Bayesian zero-failure reliability modeling and assessment method for multiple numerical control(NC) machine tools

A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.

STUDY ON THE FEEDFORWARD COMPENSATION OF THE MOTIONERRORS OF NC MACHINE TOOLS

A feedforward compensation naethod of the motion errors of NC machine tools imple- mented with software is proposed , with which the motion errors can be compensated whithout changing the original computer control systems of the NC machine tools. The experimental results show that the circular interpolation profile machining errors decrease by a factor of 2/3 after com- pensated.

Combining the People Power and the Process Power to Achieve Six Sigma Process Capability——A Case Study

Achieving Six-Sigma process capability starts with l istening to the Voice of the Customers, and it becomes a reality by combining th e People Power and the Process Power of the organisation. This paper presents a Six-Sigma implementation case study carried out in a magnet manufacturing compa ny, which produces bearing magnets to be used in energy meters. If the thickness of the produced bearing magnets is between 2.35 mm and 2.50 mm, they will be ac cepted by the customers. All the time the company could not produce the bearing magnets within the specified thickness range, as their process distribution was flat with 2.20 mm as lower control limit and 2.60 mm as upper control limit. This resulted in a huge loss in the form of non-conformities, loss of time and goodwill. The process capability of the company then was around 0.40. Organisat ion restructuring was carried out to reap the benefit of the People Power of the organisation. Statistically designed experiments (Taguchi Method based Design o f Experiments), Online quality control tools (Statistical Process Control To ols) were effectively used to complete the DMAIC (Define, Measure, Analyse, Impr ove and Control) cycle to reap the benefit of the Process Power of the organisat ion. Presently the company enjoys a process capability of 1.75, a way towards Si x-Sigma Process Capability.

QUALITY CONTROL OF HERBAL MATERIAL:ALTERNATIVE AND/OR COMPLEMENTARY TOOLS BASED ON BIOSENSORS, DNA PROFILING, NEAR INFRARED SPECTROSCOPY AND NUCLEAR MAGNETIC RESONANCE

Phar.Eur.Herbal Drug(HD)monographs state which aspects have to be considered for quality assurance through the relevant chapters'Definition'.'Characters','Identification','Tests',and'Assay'.Identification of botanical material is achieved by macroscopic and microscopic morphology,generally examined by a trained expert.Content or assay is the most difficult area of

Sand production:A smart control framework for risk mitigation

Due to the current global oil price,the sand production is considered undesirable product and the control of sand production is considered as one of the main concerns of production engineers.It can damage downhole,subsea equipments and surface production facilities,also increasing the risk of catastrophic failure.As a result of that it costs the producers multiple millions of dollars each year.Therefore,there are many different approaches of sand control designed for different reservoir conditions.Selecting an appropriate technique for preventing formation sand production depends on different reservoir parameters.Therefore,choosing the best sand control method is the result of systematic study.In this paper the sand production factors and their effects are presented where the emphasis is given towards the sand prediction to determine the probability of producing sand from the reservoir,followed by the correct prevention implementation of sand control method.The combination of these two is presented as a smart control framework that can be applied for sand production management.

Reliability Analysis of Electrical System of Computer Numerical Control Machine Tool Based on Bayesian Networks

The core of computer numerical control(CNC) machine tool is the electrical system which controls and coordinates every part of CNC machine tool to complete processing tasks, so it is of great significance to strengthen the reliability of the electrical system. However, the electrical system is very complex due to many uncertain factors and dynamic stochastic characteristics when failure occurs. Therefore, the traditional fault tree analysis(FTA) method is not applicable. Bayesian network(BN) not only has a unique advantage to analyze nodes with multiply states in reliability analysis for complex systems, but also can solve the state explosion problem properly caused by Markov model when dealing with dynamic fault tree(DFT). In addition, the forward causal reasoning of BN can get the conditional probability distribution of the system under considering the uncertainty;the backward diagnosis reasoning of BN can recognize the weak links in system, so it is valuable for improving the system reliability.

Light controlled imaging probes for intracellular target recognition： photochromism as a tool to enhance sensing precision

Chemosensors and imaging probes have been the focus of significant research interest over the past few decades.In part due to ease of preparation and simplicity in manipulation,fluorescent probes have been extensively used for biomedical applications.When used for in vitro cell imaging[1,2]。

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry.Among all errors,thermal error affects the machining accuracy considerably.Because of the significant impact of Industry 4.0 on machine tools,existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data.A thermal error modeling method is proposed based on bidirectional long short-term memory(BiLSTM)deep learning,which has good learning ability and a strong capability to handle a large group of dynamic data.A four-layer model framework that includes BiLSTM,a feedforward neural network,and the max pooling is constructed.An elaborately designed algorithm is proposed for better and faster model training.The window length of the input sequence is selected based on the phase space reconstruction of the time series.The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting.The average depth variation of the workpiece was reduced from approximately 50μm to less than 2μm after compensation.The reduction in maximum depth variation was more than 85%.The proposed model was proved to be feasible and effective for improving machining accuracy significantly.