Faping Zhang

Research on assembly quality evaluation based on Markov chain

Assembly processes involve a large number of operations and uncertain factors, which makes the random deviation of assembly quality. How to quantifiably evaluate the final assembly quality remains a question. This paper presents a new methodology to fulfill the task. Based on the analysis of the factors influencing assembly quality of missile cabin, assembly quality model is formulated to establish the relationship between the assembly status and quality loss. Then the transfer matrix of assembly process is built by the functions of quality loss for the missile cabin in terms of Markov chain. Finally, a case study of the assembly process of missile cabin has been used to support and validate the proposed model.

Analytic model of machining errors for thin walled parts based on the deformation of the workpiece-fixture system

The analytic model of machining errors caused by the deformation of workpiece-fixture system is put forward for the thin walled parts. To facility the analysis, the traditional mathematical model of tolerance zone is redefined. According to the deformation value of workpiece-fixture system calculated by FEA (Finite element analysis), transform matrixes of workpiece location and cutting points deviation have been constructed. So that location changes of the surface machining points can be acquired. Then machining error model is formulated to describe the deviations caused errors. Further for given surface tolerances the sensitive matrix is constructed to evaluate the influence of locators on the machining errors. The model has great potential to be applied toward fixture design verification and process design evaluation. Finally a study case is used to support and validate the proposed model.

Optimizing Availability of a Framework in Series Configuration Utilizing Markov Model and Monte Carlo Simulation Techniques

This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC) Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.

High-definition metrology-based machining error identification for non-continuous surfaces

This article presents a layered decomposition method to decompose the machined surface into sub-surfaces with different components in dissimilar scale to identify machining errors. The high-definition metrology-measured data of the surface is first fitted by triangular mesh interpolation method to separate the surface into two sub-surface components, namely, system error caused and random error caused, respectively, whereas the stability of sub-surface entropy is used as the criteria to determine the refined mesh in case the decomposition exists throughout. Then, the sub-surface of system error is further decomposed by bi-dimensional empirical mode decomposition to get the error components varying in scales: surface roughness, waviness and profile, and as a result to identify the machining errors. Finally, self-correlation analysis is applied to each component to verify the decomposition. The result shows that each decomposed component has a distinctive wavelength, which proves that the method can successfully decompose the comprehensive surface topography into different scale components.

A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM) may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC) is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions.

Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

A Systematic Approach to Quality Oriented Product Sequencing for Multistage Manufacturing Systems

Product sequencing is one way to reduce cost and improve product quality for multistage manufacturing systems (MMS). However, systematically evaluating the influence of product sequence on quality performance for MMS is still a challenge. By considering the rate of incoming conforming product, manufacturing system quality transition between batch to batch, and quality propagation along stages, this paper investigates the appropriate batch policies and product sequencing for MMS so that satisfied quality performance can be achieved. A model to analyze the relationship between the product sequencing and quality performance is conducted just by using the quality inspection data and the complex engineering knowledge used in the variation method is avoided. Based on Markov Chain processes methodology, quality performance is modeled as a function of transition states jointly determined by multistage condition, product sequencing, incoming part quality, and propagation of the rate of conforming products among multistage. Quality related batch strategies are discussed for optimal quality performance. Two kinds of quality efficiency are put forward to facilitate the modeling and the discussion. The results of the model will lead to guidelines for quality management in multistage manufacturing systems.

Product quality characteristics oriented task reliability modelling method for manufacturing equipments

To evaluate the task reliability of manufacturing equipments with many components, a short-time and long-time manufacturing process reliability model is proposed to fulfil this task. The short-time manufacturing process reliability model based on multiple regressions aims to eliminate the effect of datum error on product quality. The long-time manufacturing process reliability model studies reliability change mechanism over time by means of Markov chains. Reliability computing method is given by considering the general definition of task reliability. Proposed product quality characteristics oriented model applies to manufacturing equipment reliability evaluation which avoids discussing numerous components of manufacturing equipment and complex relationship between them. Finally, the case result indicates that the proposed model can get more accurate estimated value of manufacturing equipment task reliability.

Intelligent fixture structure design based on knowledge ontology

Fixture configuration is an important stage in the whole fixture design process. Because of the complexity in fixture elements selection and assembling the selected as an entire fixture, it is still a challenge to improve the efficiency of the fixture configuration. By knowledge ontology modeling this paper presents a new methodology for fixture configuration. Firstly, the ontology models of fixture and workpiece are given to represent them respectively. Secondly, combined with the rule-based reasoning (RBR), the case-based reasoning (CBR) and the analytic hierarchy process (AHP), an intelligent fixture selection method is proposed to get the suitable fixture by matching corresponding entities of fixture ontology and workpiece ontology. Then a parameter driven method is used to get fixture and its elements geometry and an automatic assemble method for assembling those elements to an entire fixture. Finally, the modeling, method and system are tested and verified by conducting a typical case.

Quality Evaluation of Multistage Manufacturing Systems by Jointly Considering the Incoming Part Quality and System Conditions

The quality performance of a multistage manufacturing systems (MMS) is jointly affected by incoming part quality, system condition unreliability due to batch-to-batch uncertainty, making it challenging to evaluate the quality performance of MMS. Previous research considered the incoming part quality and system conditions separately in systematic level. This paper aims to fill the gap by considering the joint effects of these two aspects to evaluate quality performance of a MMS from historical production data driven work. A system quality model was derived to predict the probability of producing good parts at each stage and entire MMS when the incoming good part quality rate and station conditions were given. To overcome the inconvenience of the quality model for its nonlinear transfer function, the concept of quality efficiency was developed to depict the joint effectiveness of incoming part quality and system conditions mathematically at each stage. Based on the quality model, on the paper also discusses how to maintain high good product quality rate. The results of this study suggested a possible approach of evaluating the impacts of system conditions on product quality. The results of the model will lead to guidelines of quality management in multistage manufacturing systems.Copyright