Influence of Process Variables on the Structure of Mechanically Alloyed Ti_3Al-base Intermetallics

During mechanical alloying variables such as the type of mill,milling intensity,milling time,milling at- mosphere and ball-to-powder weight ratio(BPR)affect the morphology and constitution of the product. The effect of milling time,milling atmosphere and BPR on the nature of the product formed in mechanical- ly alloyed pure Ti and blended elemental binary Ti-Al,and ternary Ti-AI-Nb alloy powders was described. Mechanical alloying of pure titanium results,after long milling times,in the formation of an fcc phase.In the binary alloy,a solid solution of aluminum in titanium,an amorphous phase,and a fcc phase form with increasing milling time.The fcc phase,which is probably a result of TiN formation,occurs more rapidly in air or nitrogen than in an inert atmosphere.Formation of the B2 phase in the ternary alloys depends both on alloy composition and the milling atmosphere,with 100％ formation in all atmospheres in Ti-25Al-25Nb but not in Ti-24Al-11Nb,and an inert atmosphere favoring formation.The times required for the formation of the different phases decrease as the BPR increases;but their sequence is unaffected.Based on this infor- mation,“milling maps”which describe phase formation as a function of the BPR and milling time are con- structed.Contamination from the milling balls increased as the BPR was increased.

A Class of Single Machine Scheduling Problems with Variable Processing Time

In this paper, single machine scheduling problems with variable processing time are raised. The criterions of the problem considered are minimizing scheduling length of all jobs, flow time and number of tardy jobs and so on. The complexity of the problem is determined. [WT5HZ]

Strong NP-Hardness of Single Machine Scheduling Problems with Variable Processing Time

In this paper, single machine scheduling problems with variable processing time is discussed according to published instances of management engineering. Processing time of a job is the product of a “coefficient' of the job on position i and a “normal' processing time of the job. The criteria considered is to minimize scheduled length of all jobs. A lemma is proposed and proved. In no deadline constrained condition, the problem belongs to polynomial time algorithm. It is proved by using 3 partition that if the problem is deadline constrained, its complexity is strong NP hard. Finally, a conjuncture is proposed that is to be proved.

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

Methanol-to-olefins,as a promising non-oil pathway for the synthesis of light olefins,has been successfully industrialized.The accurate prediction of process variables can yield significant benefits for advanced process control and optimization.The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes,such as high nonlinearities,dynamics,and data distribution shift caused by diverse operating conditions.In this paper,we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues.Firstly,a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions.Subsequently,convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns.Meanwhile,a multi-graph convolutional network is leveraged to model the spatial interactions.Afterward,the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction.Finally,the outputs are denormalized to obtain the ultimate results.The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices,making the model more interpretable.Lastly,this model is deployed onto an end-to-end Industrial Internet Platform,which achieves effective practical results.

Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra⁃Deep Submicron Digital Circuit Design

This paper presents a physics⁃based compact gate delay model that includes all short⁃channel phenomena prevalent at the ultra⁃deep submicron technology node of 32 nm.To simplify calculations,the proposed model is connected to a compactα⁃power law⁃based(Sakurai⁃Newton)model.The model has been tested on a wide range of supply voltages.The model accurately predicts nominal delays and the delays under process variations.It has been shown that at lower technology nodes,the delay is more sensitive to threshold voltage variations,specifically at the sub⁃threshold operating region as compared with effective channel length variations above the threshold region.

Enabling Variability-Aware Design-Technology Co-Optimization for Advanced Memory Technologies

This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-stage simulation flow to study the deviceto-circuit performance of advanced memory technologies in presence of statistical and process variability.We present a DRAM example to highlight the DTCO enablement for both memory and periphery.Our analysis demonstrates how the evaluation of different possible technology improvements and design combinations can be carried out to maximize the benefits of continuous technology scaling for a given set of manufacturing equipment.

Current understanding and applications of the cold sintering process

In traditional ceramic processing techniques,high sintering temperature is necessary to achieve fully dense microstructures.But it can cause various problems including warpage,overfiring,element evaporation,and polymorphic transformation.To overcome these drawbacks,a novel processing technique called“tcold sintering process(CSP)”has been explored by Randall et al.CSP enables densification of ceramics at ultra-low temperature(<300℃)with the assistance o f transient aqueous solution and applied pressure.In CSP,the processing conditions including aqueous solution,pressure,temperature,and sintering duration play critical roles in the densification and properties of ceramics,which will be reviewed.The review will also include the applications of CSP in solid-state rechargeable batteries.Finally,the perspectives about CSP is proposed.

ROBUST DESIGN MODELS FOR CUSTOMER-SPECIFIED BOUNDS ON PROCESS PARAMETERS

Robust design (RD) has received much attention from researchers and practitioners for years, and a number of methodologies have been studied in the research community. The majority of existing RD models focus on the minimum variability with a zero bias. However, it is often the case that the customer may specify upper bounds on one of the two process parameters (i.e., the process mean and variance). In this situation, the existing RD models may not work efficiently in incorporating the customer’s needs. To this end, we propose two simple RD models using the ε?constraint feasible region method - one with an upper bound of process bias specified and the other with an upper bound on process variability specified. We then conduct a case study to analyze the effects of upper bounds on each of the process parameters in terms of optimal operating conditions and mean squared error.

NO conversion by positive streamer discharge——effects of gas compositions and reaction conditions

The effects of gas compositions and reaction conditions on NO conversion by positive streamer discharge were experimentally investigated by using a link tooth wheel-cylinder reactor.The results showed that NO conversion increased with increasing O_(2) concentration and NH3 concen-tration,but decreased with increasing inlet NO concentration and gas flow rate.The addition of CO_(2) or H_(2)O to the feed gas promoted NO conversion by increasing the maximum discharge voltage,and NH4NO3 was formed in the presence of NH_(3).There was a most suitable range interval between discharge tooth wheels if both NO conversion and energy consumption were considered.Increasing applied voltage resulted in the increase in the amount of O_(3) generated by streamer discharge.

ANALYSIS OF A PARALLEL SYSTEM WITH TWO DIFFERENT UNITS

In this Paper, a parallel repairable model consisting of two units and one repairman isstudied. The working time and the repair time of the two units are all exponeotially distributed.Assume that one unit after repair will be 'as good as new', but the other one not. By introducingthe geometric process and using the method of supplementary variable, some importaDt reliabilityindlces are determined.

Factors influencing volatile hydrocarbon pollution in urban areas

Volatile hydrocarbons in urban environments pose significant risks to human and ecosystem health,resulting from wash-off into receiving waters during storm events.Effective mitigation strategies require understanding of the significance of contributing factors to pollutant generation and their processes.This study employed Bayesian Network modelling to investigate how anthropogenic and environmental factors influence volatile hydrocarbons build-up.The volatile hydrocarbons investigated were,benzene,toluene,ethylbenzene and xylene and styrene.Most volatile hydrocarbons showed statistically significant relationships with environmental factors rather than with anthropogenic factors.Additionally,the research study found that anthropogenic factors could largely contribute to releasing volatile hydrocarbon into the urban environment,while environmental factors are likely to determine their prevalence.The research outcomes will contribute to improving stormwater quality modelling approaches and strengthen the assessment of risk associated with stormwater pollutants in order to enhance stormwater reuse.