Structural Scheme Optimization Design for the Stationary Platen of a Precision Plastic Injection Molding Machine

The current development of precision plastic injection molding machines mainly focuses on how to save material and improve precision, but the two aims contradict each other. For a clamp unit, clamping precision improving depends on the design quality of the stationary platen. Compared with the parametric design of stationary platen, structural scheme design could obtain the optimization model with double objectives and multi-constraints. In this paper, a SE-160 precision plastic injection molding machine with 1600 kN clamping force is selected as the subject in the case study. During the motion of mold closing and opening, the stationary platen of SE-160 is subjected to a cyclic loading, which would cause the fatigue rupture of the tie bars in periodically long term operations. In order to reduce the deflection of the stationary platen, the FEA method is introduced to optimize the structure of the stationary platen. Firstly, an optimal topology model is established by variable density method. Then, structural topology optimizations of the stationary platen are done with the removable material from 50%, 60% to 70%. Secondly, the other two recommended optimization schemes are given and compared with the original structure. The result of performances comparison shows that the scheme II of the platen is the best one. By choosing the best alternative, the volume and the local maximal stress of the platen could be decreased, corresponding to cost-saving material and better mechanical properties. This paper proposes a structural optimization design scheme, which can save the material as well as improve the clamping precision of the precision plastic injection molding machine.

IMPROVE THE KINETIC PERFORMANCE OF THE PUMP CONTROLLED CLAMPING UNIT IN PLASTIC INJECTION MOLDING MACHINE WITH ADAPTIVE CONTROL STRATEGY

The kinetic characteristics of the clamping unit of plastic injection molding machine that is controlled by close loop with newly developed double speed variable pump unit are investigated. Considering the wide variation of the cylinder equivalent mass caused by the transmission ratio of clamping unit and the severe instantaneous impact force acted on the cylinder during the mold closing and opening process, an adaptive control principle of parameter and structure is proposed to improve its kinetic performance. The adaptive correlation between the acceleration feedback gain and the variable mass is derived. The pressure differential feedback is introduced to improve the dynamic performance in the case of small inertia and heavy impact load. The adaptation of sum pressure to load is used to reduce the energy loss of the system. The research results are verified by the simulation and experiment, The investigation method and the conclusions are also suitable for the differential cylinder system controlled by the traditional servo pump unit.

Hydraulic cylinder control of injection molding machine based on differential evolution fractional order PID

Injection molding machine,hydraulic elevator,speed actuators belong to variable speed pump control cylinder system.Because variable speed pump control cylinder system is a nonlinear hydraulic system,it has some problems such as response lag and poor steady-state accuracy.To solve these problems,for the hydraulic cylinder of injection molding machine driven by the servo motor,a fractional order proportion-integration-diferentiation(FOPID)control strategy is proposed to realize the speed tracking control.Combined with the adaptive differential evolution algorithm,FOPID control strategy is used to determine the parameters of controller on line based on the test on the servo-motor-driven gear-pump-controlled hydraulic cylinder injection molding machine.Then the slef-adaptive differential evolution fractional order PID controller(SADE-FOPID)model of variable speed pump-controlled hydraulic cylinder is established in the test system with simulated loading.The simulation results show that compared with the classical PID control,the FOPID has better steady-state accuracy and fast response when the control parameters are optimized by the adaptive differential evolution algorithm.Experimental results show that SADE-FOPID control strategy is effective and feasible,and has good anti-load disturbance performance.

Kansei Engineering Applied to the Form Design of Injection Molding Machines

This study investigated the relationship between a subject’s evaluation of injection molding machines (IMMs) and formal design features using Kansei engineering. This investigation used 12 word pairs to evaluate the IMM configurations and employed the semantic differential method to explore the perception of 60 interviewees of 12 examples. The relationship between product feature design and corresponding words was derived by multiple regression analysis. Factor analysis reveals that the 12 examples can be categorized as two styles—advanced style and succinct style. For the advanced style, an IMM should use a rectangular form for the clamping-unit cover and a full-cover for the injection-unit. For the succinct style, the IMM configuration should use a beveled form for the safety cover and a vertical rectangular form for the clamping-unit cover. Quantitative data and suggested guidelines for the relationship between design features and interviewee evaluations are useful to product designers when formulating design strategies.

A Case Study of Heat Transfer from Hot Runner Mould to the Fix Platen of the Injection-moulding Machine

In an injection moulding process, the parallelism b et ween the tie bars of the injection moulding machine is very important as it will affect the mould closing and clamping system. In recent years, more and more ho t runner systems are being applied in the moulding industry to save material and decrease the losses of injection pressure. Heat transfer from hot runner system from the fixed half which is secured in the fix machine platen could transmit s o much heat that it may cause high temperature differential between the machine fix platen and moving platen. This will cause the tie bar to become unparallel. Part quality will be compromised and the wear of the tie bar will be excessive. Overhaul of the tie bar may be necessary after a short period of time which is c ostly. This raises the need to analyze the heat transfer from the hot runner sys tem to the machine fix platen and the methods of isolating or minimizing the hea t transfer. In this case study, a photo lens article mould was used. The mould w as built with a direct hot runner nozzle system. Heat conduction from hot runner and machine screw to machine fix platen were studied based on either using high temperature heat insulating plate put in placed between the mould and the mould ing machine fix platen or drill cooling channels in the front mould clamping pla te. The high temperature insulator is very costly as it is made out of glass re inforced polymer composite material. Experimental results were obtained and anal yzed to find the best method to minimize the unwanted heat transfer using the ch eapest and most effective method.

Model evolvement and reuse technology of injection molding machine based on performance knowledge

To illuminate the necessity of model evolvement and reuse, dynamics of injection molding machine＇s product models are analyzed. The performance knowledge is used to support the model evolvement and reuse. The driven factors of mechanical product model are concluded. The dynamic characteristics of reuse. Finally, HT1800X1N series injection molding machines are taken as examples to illuminate that the arithmetic is correct and practical.

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

GUARDIAN: A Multi-Tiered Defense Architecture for Thwarting Prompt Injection Attacks on LLMs

This paper introduces a novel multi-tiered defense architecture to protect language models from adversarial prompt attacks. We construct adversarial prompts using strategies like role emulation and manipulative assistance to simulate real threats. We introduce a comprehensive, multi-tiered defense framework named GUARDIAN (Guardrails for Upholding Ethics in Language Models) comprising a system prompt filter, pre-processing filter leveraging a toxic classifier and ethical prompt generator, and pre-display filter using the model itself for output screening. Extensive testing on Meta’s Llama-2 model demonstrates the capability to block 100% of attack prompts. The approach also auto-suggests safer prompt alternatives, thereby bolstering language model security. Quantitatively evaluated defense layers and an ethical substitution mechanism represent key innovations to counter sophisticated attacks. The integrated methodology not only fortifies smaller LLMs against emerging cyber threats but also guides the broader application of LLMs in a secure and ethical manner.

Research on Torque Ripple Under Healthy and Open-Circuit Fault-Tolerant Conditions in a PM Multiphase Machine

In this paper,research into torque ripple production has been undertaken for both the healthy and open-circuit faulttolerant conditions of a five-phase permanent magnet(PM)machine by using the instantaneous power(I-Power)approach.When only the fundamental component of the phase currents is applied to the phase windings,it has been shown that the 9th and 11th harmonics of the back-electromotive force(back-EMF)causes torque ripples in a five-phase PM machine and its frequency is ten times the frequency of the fundamental phase currents.When the combined fundamental and third harmonic components of the phase currents are applied to the phase windings,it has been shown that the 7th and 13th harmonic of the back-electromotive force(back-EMF)causes additional torque ripples in a five-phase PM machine.These torque ripples under fault-tolerant conditions have been analyzed analytically,as well.It has been proven that there are interactions between the fundamental component of current and the third harmonic component of the back-EMF and vice versa.These interactions cause torque ripples.A finite element analysis(FEA)model of the five-phase PM machine has been done to validate the analytical results.

Torque Characteristics of High Torque Density Partitioned PM Consequent Pole Flux Switching Machines With Flux Barriers

Unique double salient structure of Permanent Magnet Flux Switching Machines(PMFSM)with both Concentrated Armature inding(CAW)and Permanent Magnet(PM)on stator attract researcher's interest for high speed brushless application when high torque density(T den)and power density(P den)are the primal requirements.However,despite of stator leakage flux,high rare-earth PM usage,PMFSM is subjected to slot effects due to presence of both PM and CAW in stator and partial saturation due to double salient structure which generates cogging torque(T cog),torque ripples(Trip)and lower average torque(T avg).To overcomne aforesaid demerits,this paper presents Partitioned PM(PPM)Consequent Pole Flux Switching Machine(PPM-CPFSM)with flux barriers to enhance flux mnodulation,curtail PM usage and diminish stator leakage flux which reduces slotting effects and partial saturation to ultimately reduces T cog and Trip In comparison with the existing state of the art,proposed PPM-CPFSM reduces 46.5390 of the total PM volumne and offer Tavg higher up to 88.8%,suppress Trip naximun up to 24.8%,diminish Tcog up to 22.74%and offer 2.45 times Tden and Pden.Furthermore,torque characteristics of proposed PPM-CPFSM is investigated utilizing space harmonics injection i.e.inverse cosine,inverse cosine with 3rd harmonics and rotor pole shaping techniques i.e.,ecce ntric circle,chanfering and notching.Detailed electromagnetic perfornance analysis reveals that harmonics injection suppressed Tcog maximun up to 83.5%,Trip up to 40.72%at the cost of 4.71%Tavg.Finally,rotor mnechanical stress analysis is utilized for rotor withstand capability and 3D-FEA based Coupled Elctromagnetic Thermal Analysis(CETA)for thermal behavior of the developed PPM CPFSM.CETA reveals that open space along PPM act as cooling duct that inprove heat dissipation.

Self-Adaptive Stepsize Affine Projection Based Parameter Estimation of IPMSM Using Square-Wave Current Injection

Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make sure that the results of parameter estimation are convergent.In order to solve such problem,self-adaptive stepsize affine projection algorithm for parameter estimation of IPMSM is proposed in this paper.Compared with traditional affine projection algorithm,this method can obtain the stepsize automatically based on the operation condition,which can ensure the convergence and celerity of the process of parameter estimation.Then,on the basis of self-adaptive stepsize affine projection algorithm,a novel parameter estimation method based on square-wave current injection is proposed.By this method,the error of estimated parameter caused by stator resistance,linkage magnetic flux and dead-time voltage can be reduced effectively.Finally,the proposed parameter estimation method is verified by experiments on a 2.2-kW IPMSM drive platform.

Application of supervised machine learning to predict the enhanced gas recovery by CO_(2) injection in shale gas reservoirs

The technique of Enhanced Gas Recovery by CO_(2) injection(CO_(2)-EGR)into shale reservoirs has brought increasing attention in the recent decade.CO_(2)-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design.Nevertheless,more challenges arise when simulating and predicting CO_(2)/CH4 displacement within the complex pore systems of shales.Therefore,the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO_(2) injection to shale reservoirs.In recent years,machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems.Thus,this work proposes a solution by developing a supervised machine learning(ML)based model to preliminary evaluate CO_(2)-EGR efficiency.Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations.In this work,linear regression and artificial neural networks(ANNs)implementations were considered for predicting the incremental enhanced CH4.Based on the model performance in training and validation sets,our accuracy comparison showed that(ANNs)algorithms gave 15%higher accuracy in predicting the enhanced CH4 compared to the linear regression model.To ensure the model is more generalizable,the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model.Among ANNs models presented,ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination(R2)of 0.78 compared to the linear regression model with R2 of 0.68.Our developed MLbased model presents a powerful,reliable and cost-effective tool which can accurately predict the incremental enhanced CH4 by CO_(2) injection in shale gas reservoirs.

Generation of Rate-of-Injection (ROI) profile for Computational Fluid Dynamics (CFD) model of Internal Combustion Engine (ICE) using machine learning

Injector configuration and spray characteristics are important parameters that define diesel combustion and emissions performance. One of the critical spray inputs is the Rate-of-Injection (ROI) profile. The ROI profile depends on the spray’s operating conditions, including nozzle geometry (e.g., nozzle diameter), injection pressure, and injection duration. Besides, the internal nozzle flow phenomenon and external ambient conditions can further impact fuel introduction characteristics. This study measured the ROI profile of a heavy-duty (multi-hole) diesel injector using the Bosch tube technique. Injection pressure and injection duration were varied from 600 to 2600 bar and 0.5–3.0 ms, respectively. After post-processing, measurement data were then used to train numerical models, including a developed machine learning (ML) model that can create very similar ROI profiles with experimental data. Next, a Computational Fluid Dynamics (CFD) simulation used the ROI profile generated by ML model. For comparison, there are other simplified ROI profiles used in similar CFD simulation configuration. Results showed that the any difference in ROI profiles could affect the combustion and emissions significantly. This further emphasizes the need to provide high-fidelity spray input in terms of ROI profile for CFD simulation. The current ML model can deliver a realistic ROI profile for any given rail pressure and injection duration.

Automated process parameters tuning for an injection moulding machine with soft computing

In injection moulding production,the tuning of the process parameters is a challenging job,which relies heavily on the experience of skilled operators.In this paper,taking into consideration operator assessment during moulding trials,a novel intelligent model for automated tuning of process parameters is proposed.This consists of case based reasoning (CBR),empirical model (EM),and fuzzy logic (FL) methods.CBR and EM are used to imitate recall and intuitive thoughts of skilled operators,respectively,while FL is adopted to simulate the skilled operator optimization thoughts.First,CBR is used to set up the initial process parameters.If CBR fails,EM is employed to calculate the initial parameters.Next,a moulding trial is performed using the initial parameters.Then FL is adopted to optimize these parameters and correct defects repeatedly until the moulded part is found to be satisfactory.Based on the above methodologies,intelligent software was developed and embedded in the controller of an injection moulding machine.Experimental results show that the intelligent software can be effectively used in practical production,and it greatly reduces the dependence on the experience of the operators.

我国土壤消毒机械的研发与应用

土壤处理机械是土壤消毒的核心技术。综述了在国家重点研发计划等项目的资助下,我国突破了一系列技术瓶颈,研发了固态、液态和火焰消毒技术。土壤消毒秸杆还田一体机采用偏心结构和反旋土壤提升技术,作业深度可至40 cm土层,在相同用量下,相比手撒棉隆,草莓增产60.4%,生姜增产26.9%~53.1%。采用土壤消毒秸杆还田一体机相同构造的土壤提升技术,研发了以天然气或丁烷作为燃料的火焰消毒机,箱体温度400~600℃,持留2~3 s,处理后对土壤根结线虫防效可达到100%,对杂草的防效在86.6%以上。火焰消毒对土壤病原真菌的防效为63.2%~80.8%,研发的小型广角电喷式注射消毒机,具有左右摇摆结构,使土壤消毒机械转弯时仍能均匀施药。我国独创的这3种施药机械在山东、安徽、河北、云南等地得到了广泛的应用,取得了良好的经济效益和社会效益。

草鱼疫苗连续自动注射装置设计与试验

草鱼疫苗注射主要依靠人工注射,具有劳动强度大、注射效率低、注射位置非标准化和注射精准度差等缺点,严重制约水产养殖的发展。根据草鱼疫苗注射特点,制定了疫苗自动注射作业策略,通过开展鱼苗姿态自动识别与快速调整、注射工位自动分配、鱼体输送与拨杆LS-DYNA动力学仿真分析、鱼体柔性固定与快速注射等技术研究,研制了疫苗自动注射样机,以100~120 mm长的草鱼鱼苗为自动注射对象进行试验。结果显示:草鱼疫苗自动注射装置产量约为900尾/h,试验鱼苗注射疫苗后3周内的存活率达到98.3%,存活率较人工疫苗注射提高4个百分点。该装置实现了草鱼幼苗的高效连续注射,提高了水产养殖装备自动化水平,对纺锤形鱼幼苗的连续注射均具有参考意义,具有较高应用价值与经济效益。

基于IAOA-KELM的储气库注采管柱内腐蚀速率预测

针对储气库注采管柱的内腐蚀速率预测问题,建立了基于阿基米德优化算法(Archimedes Optimization Algorithm,AOA)与核极限学习机(Kernel Extreme Learning Machine,KELM)相结合的模型提高腐蚀速率预测精度。通过引入佳点集、改进密度降低因子、采用黄金正弦算法缩小搜索空间,提高局部开发能力,利用改进阿基米德优化算法(Improved Archimedes Optimization Algorithm,IAOA)优化KELM正则化系数(C)和核函数参数(γ),进而建立IAOA-KELM储气库注采管柱内腐蚀速率预测模型;使用MATLAB软件运用该模型对某注采管柱内腐蚀数据集进行学习与预测,将IAOA-KELM模型与KELM、粒子群优化算法(Particle Swarm Optimization,PSO)-KELM、AOA-KELM结果进行预测误差对比。结果表明,IAOA-KELM模型的预测值与实际值较为拟合,其E RMSE为0.65%,E MAE为0.39%,R 2为99.83%,均优于其他模型。研究表明,IAOA-KELM模型能够更为准确地预测储气库注采管柱内腐蚀速率,为储气库注采管柱的运维及储气库的健康管理提供参考。

射流与搅拌协同式浮选机气体引射规律探究

为了探究射流与搅拌协同式浮选装置在射流与搅拌协同作用下的气体引射规律,建立了射流与搅拌耦合区域模型,分别模拟了射流速度变化、搅拌速度变化、射流速度与搅拌速度同时变化对装置吸气能力的影响,搭建了射流与搅拌协同式浮选装置并对其进行试验。结果表明,射流速度是影响装置吸气能力的主要影响因素,搅拌速度对装置吸气能力产生一定的影响;射流与搅拌协同作用下浮选装置的吸气能力取决于射流速度与搅拌速度的相对贡献;射流速度与搅拌速度的增大对喷嘴的吸气能力分别产生促进与抑制的效果;当射流速度带来的增益效果大于搅拌速度带来的抑制效果时,提高装置吸气量的同时也促进了物料的混合,反之则降低装置的吸气量;随着搅拌速度的不断增加,叶轮剪切射流束的剪切力与剪切面积逐渐减小,射流束不再冲击叶轮,此时装置的吸气能力取决于射流速度大小。射流与搅拌协同式浮选装置在流量8.2m^(3)/h以上工作时,搅拌速度对喷嘴吸气能力影响较小;当在流量8.4m^(3)/h、转速7.5m/s的条件下工作时,此时喷嘴的吸气能力得到质的提高并且物料混合效果较好,浮选装置具有较好的工作能力;当搅拌速度达到8.75r/s以上时,射流束逐渐发生轨迹偏移,装置吸气能力取决于射流速度且射流束不再冲击驱动叶轮。

基于STM32的微流控芯片离心进样机设计

针对某种微流控芯片在使用过程中需要保证微腔室全部充满样品液的要求,设计了基于STM32单片机的离心进样机。硬件方面,选用STM32F103ZET6型单片机作为控制器,选用ECMAC20602RS型伺服电机作为驱动电机,选用TK6051iP型触摸屏作为人机交互界面,设计了设备的整体硬件结构。软件方面,编写了基于Modbus协议的串口通信程序,设计了触摸屏人机交互界面,设计了设备的控制程序流程。通过实验对设备的功能进行了测试,实验结果表明,当转速和时间分别设置为1500 r/min和120 s时,微腔室全部充满样品,并且没有出现液体渗漏,满足了微流控芯片的进样要求。