Precision Design for Machine Tool Based on Error Prediction

Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.

Investigation into precision engineering design and development of the next-generation brake discs using Al/SiC metal matrix composites

Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive brake discs using nonhomogeneous Al/SiC metal-matrixcomposite materials.The design and development are based on modeling and analysis following stringent precision-engineering principles,i.e.,brake-disc systems that operate repeatably and stably over time as enabled by precision-engineering design.The design and development are further supported by tribological experimental testing and finite-element simulations.The results show the industrial feasibility of the innovative design approach and the application merits of using advanced metal-matrix-composite materials for next-generation automotive and electric vehicles.

Die design for cold precision forging of bevel gear based on finite element method

The finite element analysis （FEA） software Ansys was employed to study the stress state of the dies of both plane and non-plane parting face structures with uniform interference and the die of plane parting face structure with non-uniform interference. Considering the symmetry of the die, a half gear tooth model of the two-ring assembled die with 2.5 GPa inner pressure was constructed. Four paths were defined to investigate the stress state at the bottom comer of the die where stress concentration was serious. FEA results show that the change of parting face from non-plane to plane can greatly reduce the stress at the teeth tips of the die so that the tip fracture is avoided. The interference structure of the die is the most important influencing factor for the stress concentration at the bottom comer. When non-uniform interference is adopted the first principal stress at the comer on the defined paths of the die is much lower than that with uniform interference. The bottom hole radius is another important influencing factor for the comer stress concentration. The first principal stress at the comer of the plane parting face die with non-uniform interference is reduced from 2.3 to 1.9 GPa when the hole radius increases from 12.5 to 16.0 mm. The optimization of the die structure increases the life of the die from 100 to 6 000 hits.

Machine learning of materials design and state prediction for lithium ion batteries

With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

Dynamic Accuracy Design Method of Ultra-precision Machine Tool

Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable and the related research is rarely available. In light of above reasons, a DAD method of ultra-precision machine tool is proposed in this paper, which is based on the frequency domain error allocation.The basic procedure and enabling knowledge of the DAD method is introduced. The application case of DAD method in the ultra-precision flycutting machine tool for KDP crystal machining is described to show the procedure detailedly. In this case, the KDP workpiece surface has the requirements in four different spatial frequency bands, and the emphasis for this study is put on the middle-frequency band with the PSD specifications. The results of the application case basically show the feasibility of the proposed DAD method. The DAD method of ultra-precision machine tool can effectively minimize the technical risk and improve the machining reliability of the designed machine tool. This paper will play an important role in the design and manufacture of new ultra-precision machine tool.

PRODUCT DATA PREDICTION WITH UNCERTAINTY IN PRODUCT LIFE CYCLE DESIGN

Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there still leaves a great deal of data unavailable. This makesdata prediction a valuable work. A method that can predict data of product under development basedon the existing similar products is proposed. Fuzzy theory is used to deal with the uncertainties indata prediction process. The proposed method can be used in life cycle design, life cycleassessment (LCA) etc. Case study on current refrigerator is used as a demonstration example.

A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.

Improvement of System Dynamic Precision by Dynamic Prediction

According to the characteristic of the sensor inertia, the dynamic prediction to improve the system dynamic precision is presented in this paper. With the recurrence calculation of time constant of the sensor, the system dynamic precision is greatly improved. The example using this method is given.

The Relationship between Interchangeability Standards and Precision Design in Teaching a Course on Interchangeability and Measurement Techniques

This article discusses some views on the relationship between carrying out and applying standards and precision design and the teaching of a course on interchangeability and measurement techniques. It points out that while emphasizing precision design, we should not underrate the significance of interchangeability and standardization. Although there are presently many teaching models available for such courses, each course should be designed separately to preserve its systematic character and integrality. As well, the development of students' abilities in precision design and the application of standards should be strengthened in experimental lessons within each course.

Forming-Precision-Driven Structure Design of Hydraulic Press: Methodology and Case Study

The structure stiffness of presses has great effects on the forming precision of workpieces, especially in near-net or net shape forming. Conventionally the stiffness specification of presses is empirically determined, resulting in poor designs with insufficient or over sufficient stiffness of press structures. In this paper, an approach for the structure design of hydraulic presses is proposed, which is forming-precision-driven and can make presses costeffective by lightweight optimization. The approach consists of five steps:(1)the determination of the press stiffness specification in terms of the forming precision requirement of workpieces;(2)the conceptual design of the press structures according to the stiffness and workspace specifications, and the structure configuration of the press;(3)the prototype design of the press structures by equivalently converting the conceptual design to prototypes;(4)the selection of key structure parameters by sensitivity analysis of the prototype design; and(5)the optimization of the prototype design. The approach is demonstrated and validated through a case study of the structure design of a 100 MN hydraulic press.

A machine learning approach for accelerated design of magnesium alloys.Part B: Regression and property prediction

Machine learning(ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface(GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ~80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design.

Design methodology of a mini-missile considering flight performance and guidance precision

The design of mini-missiles(MMs)presents several novel challenges.The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision.The miniaturization of the size of MMs makes the design of the guidance,navigation,and control(GNC)have a larger-thanbefore impact on the main-body design(shape,motor,and layout design)and its design objective,i.e.,flight performance.Pursuing a trade-off between flight performance and guidance precision,all the relevant interactions have to be accounted for in the design of the main body and the GNC system.Herein,a multi-objective and multidisciplinary design optimization(MDO)is proposed.Disciplines pertinent to motor,aerodynamics,layout,trajectory,flight dynamics,control,and guidance are included in the proposed MDO framework.The optimization problem seeks to maximize the range and minimize the guidance error.The problem is solved by using the nondominated sorting genetic algorithm II.An optimum design that balances a longer range with a smaller guidance error is obtained.Finally,lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.

Simplified prediction model for lighting energy consumption in office building scheme design

At the scheme design stage,the potential of daylighting is significant due to the saving for electric lighting use. There are few simple tools for architects to optimize the daylighting design. Therefore,it is useful to develop a design guideline related to the evaluation of lighting energy saving potential and sunlight design strategies. This paper analyzes the impacts of different artificial lighting control methods and design parameters on daylighting. A direct correlation between lighting energy consumption and parameters such as orientations,window to wall ratio (WWR) and perimeter depth is established. A simplified prediction model is proposed to estimate lighting energy consumption with the given perimeter depth,WWR,and window transparency. Validation of the model is carried out compared with detailed lighting simulation software for an office building. After the variation analysis for these parameters,design advises for the daylighting design at scheme design phase are summarized.

Effective tool design of three-rank form as precision removal-process of ITO thin-films

A new effective tool design of three-rank form of electroremoval was present using a precision recycle system offering faster performance in removing the indium-tin-oxide(ITO) thin-films on color filter surface of displays. Higher electric power is not required since the three-rank form tool is adopted as a feeding mode to reduce the response area. The low yield of ITO persists throughout the entire semiconductor production process. By establishing a recycle process of ultra-precise removal of the thin-film nanostructure, defective products in the optoelectronic semiconductors industry can be effectively recycled, decreasing both production costs and pollution. A 5th generation TFT-LCD was used. The design features of the removal processes for the thin-films and the tool design of three-rank form were of major interest. For the precision removal processes, a pulsed current can improve the effect of dreg discharge and contributes to the achievement of a fast workpiece (displays' color filter) feed rate, but raises the current rating. High flow velocity of the electrolyte with a high rotational speed of the tool electrodes elevates the ITO removal effect. A displays' color filter with a fast feed rate is combined with enough electric power to provide highly effective removal. A small thickness of the rank and a small arc angle of the negative-electrode correspond to a higher removal rate for ITO-film. An effective three-rank form negative-electrode provides larger discharge mobility and better removal effect. It only needs a short period of time to remove the ITO easily and cleanly.

Radial basis function neural network and overlay sampling uniform design toward polylactic acid molecular weight prediction

Polylactic acid(PLA)is a potential polymer material used as a substitute for traditional plastics,and the accurate molecular weight distribution range of PLA is strictly required in practical applications.Therefore,exploring the relationship between synthetic conditions and PLA molecular weight is crucially important.In this work,direct polycondensation combined with overlay sampling uniform design(OSUD)was applied to synthesize the low molecular weight PLA.Then a multiple regression model and two artificial neural network models on PLA molecular weight versus reaction temperature,reaction time,and catalyst dosage were developed for PLA molecular weight prediction.The characterization results indicated that the low molecular weight PLA was efficiently synthesized under this method.Meanwhile,the experimental dataset acquired from OSUD successfully established three predictive models for PLA molecular weight.Among them,both artificial neural network models had significantly better predictive performance than the regression model.Notably,the radial basis function neural network model had the best predictive accuracy with only 11.9%of mean relative error on the validation dataset,which improved by 67.7%compared with the traditional multiple regression model.This work successfully predicted PLA molecular weight in a direct polycondensation process using artificial neural network models combined with OSUD,which provided guidance for the future implementation of molecular weight-controlled polymer's synthesis.

Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

The commonly used trial-and-error method of biodegradable Zn alloys is costly and blindness.In this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is Zn-0.4Mn-0.4Li-0.05Mg.Through tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion rate.Finally,influences of the selected features on UTS and CR are discussed in detail.The combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Toward predictable universal genetic circuit design

In thepast 2decades,synthetic biologists have applied systematic engineering principles to genetic circuit design to devise biological systems with bespoke behaviors,such as Boolean logic gates,signal filters,oscillators,state machines,perceptrons,and genetic controllers[1,2].Following a bottom-up strategy,the genetic circuits are designed by assembling a set of well-characterized biological components,or genetic parts[3],and optimized through the iterative Design-Build-Test-Learn(DBTL)cycles.

User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management

In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.