A new process-based ontology for KBE system implementation: application to inspection process planning

This paper focuses in the definition of a new ontology (Onto-Process) and the resulting informal model. The developed ontology constitutes an improvement to other ontologies since it covers the requirements of manufacturing processes engineering and, in particular, the inspection planning process with automated machines. Inspection planning activity usually implies to make repetitive and well-known decisions. These decisions are often based on the knowledge of inspection operators; therefore, it is a good candidate for implementing a knowledge-based engineering system. The sequence of activities involved in the inspection planning has been identified using Icam definition diagrams (IDEF0) in order to express the knowledge at a high level. These diagrams constitute an important support for defining the sequence of activities and the information flow. Later on, this information has been analyzed in depth using conceptual maps and it has been classified into units of knowledge. Then, each of these units of knowledge is decomposed into elementary objects of knowledge and relationships among them are defined. In a first approach, the ontology has been tested using a very particular but essential task in inspection planning: the activity related to the definition of contact points over inspection surfaces. In this activity, decisions must be made about the number, distribution, and sequencing of points. In the methodology developed in this paper the differences between form and non-form error inspection is considered. Finally, the knowledge required to support these decisions is represented using Onto-Process.     

Decision diagram based methods and reliability analysis for k-out-of-n: G systems

Binary k-out-of-n systems are commonly used reliability models in engineering practice. Many authors have extended the concept of k-out-of-n system to multi-state k-out-of-n systems. This paper proposes a binary decision diagram (BDD) based approach for binary k-out-of-n: G system and a multi-state multi-valued decision diagram (MMDD) based approach for multi-state k-out-of-n: G system. BDD and MMDD have been extensively used for representing and manipulating logic functions in many areas, including reliability modeling and analysis. In this paper, patterns of BDD/MMDD for binary/multi-state k-out-of-n: G system are summarized and proved, a two-step algorithmic process is proposed for modeling the BDD/MMDD and three case studies are implemented to demonstrate the presented methods. Complexity analysis shows that the presented method is more computationally efficient than the traditional algorithms for k-out-of-n: G system.     

Contribution in the evaluation of a performance index of hot forging dies

The present paper focuses on a new concept of evaluation of a performance index of hot forging dies through the combination of a process behaviour model and a reliability approach analysis of the forging process. The process behaviour is modelled and simulated using the computer-aided engineering FORGE® tool that allowed optimizing the forging parameters and determining the stresses and strains together with temperatures during the forging process and also identifying the factors affecting the die life. The respective reliability mechanical model is developed and reliability simulations have been conducted using PHIMECASoft tool in order to evaluate the reliability index β and determine the sensitivity of the variations in the random values of the input parameters of the forging process. The concept of this contribution has been applied to real industrial dies used to forge automotive steering levers produced in the forging workshop of the Ferrovial Company of Annaba in Algeria. The performance index is given by the number of forged parts corresponding to the reliability index of the dies before failure occurs.     

Decoupling analysis on nonlinear system based on the modified generalized frequency response functions

This paper presents a nonlinear decoupling approach based on the Modified Generalized Frequency Response Functions (MGFRFs) and the nonlinear feature of phase invariance, for the pure nonlinearity-input nonlinear system. The MGFRFs are defined by combining the ‘homotopy’ GFRFs and phase information of the system input. The nonlinear feature of phase invariance is extracted based on MGFRFs. The decoupling approach is proposed based on MGFRFs and extended from the pure tone excitation to the multi-tone excitations by considering phase invariance. Numerical simulation and experimental investigation were carried out, whose results have shown that nonlinear feature of phase invariance is correct and reasonable and the proposed decoupling approach is valid and feasible. The proposed decoupling approach can be employed to identify the excitation sources and to estimate nonlinear system parameters for the pure nonlinearity-input nonlinear vibration system.     

Effect of waterjet peening on aluminum alloy 5005

The present study addresses the effect of waterjet peening parameters on aluminum alloy 5005. The approach was based on the response surface methodology utilizing the Box–Behnken experimental design. Workable empirical models were developed to predict surface roughness (R _a ) and hardness (HV). Increasing the number of passes, pressure, and standoff distance produces a higher surface roughness as well as a higher hardness. On the contrary, increasing the feedrate produces a lower surface roughness and hardness. The developed empirical models for R _a and HV have reasonable correlations between the measured and predicted responses with acceptable coefficients of determinations. A different set of optimum parameters was generated based on different desirability functions for each response. The predicted and the actual responses for optimized R _a and HV are satisfactory with good reliability. It is shown that the models are workable in predicting the responses of R _a and HV in the present research. A proper selection of peening parameters can be formulated to be used in practical works.     

Reliability estimation for cutting tools based on logistic regression model using vibration signals

As an important part of CNC machine, the reliability of cutting tools influences the whole manufacturing effectiveness and stability of equipment. The present study proposes a novel reliability estimation approach to the cutting tools based on logistic regression model by using vibration signals. The operation condition information of the CNC machine is incorporated into reliability analysis to reflect the product time-varying characteristics. The proposed approach is superior to other degradation estimation methods in that it does not necessitate any assumption about degradation paths and probability density functions of condition parameters. The three steps of new reliability estimation approach for cutting tools are as follows. First, on-line vibration signals of cutting tools are measured during the manufacturing process. Second, wavelet packet (WP) transform is employed to decompose the original signals and correlation analysis is employed to find out the feature frequency bands which indicate tool wear. Third, correlation analysis is also used to select the salient feature parameters which are composed of feature band energy, energy entropy and time-domain features. Finally, reliability estimation is carried out based on logistic regression model. The approach has been validated on a NC lathe. Under different failure threshold, the reliability and failure time of the cutting tools are all estimated accurately. The positive results show the plausibility and effectiveness of the proposed approach, which can facilitate machine performance and reliability estimation.     

人机协同制造系统可靠性试验与评估

人机协同制造系统可靠性对系统的运行具有重要影响,在建立人机协同制造系统可靠性模型基础上,提出人机协同制造系统可靠性试验与评估方法.利用某人机协同制造系统可靠性增长试验中积累的可靠性试验数据,估算出系统目前的可靠性指标,研究结果表明,该方法在系统可靠性分析中合理可行.     

Damage localization using experimental modal parameters and topology optimization

This work focuses on the development of a damage detection and localization tool using the topology optimization feature of MSC.Nastran. This approach is based on the correlation of a local stiffness loss and the change in modal parameters due to damages in structures. The loss in stiffness is accounted by the topology optimization approach for updating undamaged numerical models towards similar models with embedded damages. Hereby, only a mass penalization and the changes in experimentally obtained modal parameters are used as objectives. The theoretical background for the implementation of this method is derived and programmed in a Nastran input file and the general feasibility of the approach is validated numerically, as well as experimentally by updating a model of an experimentally tested composite laminate specimen. The damages have been introduced to the specimen by controlled low energy impacts and high quality vibration tests have been conducted on the specimen for different levels of damage. These supervised experiments allow to test the numerical diagnosis tool by comparing the result with both NDT technics and results of previous works (concerning shifts in modal parameters due to damage). Good results have finally been achieved for the localization of the damages by the topology optimization.     

A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert

The prime factor for selecting equipment is its performance capability and reliability without compromising on quality. Materials for aerospace application such as aluminum and its alloys have limited applications because of their complications in machining, effectively and economically. There is no further development in raising the effectiveness above the optimal level in cutting tool materials. The surface roughness influences the determination of the quality of the product. The present study focuses on finding optimal end milling process parameters by considering multiple performance characteristics using grey fuzzy approach. In this work, Aluminum Alloy 6082T6 (AA6082T6) is used as workpiece material which was end milled using Aluminum Chromo Nitride (AP3) coated milling insert. Three process performance parameters namely Centre Line Average Roughness (Ra), Root Mean Square Roughness (Rq) and Material Removal Rate (MRR) were optimized. The grey output is fuzzified into five membership functions and also with twenty-seven rules. Grey Fuzzy Reasoning Grade (GFRG) is developed and the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) shows that the maximum contribution in the depth cut is (31.785%) followed by feed (28.212%). Moreover, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has been developed with the help of the same input values compared to the performance of the fuzzy logic model. With the help of detailed analysis, it has been found that the fuzzy logic based model gives more reasonable results when compared to ANFIS model.     

Effect of uncertainty parameters on graphene sheets Young’s modulus prediction

Software based on molecular structural mechanics approach (MSMA) and using finite element method (FEM) has been developed to predict the Young’s modulus of graphene sheets. Obtained results have been compared to results available in the literature and good agreement has been shown when the same values of uncertainty parameters are used. A sensibility of the models to their uncertainty parameters has been investigated using a stochastic finite element method (SFEM). The different values of the used uncertainty parameters, such as molecular mechanics force field constants k _r and k _θ , thickness (t) of a graphene sheet and length (L _B ) of a carboncarbon bonds, have been collected from the literature. Strong sensibilities of 91% to the thickness and of 21% to the stretching force (k _r ) have been shown. The results justify the great difference between Young’s modulus predicted values of the graphene sheets and their large disagreement with experimental results.     

Reliability and sensitivity analysis of a repairable system with warm standbys and R unreliable service stations

This paper deals with the reliability and sensitivity analysis of a repairable system with M operating units, S warm standby units, and R unreliable service stations. Failure times and service times of operating or standby units are assumed to be exponentially distributed. While the service station is working, it is subject to breakdowns according to a Poisson process. When the station breaks down, it requires repair at a repair facility, where repair times follow the negative exponential distribution. This paper provides the expressions for system reliability, R _Y (t), and the mean time to system failure, MTTF. We perform a sensitivity analysis for changes in the reliability characteristics, along with changes in specific values of the system parameters. Sensitivity analysis for the MTTF is also investigated.     

A study on the development of a web based urban transit reliability evaluation system

Over the past twenty years, the maintenance system developed, and its importance increased. For the effective maintenance, the maintenance system has been developed by introducing the concept of RCM (Reliability Centered Maintenance). This research is to develop an effective maintenance system for urban transit based on the concept of RCM. RCM is a systematic approach to develop a cost-effective maintenance strategy based on the various components’ reliability of the subject system. The final process of RCM determines appropriate failure maintenance strategies. For realization of RCM, reliability evaluation framework has been studied to compute the reliability index for urban transit. The framework requires the following processes: BOM (Bill of Materials), RBD (Reliability Block Diagram) based on FBD (Function Block Diagram), and failure code classification. The goal of this paper is to define the maintenance procedure for the subject system since successful maintenance system depends on an automated maintenance plan. This plan can be scheduled effectively by collecting and analyzing data from maintenance experience. For doing this, this paper proposes the web-based maintenance system for collecting data and the computing of MTBF (Mean Time between Failures) at the maintenance stage for analyzing data.     

Experimental and Computational Methods for Studying Multicomponent Equilibria. II. An Automated System for Precision emf Titrations

ABSTRACT

A potentiometric titrator designed mainly for complex formation investigations is described. The titrator is constructed from electronic building blocks, without a computer. The functioning of the titrator may be described by a rigid cyclic program with the possibility of adapting the titrator to the actual chemical system by varying a few key parameters on thumbwheel switches. Before the titration, the experimenter makes the decisions necessary. The titrator has no inherent facilities to decide whether equilibrium has been attained. Experiences gained with this titrator and the advantages over a computer-based system are discussed.     

Application of response surface methodology for predicting bend force during air bending process in interstitial free steel sheet

In this work, bend force has been modeled using response surface methodology for air bending operation of interstitial free steel sheet. The process parameters considered in this investigation are punch travel (d), strain hardening exponent (n), punch radius (r), punch velocity (v), and width of the sheet (w). The experimental plan was based on the central composite design. The model results are in satisfactory agreement with the experimental results. In addition, an analysis for the effect of the individual input bending parameters on the response has been carried out and presented in this study. This study shows that the punch travel is the dominant factor determining the bend force followed by punch velocity and punch radius. The interaction effects of punch travel and punch radius are considerably significant.     

Implementation of a supervised learning technique in a multi-agent system for building production orders

The authors describe the implementation of a supervised learning algorithm within a multi-agent system, whose general objective is to build production orders. Although this task has been carried out traditionally by the production management system, the classic approach lacks adaptive techniques and intelligent behavior. It is acknowledged that the combinatorial problem underlying the construction of production orders belongs to the NP hard complexity class. Therefore, flexible computational solutions are needed. We claim that by using intelligence and collaboration in a multi-agent system (MAS), a correct solution is reached more efficiently. Intelligence is emulated by both learning and decision-making, achieved through a feed-forward artificial neural network (FANN). The FANN is embedded in a machine agent, which determines the appropriate machine to manufacture the product. Collaboration is obtained by employing a sound protocol based on FIPA-ACL messages. We illustrate the approach by designing and implementing a MAS, which is already in use in a company that produces labels.     

Limit cycle analysis of active disturbance rejection control system with two nonlinearities

Introduction of nonlinearities to active disturbance rejection control algorithm might have high control efficiency in some situations, but makes the systems with complex nonlinearity. Limit cycle is a typical phenomenon that can be observed in the nonlinear systems, usually causing failure or danger of the systems. This paper approaches the problem of the existence of limit cycles of a second-order fast tool servo system using active disturbance rejection control algorithm with two fal nonlinearities. A frequency domain approach is presented by using describing function technique and transfer function representation to characterize the nonlinear system. The derivations of the describing functions for fal nonlinearities and treatment of two nonlinearities connected in series are given to facilitate the limit cycles analysis. The effects of the parameters of both the nonlinearity and the controller on the limit cycles are presented, indicating that the limit cycles caused by the nonlinearities can be easily suppressed if the parameters are chosen carefully. Simulations in the time domain are performed to assess the prediction accuracy based on the describing function.     

A particle swarm approach for grinding process optimization analysis

Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The grinding of silicon carbide is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the material removal rate (MRR) while maintaining the required surface finish and limiting surface damage. In this work, optimization based on the available model has been carried out to obtain optimum parameters for silicon carbide grinding via particle swarm optimization (PSO) based on the objective of maximizing MRR with reference to surface finish and damage. Based on statistical analysis for various constraint values of surface roughness and number of flaws, simulation results obtained for this machining process for PSO are comparatively better to genetic algorithm (GA) approach. In addition, the post-optimal robustness of PSO has also been studied. From simulation results together with the proposed robustness measurement method, it has been shown that PSO is a convergent stable algorithm.     

Minimizing the makespan and the system unavailability in parallel machine scheduling problem: a similarity-based genetic algorithm

This paper is devoted to ponder a joint production and maintenance problem. For solving the problem, a genetic algorithm named similarity-based subpopulation genetic algorithm (SBSPGA) is introduced. SBSPGA is presented based on a well-known evolutionary algorithm, the subpopulation genetic algorithm II (SPGA-II). Compared with the SPGA-II, the innovation of the SBSPGA could be divided into two parts: (1) using a similarity model for the elitism strategy and (2) performing the algorithm in just one stage. To tackle the maintenance aspect, reliability models are employed in this paper. The aim of this paper was to optimize two objectives: minimization of the makespan for the production part and minimization of the system unavailability for the maintenance part. To execute our proposed problem, two decisions must be made at the same time: achieving the best assignment of n jobs on m machines to minimize the makespan and determining the time at which the preventive maintenance activities must be performed to minimize the system unavailability. The maintenance activity numbers and the maintenance intervals are not fixed in advanced. Promising the acquired results, a benchmark with tremendous number of test instances (more than 5,000) is employed.     

Multi-objective reliability-based optimization for design of trapezoidal labyrinth weirs

In the present research, the reliability-based design optimization (RBDO) of labyrinth weirs has been investigated. The optimization problem is formulated such that the optimal shape of trapezoidal labyrinth weir described by a number of variables is found by minimizing the volume of the trapezoidal labyrinth weir and maximizing the reliability index. The constraint conditions are the weir geometric shape and its different ratios. In order to achieve this purpose, a framework is presented whereby non-dominated sorting genetic algorithm (NSGA-II) is integrated with monte carlo simulation (MCS) method to solve the RBDO approach of trapezoidal labyrinth weirs. The proposed method is applied to UTE Dam labyrinth weir, and the results are compared with the real one. The results show the need for design based on reliability in the labyrinth weirs that propose using RBDO for weir design. The results showed that RBDO approach can achieve a more reliable design in addition to reducing the volume of the trapezoidal labyrinth weir. Finally, the sensitivity analysis of the parameters effective on the reliability index revealed that three design variables of weir width, total upstream head and discharge coefficient are the main parameters affecting weir RBDO solution.     

Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker

Based on empirical mode decomposition (EMD) method and support vector machine (SVM), a new method for the fault diagnosis of high voltage circuit breaker (CB) is proposed. The feature extraction method based on improved EMD energy entropy is detailedly analyzed and SVM is employed as a classifier. Radial basis function (RBF) is adopted as the kernel function of SVM and its kernel parameter γ and penalty parameter C must be carefully predetermined in establishing an efficient SVM model. Therefore, the purpose of this study is to develop a genetic algorithm-based SVM (GA-SVM) model that can determine the optimal parameters of SVM with the highest accuracy and generalization ability. The classification accuracy of this GA-SVM approach is tried by real dataset and compared with the SVM, which has randomly selected kernel function parameters. The experimental results indicate that the classification accuracy of this GA-SVM approach is more superior than that of the artificial neural network and the SVM which has constant and manually extracted parameters.