurgical, environmental, and mechanical conditions are combined. Furthermore, a plastic resin is degraded by a chemical agent in the presence of stress. So, the effects of the chemical agent and mechanical stresses cause cracking after some time. It has been determined how chemical agents' consequences critical plastic component failure. Moreover, contamination of chemical agents is an accelerating agent to plastic parts considering ESC mechanism. Molding release agents are selected for questioning as a chemical agent because they are used widely in the industries. Plastic parts that have different mechanical properties are manufactured while using different molding release agents, and craze initiation and propagation stages have been observed experimentally. Therefore, considering ESC, some chemical agents is a factor of increasing crazes to grow more rapidly. [ABSTRACT FROM AUTHOR]

The classical plastic injection method is based on the principle of injecting a single color of a single polymeric material into the mold cavity under high pressure. In cases where the products are expected to have contrasted functional features and different colors, the classic injection process and the conventional injection molds are not sufficient. This paper proposes a new design approach for multi-component injection molds required by products containing different polymeric materials or different colors of the same polymeric material at the same time. It also presents a case study including the design of the hot runner, electromechanical rotary-cross, cooling, and ejection systems of a two-component, eight-cavity toothbrush mold. The polymeric materials are polypropylene for the first component, and styrene based thermoplastic elastomer for the second component, which exhibit good bonding properties with each other. In addition, an analysis study covering the filling parameters and production defect generations is also provided. The adopted design approach provides a production rate of 1600 parts per hour, corresponding to 18 s cycle time and 200 cycles per hour, and makes it sufficient to rotate only the 80 kg core plate instead of 1120 kg entire core side. Compared to existing methods, the results show that the proposed multi-component injection mold design method eliminates the need for particular injection machines and robotic systems, shortens the cycle time, and reduces energy consumption. [ABSTRACT FROM AUTHOR]

In the 20th century, there was a burst concerning the development of the automobile industry, which has become an essential asset for society. With its evolution, this industry created a foundation that based its competitiveness on satisfying people's needs with the highest possible quality and always respecting the delivery deadlines. With the growth in demand, the improvement of certain processes was needed to achieve the desired production goals, accomplished through automation and robotics, as production and assembly lines increasingly used fully automated processes. In plastic injection lines, production is constant and carried out quickly, so it is desirable to perform component assembly steps that immediately support the output of the injection mould parts. This work consists of adapting an obsolete robotic cell to be implemented in one of the production lines to insert components into the injected parts, replacing labour work. Through a mechanical project and an automation design, the equipment was concluded and is currently in production, fulfilling the necessary requirements and improving the process' cycle time. This proves that it is possible to recover old equipment, which is able to improve current tasks and common needs in modern industry, increasing the economic sustainability of the processes and saving resources. [ABSTRACT FROM AUTHOR]

The article describes an optimization methods analysis in the technological process of plastic injection using the simulation tool Autodesk Moldflow Adviser. The Polyethylene 120J was the material used in production of the plastic part analyzed. Following the simulations, deficiencies affecting the quality of the final part were identified. Modifications were gradually made both in the cover's design and as changes in the process parameters so as to eliminate the deficiencies identified. In the first step, the value of the injection time was modified based on the results of the analysis of the determination of the ratio of solidified layers at the end of the process of filling the mold cavity. The second type of adjustment required a change in the molding's design. Based on the result of the simulation of forecasting cooling quality, we proposed a change in the wall thickness of selected model elements, specifically the adjustment of the wall thickness of the bolted joint column from 3 to 4 mm. The last adjustment consisted of adjusting the additional pressure profile. [ABSTRACT FROM AUTHOR]

Since its beginning, lean manufacturing has built a worldwide reputation based on results related to production improvement and cost reduction in several companies. This management philosophy focuses on customer value creation through the elimination of production wastes. Lean methods and techniques have spread their scope from the automotive industry to a wide range of industries and services. This article presents a case study that describes the use of the lean tool value stream mapping in the production process of automotive parts for a major automotive company. At the beginning of the project, relevant data from the process were collected and analysed. Subsequently, the initial process was mapped, the related wastes were identified, and then future processes were mapped and financial results were estimated. The proposals were presented onkaizenmeetings, the action plan was discussed and the decision regarding which option to choose was taken. Consequently, the Cycle Time and the level of the workforce were reduced, the process was improved and savings were obtained. [ABSTRACT FROM PUBLISHER]

One of the many methods used in the production of plastic materials is plastic injection method. The plastic injection method needs equipment difficult to choose, and it is also costly. Many negativities could be experienced during production. One of them is the water droplet problems on the surfaces of the mold. Provision of insufficient cooling on molds, ambient temperature, non-homogenous humidity, mechanical and other stresses occurring during production, cracks on the product, warpage and thermal residual all cause some stress-like defects. In this study, the effect of water droplets problem on production efficiency in the molds of Polyethylene Terephthalate (PET) raw materials manufactured in a private enterprise was examined. The percentage productivity table has been created based on different months of the year, August July and August. In the enterprise, 8 dehumidifiers were placed to 17 injection machines to create a homogeneous production temperature in the environment. Humidity and heat values obtained during production were monitored daily and recorded. In this study, which was conducted over two years and three months, positive results were obtained suggesting that the water droplets formed in injection molds has decreased with the effect of dehumidification devices, and the efficiency of injection production has also increased by 8%. [ABSTRACT FROM AUTHOR]

One of the most common plastic manufacturing methods is injection molding. In injection molding process, scheduling of plastic injection machines is very difficult because of the complex nature of the problem. For example, similar plastic parts should be produced sequentially to prevent long setup times. On the other hand, to produce a plastic part, its mold should be fixed on an injection machine. Machine eligibility restrictions should be considered because a mold can be usually fixed on a subset of the injection machines. Some plastic parts which have same shapes but different colors are used same mold so these parts can only be scheduled simultaneously if their mold has copies, otherwise resource constraints should be considered. In this study, a multi-objective mathematical model is proposed for parallel machine scheduling problem to minimize makespan, total tardiness, and total waiting time. Since NP-hard nature of problem, this paper presents a two-stage mathematical model and a two-stage solution approach. In the first stage of mathematical model, jobs are assigned to the machines and each machine is scheduled separately in the second stage. The integrated model and two-stage mathematical model are scalarized by using goal programming, compromise programming and Lexicographic Weighted Tchebycheff programming methods. To solve large-scale problems in a short time, a two-stage solution approach is also proposed. In the first stage of this approach, jobs are assigned to machines and scheduled by using proposed simulated annealing algorithm. In the second stage of the approach, starting time, completion time and waiting time of the jobs are calculated by using a mathematical model. The performance of the methods is demonstrated on randomly generated test problems. [ABSTRACT FROM AUTHOR]

In this study, the thermal analysis and numeric modelling of flow in nozzle of a real plastic injection machine with injection weight of 300 gram were conducted. The nozzle geometry was changed to optimize flow in nozzle for high-density polyethylene HDPE at temperature of 200 °C and injection pressure of 150 MPa. For numeric modelling, the ANSYS Fluent R14 was used. The analysis was made for four different geometries consisting of real system dimension r-NG and others design dimensions NG1, NG2 and NG3 . The results of the analysis showed that the most suitable flow was determined in the third NG3 design. In this geometry, for flow in the nozzle section, rounding was made in the sudden shrinking flow section to give a throat shape and so, the geometry ensuring optimized flow was obtained. As a result, with changing of nozzle geometries of plastic injection machines having different pushing capacities they were used in the industry, the positive results will be able to be obtained.

In this study, the changes in mechanical, rheological and morphological properties of glass-fiber reinforced poly(oxymethylene) composite after ageing were investigated. Samples were prepared with plastic injection molding method. Ageing process was done by waiting samples at laboratory temperature and water during 28 days. Tensile and 3 point flexural tests were done to determine the mechanical properties, melt flow index (MFI) was measured to determine the rheological property and scanning electron microscopy (SEM) was used to observe the morphological structure. From experimental results, it was seen that mechanical, rheological and morphological properties were affected by the ageing environment. It was found that mechanical properties of aged samples were lower than that of unaged sample. The most decrement in the mechanical properties was occurred at the aged in water samples. Ageing process increased MFI value.

In today's production conditions, it is very important for companies to produce products that meet the determined quality standards. In addition to the production of semi-finished products produced in plastic injection lines with high quality and efficiency, it is aimed to reduce the lost time and waste materials in production. Since automobile headlights can be in many different colors, it is necessary to keep the product quality at a high level in the transition between colors in the production made by plastic injection and to reduce the number of defective products caused by color errors. In this study, the problem of a single row-dependent machine to reduce the amount of waste that occurs between color transitions in the plastic injection line of an automotive side-industry company producing headlights is discussed. Tabu Search method, which is one of the heuristic optimization algorithms was used to solve the problem. In the solution, production sequences that will reduce the amount of waste of the products in the production program were created by using the annual data kept by the production department of the factory. The algorithm was run using data from two machines out of 18, saving 134 kg of waste materials. It has been clarified that if the developed algorithm is applied to all machines in the department, productivity and product quality will be increased by saving waste materials. [ABSTRACT FROM AUTHOR]

In injection molding processes for thermoplastic parts, the polymer solidification phase in the molding cavity has a strong influence on the quality of the shaped parts and also on the process cycle time. Reducing cycle time is one of the major concerns for plastic injection industries. As cooling phase presents the most critical phase to get quality and cycle time of the part, the application of additive manufacturing (AM) technologies has been overcoming the limitations of traditional cooling system design. AM enables the construction of conformal cooling channels for higher cooling uniformity due to its almost unlimited freedom of design that can fulfil the desired functions in injection molding process equipment. The analysis of the heat transfer during the phase of cooling allows the investigation of the optimal positioning of the cold sources and their intensities. In this paper, a systematic approach is used to replace conventional channels in an injection molding tool with conformal cooling channels. A simulation is used to develop a numerical model that describes the heat transfer and predicts the cycle time of both the optimal and conventional designs. Finally, a numerical comparison is made between traditional and conformal cooling to demonstrate the beneficial effect on reducing the manufacturing cycle and enhancing part quality. [ABSTRACT FROM AUTHOR]

Conformal cooling is an additive manufacturing-based solution and it is a rapidly developing method for reducing the cooling time of the plastic injection process. The present study investigates the thermal and mechanical performances of the 3D-printed conformal cooling channels using computational analyses and multi-objective optimization. For a real injection mold product, two different conformal cooling channel profiles, which are circular and elongated, are analyzed individually. Their cooling time, temperature non-uniformity, and pressure drop are assessed. Compared to the traditional channels, the cooling time of designed CCCs is reduced in the range of 30-60%. The cooling and fatigue life performances of the elongated channel are analyzed for different channel pathways and cross section areas. As for the circular channel, the coolant temperature, volume flow rate, and channel diameter are selected as the parameters within the ranges of 288.0-298.0 K, 1.0-10.0 L/min, and 2.1-2.5 mm, respectively. According to these parameters, the multi-objective optimization study is performed and the best trade-off point is found at the channel diameter of 2.5 mm, coolant temperature of 297 K, and the flow rate of 1 L/min when all the objectives have equal weights in the optimization problem. [ABSTRACT FROM AUTHOR]

Nanopattern replication of complex structures by plastic injection is a challenge that requires simulations to define the right processing parameters. Previous work managed to simulate replication for single cavities in 2D and 3D, showing high performance requirements of CPU to simulate periodic trenches in 2D. This paper presents two ways to approach the simulation of replication of complex 3D hydrophobic surfaces. The first approach is based on previous CFD Ansys Fluent and compared to FE based CFD Polyflow software for the analysis of laminar flows typical in polymer processing and glass forming as well as other applications. The results showed that Polyflow was able to reduce computing time from 72 h to only 5 min as desired in the project. Furthermore, simulations carried out with Polyflow showed that higher injection and mold temperature lead to better replication of hydrophobicity in agreement with the experiments. Polyflow simulations are proved to be a good tool to define process parameters such as temperature and cycle times for nanopattern replication.

Goal: Companies are increasingly concerned about the life cycle of their products in the economic and environmental perspectives. One of the processes that generates great financial returns is the injection molding process. But this process results in many impacts to the environment. The environmental concern has generated many studies involving this theme and the difficulty of measuring the impacts generated by this process. In view of the environmental concern and the injection molding process this study aims to review in the literature on eco-design tools based on the Streamlined Life Cycle Assessment of plastic injection molded products.Design / Methodology / Approach: The review of the literature was carried out by researching for complete articles published in three databases (Science Direct, Scopus and Web of Science), among the years 2013 - 2018, using combinations of keywords and Boolean operators.Results: Several studies revealed that injection molding generates many impacts, such as the high consumption of electricity caused by the emission of greenhouse gases and use of raw material.Limitations of the investigation: This paper is not free of limitations. The study analyzed the literature through three databases for a period of time and used a combination of keywords. It may be that some document has not been analyzed.Practical implications: The development of a rapid tool for the application of LCA studies will allow quick decision-making to the managers in the environmental perspective, besides reducing costs, reducing time with the application of complex studies, and assisting the designer to develop the Eco-design.Originality / Value: The study addressed the injection molding process and there are no studies in the literature that characterize this theme. Thus, a study that addresses this gap and evidences the importance of the development of an environmental tool that assists sustainable practices in the process of plastic injection molding becomes important.

Goal: Companies are increasingly concerned about the life cycle of their products in the economic and environmental perspectives. One of the processes that generates great financial returns is the injection molding process. But this process results in many impacts to the environment. The environmental concern has generated many studies involving this theme and the difficulty of measuring the impacts generated by this process. In view of the environmental concern and the injection molding process this study aims to review in the literature on eco-design tools based on the Streamlined Life Cycle Assessment of plastic injection molded products. Design / Methodology / Approach: The review of the literature was carried out by researching for complete articles published in three databases (Science Direct, Scopus and Web of Science), among the years 2013 - 2018, using combinations of keywords and Boolean operators. Results: Several studies revealed that injection molding generates many impacts, such as the high consumption of electricity caused by the emission of greenhouse gases and use of raw material. Limitations of the investigation: This paper is not free of limitations. The study analyzed the literature through three databases for a period of time and used a combination of keywords. It may be that some document has not been analyzed. Practical implications: The development of a rapid tool for the application of LCA studies will allow quick decision-making to the managers in the environmental perspective, besides reducing costs, reducing time with the application of complex studies, and assisting the designer to develop the Eco-design. Originality / Value: The study addressed the injection molding process and there are no studies in the literature that characterize this theme. Thus, a study that addresses this gap and evidences the importance of the development of an environmental tool that assists sustainable practices in the process of plastic injection molding becomes important. [ABSTRACT FROM AUTHOR]

University Research & Development (R&D) laboratories are facilities used for research and manufacturing activities in engineering and basic science fields. Various filling materials are used in plastic R&D laboratories to improve the mechanical properties of thermoplastics or to recycle waste materials. The use of filling materials causes changes in plastic production parameters. This study dealt with the risks arising from extrusion, injection and shredder in a plastic injection laboratory, which led to the inability to manufacture the appropriate product under a holistic methodology. The proposed holistic methodology merges the concepts of failure mode and effects analysis (FMEA), fuzzy best-worst method (FBWM), and a rule-based three-hierarchical Bayesian network (RB-THBN). First, 15 root risks have been identified under a three-stage hierarchy with the support of laboratory decision makers. Then, questionnaires were created using the FMEA concept. The importance values (degree of belief-DoB) of three different FMEA parameters (severity, occurrence and detection) by experts for each risk have been calculated with FBWM. And finally, the RB-THBN has been created, and the final risk values have been computed using the linear utility function for the network. The system has been analyzed via a Bayesian modeling software. A validity test and a control measures planning has also been executed. According to the results, the highest root risk has been determined as "E2-The prepared mixture is not homogeneous" with a crisp risk score of 0.639559. In addition, "Failure due to extrusion" has been determined as the highest risk category with a score of 0.564059. It was discussed in detail that these risks arise from homogenization and how the used mixture should be homogenized. A comparative study among traditional FMEA, FMEA extended under FBWM and the proposed approach is performed to observe the differences in final ranking of root risks and to highlight the arguments that strengthen the advantages of the proposed method over the other two. • Proposed a holistic FMEA methodology under Bayesian Network. • Identified 15 root risks under a three-stage hierarchy in a university plastic injection laboratory. • Computed the importance values of FMEA parameters by FBWM. • Performed a risk assessment by a rule-based three-hierarchical Bayesian network. • Analyzed the system via GeNle and employed a control measures planning. [ABSTRACT FROM AUTHOR]

Copyright of Synthesis of Theoretical & Applied Mechanics / Sinteze de Mecanică Teoretică și Aplicată is the property of Matrix Rom and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Synthesis of Theoretical & Applied Mechanics / Sinteze de Mecanică Teoretică și Aplicată is the property of Matrix Rom and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Plastic industry is today in a constant growth, demanding several products from other segments, which includes the plastic moulds, used mainly in the injection moulding process. This paper presents a methodology for the surface evaluation of plastic moulds, aiming the automation of the polishing surface analysis. Provided that this type of analysis by traditional procedures can be slow and expensive, the development of automatic system could lead to considerable improvements regarding the speed and reliability of information. The starting point of the evaluation procedure is the image generated by the laser light scattered over the sample mould surface that could be captured and analysed by image processing and artificial intelligence techniques. The results showed that the proposed system is able to mapping and classifying several damages over the polished surface and could be an alternative to reduce efficiently the costs and the spending time in mould surface inspection tasks. [ABSTRACT FROM AUTHOR]

An optimization model based on mass balance and energy balance for a blast furnace process is established by using a nonlinear programming method. The model takes the minimum CO2 emission of a blast furnace as optimization objective function, and takes plastic injection or pulverized coal injection into account. The model includes sixteen optimal design variables, six linear equality constraints, one linear inequality constraint, six nonlinear equality constraints, one nonlinear inequality constraint, and thirteen upper and lower bound constraints of optimal design variables. The optimization results are obtained by using the Sequential Quadratic Programming (SQP) method. Comparative analyses for the effects of plastic injection and pulverized coal injection on the CO2 emission of a blast furnace are performed. [ABSTRACT FROM AUTHOR]