Fuzzy estimation for process capability indices

Process capability indices are summary statistics which measure the actual or the potential performance of process characteristics relative to the target and specification limits. In most traditional methods, precise estimation is used to assess the capability of manufacturing processes. In this paper we introduce an algorithm based on Buckley’s estimation approach, and use a family of confidence intervals to estimate process capability indices C_p, C_pk and C_pm. The estimators of these indices thus obtained are triangular shaped fuzzy numbers. We also present and illustrate method for the comparison of estimated process capability indices. Numerical examples are given to show the performance of the method.     

Development of fuzzy process accuracy index for decision making problems

Several statistical decision making tools and methods are available to organize evidence, evaluate risks, and aid in decision making. Process capability indices are the summary statistics to point out the process performance. In this paper, these indices are analyzed to obtain a new decision making tool. Process accuracy index (C_a) measures the degree of process centering and gives alerts when the process mean departures from the target value. It focuses on the location of process mean and the distance between mean and target value. We modify the traditional process accuracy index to obtain a new tool under fuzziness. With the proposed tool, specification limits and process mean can be defined as triangular or trapezoidal fuzzy numbers. The proposed tool is illustrated to solve a supplier selection problem.     

Software and systems engineering process capability in the South Australian defence industry

It is well recognised that that there is a correlation between process maturity in large organisations and project success. In response to this, a number of process models and standards have been developed for the large-project environment. The Australian defence industry, unlike many overseas countries, relies to a much greater extent on small and medium-sized enterprises to supply equipment and services. Hence, the question has arisen about the scalability of overseas concepts to the Australian defence industry situation. To address this question, a research project has been undertaken to identify the current baseline of process capability for the South Australian defence software and systems engineering industry. This paper presents findings from the research project, including a general characterisation of the industry process capability and a discussion of the common perceived strengths and challenges of organisations within the industry. The project’s objectives, research design and findings are then compared and contrasted with similar research activities conducted in different parts of the world. Finally, the paper draws conclusions based on the body of work presented and suggests areas for future research and development to address industry needs.     

Sensor selection for model-based real-time optimization: relating design of experiments and design cost

Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work presented here addresses one of these design decisions and proposes a systematic approach to the selection of sensors to be used by the RTO system. This paper develops a sensor system selection metric based on a trade-off between two approaches to the design of experiments, which is shown to be consistent with the design cost approach of Forbes and Marlin [Computers Chem Eng 20 (1996) 7/7]. The resulting design metric is incorporated into a systematic procedure for RTO sensor selection problem. Finally, the proposed RTO sensor selection procedure is illustrated with a case study using the Williams–Otto [AIEE Trans 79 (1960), 458] plant.     

Adaptability analysis of design for additive manufacturing by using fuzzy Bayesian network approach

The rapid development of Additive Manufacturing (AM) has been conspicuous and appealing towards manufacturing end-use products and components over the past decade. The continual advancement of AM has brought many advantages such as personalization and customization, reduction of material waste, cutting off the existence of special tooling during fabrication, etc. However, the AM approach has its limitations, such as a lack of knowledge of AM process activities and the progressive industrialization of AM, which makes the design process activities unstable, unpredictable, and have a limited effect. The concept of “design for AM (DFAM)” is increasing, which means we have the opportunity to concentrate almost totally on product functioning. Therefore, the entire design paradigm must be revised to accommodate new production capabilities, geometries, and parameters to avoid molding or machine tooling technology constraints. Few studies have attempted to provide systematic and quantitative knowledge of the relationship between these elements and the feasibility of the design process, making it difficult for designers to assess and control AM industrialization. For this reason, DFAM is needed to reform AM from rapid manufacturing to a mainstream manufacturing method. This paper put forward a framework based on the Fuzzy Bayesian Network (FBN) for DFAM decision-making. Twenty impact factors were encapsulated from experts’ experience and existing literature to investigate the potential adaptability of DFAM. The proposed approach uses expert knowledge and Fuzzy Set Theory (FST) presented with Triangular Fuzzy Numbers (FFN) to perceive the uncertainties. The Bayesian Network (BN) captures the causal relationships and dependencies among the impact components and analyzes the DFAM adaptability for robust probabilistic reasoning. A robot arm claw was used to show the effectiveness of our approach. The results showed that FBN could be used to guide DFAM adaptability in the manufacturing industry.     

ANP-GRAP方法在军校教学能力竞赛中的应用

针对军校教学能力竞赛规则中存在的主要问题,采用网络层次分析法与灰色关联分析法集成的方案,提出具体改进措施。着眼教学能力评价指标间具有较强相互影响的实际,利用网络层次分析法确定各指标权重;构建灰色关联分析模型,提出求解评价结果的思路;采集某次教学能力竞赛的原始数据,通过实例验证该评价模型的有效性。     

Joint optimization of product tolerance design,process plan,and production plan in high-precision multi-product assembly

With the ever-increasing product variety faced by the manufacturing industry, investment efficiency can only be maintained by the application of multi-product assembly systems. In such systems, the product design, process planning, and production planning problems related to different products are strongly interconnected. Despite this, those interdependent decisions are typically made by different divisions of the company, by adopting a decomposed planning approach, which can easily result in excess production costs. In order to overcome this challenge, this paper proposes an integrated approach to solving the above problems, focusing on the decisions crucial for achieving the required tolerances in high-precision assembled products. The joint optimization problems related to product tolerance design and assembly resource configuration are first formulated as a mixed-integer linear program (MILP). Then, a large neighborhood search (LNS) algorithm, which combines classical mathematical programming and meta-heuristic techniques, is introduced to solve large instances of the problem. The efficiency of the method is demonstrated through an industrial case study, both in terms of computational efficiency and industrial effectiveness.     

State of art of optimization methods for assembly line design

The problem of optimal design of the assembly lines is considered. The paper is especially focused on the line balancing and resource planning step for the preliminary design stage. A survey of existing methods is given.     

Identification test design for multivariable model-based control: An industrial perspective

The design of plant tests to generate data for identification of dynamic models is critically important for development of model-based process control systems. Multivariable process identification tests in industry continue to rely on uncorrelated input signals, even though investigations have shown the benefits of other input designs which lead to correlated, higher-amplitude input signals. This is partly due to difficulties in formulating and solving computationally tractable problems for identification test design. In this work, related results are summarized and extended. Connections between different designs that target D-optimality or integral controllability are established. Related concepts are illustrated through simulation case studies.     

Ergonomic considerations in the design of interactive languages for process control

The paper introduces the concept of software ergonomics and stresses the importance of respecting the working habits of the user while designing the software aids. Language design criteria are presented under two broad categories: those involving the syntax of the language and those involving its semantics. These criteria are then exemplified using an actual experience of designing an interactive language (TOOL) for a large public utility.     

A novel hybrid immune algorithm for global optimization in design and manufacturing

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.     

New capability indices for measuring the performance of a multidimensional machining process

Engineering tolerance plays an important role in the process capability analysis for determining whether a manufacturing process is capable of making good quality products. In contrast with the engineering tolerance region in a multivariate manufacturing process, the multidimensional machining process or the nano-cutting process has a special engineering tolerance called the positional tolerance. Positional tolerance is a special type of geometric dimensioning and tolerancing which describes the tolerance region between the actual location of machining results and the target location. In the past few years, several capability indices have been developed for measuring the performance of a multidimensional machining process under the assumption that the variances of machining results on different directions are equal. However, this assumption may not be true in most practical situations. In this paper, we propose three novel capability indices for measuring the performance of a multidimensional machining process under the assumption that the variances of machining results on different directions may not be equal. The statistical properties of the point estimators and their confidence intervals for the new capability indices are derived. Both the simulation results and numerical examples show that the new capability indices outperform the predecessors.     

Knowledge-based design for assembly in agile manufacturing by using Data Mining methods

Decision making in early production planning phases is typically based on a rough estimation due to lack of a comprehensive, reliable knowledge base. Virtual planning has been prevailed as a method used to evaluate risks and costs before the concrete realization of production processes. The process of product assembly, which yields a high share in total production costs, gets its particular importance. This paper introduces a new approach and its initial implementation for knowledge-based design for assembly in agile manufacturing by using data mining (DM) methods in the field of series production with high variance. The approach adopts the usage of bulk data with old, successful designs in order to extrapolate its scope for assembly processes. Especially linked product and process data allow the innovative usage of DM methods in order to facilitate the front loading in the product development. The concept presents an affordable assistance potential for development of new products variants along the product emergence process (PEP). With this approach an early cost estimation of assembly processes in series production can be conducted using advanced DM methods as shown in an industrial use case. Furthermore, design and planning processes can be supported effectively.     

A knowledge support approach for the preliminary design of platform-based products in Engineering-To-Order manufacturing

A product platform is a design approach for meeting the demand for customizable products. Traditional knowledge-based technologies or systems lack flexibility in supporting both configuration and parameter design of platform-based products. In many cases, customers’ requirements and knowledge models both contain incomplete information, and there are complex relations among various solutions, functions and solution parameters in Engineering-To-Order (ETO) products. A knowledge model for the preliminary design of ETO products is presented in this paper, and linkages are established between configuration design knowledge and parameter design procedures. The basis of the knowledge model is the Extended Function-Solutions (EFS) tree, from which design case trees, design modules, constraint checking rules, and module interface templates derive. A corresponding knowledge retrieval and reuse strategy is also presented. It uses an improved fuzzy information axiom to search for the optimal configuration with incomplete information. The parameter design process model of new products then can be generated based on the optimal configuration. The case study demonstrates the knowledge modeling, retrieval and reuse for the preliminary design of open-type crank presses. Moreover, the effectiveness of the methodology is discussed by analyzing the verification approach and the satisfaction of customers’ requirements.     

Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems

In this article, an empirical analysis of experimental design approaches in simulation-based metamodelling of manufacturing systems with genetic programming (GP) is presented. An advantage of using GP is that prior assumptions on the structure of the metamodels are not required. On the other hand, having an unknown structure necessitates an analysis of the experimental design techniques used to sample the problem domain and capture its characteristics. Therefore, the study presents an empirical analysis of experimental design methods while developing GP metamodels to predict throughput rates in a common industrial system, serial production lines. The objective is to identify a robust sampling approach suitable for GP in simulation-based metamodelling. Experiments on different sizes of production lines are presented to demonstrate the effects of the experimental designs on the complexity and quality of approximations as well as their variance. The analysis showed that GP delivered system-wide metamodels with good predictive characteristics even with the limited sample data.     

An integrated approach for the optimization of tolerance design and quality cost

Different normality-based optimization strategy (NBOS) methods have been developed and used to perform quality improvement in the past few decays. Improving the quality of a production process using a NBOS method possibly incurs misleading results if the quality measurements follow a skewed distribution. An integrated model, with components of a tolerance cost model for the determinations of optimal tolerance limits and a quality investment model for the identification of optimal investment level, is applied to establish a new optimization strategy method for the skew normal distribution (SND), named SNDOS method. The SND generalizes the normal distribution to include skewed distributions as members, and hence the SNDOS method is applicable for quality improvement either the distribution of quality measurements follow a symmetric or skewed distribution. Two examples about car seat production process are used to illustrate the application of the SNDOS method. The sensitivity of the SNDOS method to the loss coefficient of the integrated model is evaluated for different inputs of the skewness parameter of the SND through a numerical study.     

Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing

In this paper, three simulated annealing based algorithms that exploit auxiliary knowledge in different ways are devised and employed to handle a manufacturing process planning problem for reconfigurable manufacturing. These algorithms are configured based on a generic combination of the simulated annealing technique with; (a) heuristic knowledge, and (b) metaknowledge. Capabilities of the implemented algorithms are tested and their performances compared against a basic simulated annealing algorithm. Computational and optimization performances of the implemented algorithms are investigated and analyzed for two problem sizes. Each problem size consists of five different forms of a manufacturing process planning problem. The five forms are differentiated by five alternative objective functions. Experimental results show that the implemented simulated annealing algorithms are able to converge to good solutions in reasonable time. A computational analysis indicates that significant improvements towards a better optimal solution can be gained by implementing simulated annealing based algorithms that are supported by auxiliary knowledge.     

A process knowledge representation approach for decision support in design of complex engineered systems

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. Current research on the process knowledge representation of decision-making in engineering design has predominantly focused on either mathematical models of individual decisions at the micro-level or organizational models of group decision consensus at the macro-level. However, the management of complexity and uncertainty in the model-based realization of engineered systems is critical to achieving rational, comprehensive, and robust decisions, especially in terms of knowledge-intensive design. The efficiency and effectiveness of decisions in system design are intrinsically linked to the process, knowledge, and system concepts involved, necessitating a more flexible and systematic decision process representation scheme that supports both the management of complexity and uncertainty. Hence, in this paper, we propose a decision-centric design process representation scheme named the Phase-Event-Information X (PEI-X) diagram and its corresponding systematic design guidance method for designing decision workflows. Using the proposed method, designers have the ability to (1) model hierarchical decision processes that cover vertical and horizontal interaction patterns, and (2) exploit the synthesis of the “Formulating-Identifying-Reusing-Exploring” iterative process to extend the understanding and prediction of decision process behaviors in design. We achieve the aforesaid abilities through the implementation of a knowledge-based design guidance system for collaborative decision support and we demonstrate the efficacy by adopting a specific multi-stage manufacturing process design problem, hot rod rolling system design, and carry out an integrated design of materials, products, and related manufacturing processes.