A fuzzy expert system to increase accuracy and precision in measurement system analysis

In this paper, we have presented the first proposal to apply measurement system analysis (MSA) in fuzzy environment. Nowadays, because the quality control techniques are applied a lot in manufacturing, research and development environments, many decisions are made in processes and equipment evaluation, control, and improvement. Besides, the philosophy of measurement system analysis (MSA) depends on measurement error which hides true process capability; therefore, it must be implemented prior to any process improvement activities to minimize the measurement errors. Since the capability of each quality system is related to the accuracy of its measurement system, this research develops a novel methodology to increase precision and accuracy of MSA. To do so analyzing measurement systems under fuzziness of indices are investigated to make the environment more realistic. In this article, not only fuzzy measurement system analysis with gauge repeatability and reproducibility (GR&R) index as a triangular fuzzy number is proposed, but a fuzzy measurement system analysis a fuzzy expert system is presented to make more reliable and appropriate decisions. At the end, the applicability of the proposed methodology has been demonstrated within a case study in automotive parts industry to show impact of uncertainty in the reality.     

Using normal approximation for calculating the p-value in assessing process capability index Cpk

When a process is in a perfect state of statistical control, the process capability indices are widely used to measure the capability of the process to manufacture items within the required tolerance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. The index C_pk has been used in various manufacturing industries to provide a quantitative measurement of the performance of the manufacturing. In this paper, based on the theory of statistical hypothesis testing, we propose a procedure on assessing C_pk index for practitioners to use in determining whether a given process as capable. Applying Hamakers approximation, the testing procedure can be adequately derived from a normal distribution approximation, which avoids the more complicated non-central t-distribution. The provided approach is easy to use and the decision making is more reliable.     

Bootstrap approach for estimating process quality yield with application to light emitting diodes

Process capability indices have been widely used by quality professionals for measuring process performance. Although process yield is the most common criterion used in the manufacturing industry for measuring process performance, a more advanced measurement formula Y_q, called quality yield index, has been proposed as an alternative measure of process performance. Quality yield can be viewed as the classical process yield minus the truncated expected relative process loss, within the specifications, which focuses on customer satisfaction. By taking customer loss into consideration, the advantage of using the quality-yield measure as process performance is that the formula can be applied to processes with arbitrary distributions. Unfortunately, statistical properties of the estimated Y_q are mathematically intractable. Therefore, capability testing cannot be performed. In this paper, a nonparametric but computer intensive method called bootstrap is used to obtain a lower confidence bound on quality yield for capability testing purposes. Simulation studies are conducted to examine the sampling distribution of the estimated Y_q. An application using the index Y_q for the light emitting diode manufacturing process is presented for illustration purposes.     

Thermal-error modeling for complex physical systems: the-state-of-arts review

Flexibility and responsiveness to customer demands are very important for success. Generally additional time is needed for setup caused by poor design of equipment. At this point the terms continuous process improvement and SMED (single minute exchange of dies) as an approach of lean manufacturing come into play. A lean manufacturing system is part of corporate culture, like tools and approaches. In this research work, the process capability analysis technique is implemented by using MINITAB14 software to investigate the relation between SMED methodology and equipment design. The index C_pk has been used in this application study to provide a quantitative measurement of the equipment design by applying the SMED methodology in automobile manufacturing. The results of this research study indicated that SMED in other words “quick changeover” is still a suitable method not only for manufacturing improvement but also for equipment/die design development.     

Modified multivariate process capability index using principal component analysis

The existing research of process capability indices of multiple quality characteristics mainly focuses on nonconforming of process output, the concept development of tmivariate process capability indices, quality loss function and various comprehensive evaluation methods. The multivariate complexity increases the computation difficulty of multivariate process capability indices（MPCI）, which makes them hard to be used in practice. In this paper, a new PCA-based MPCI approach is proposed to assess the production capability of the processes that involve multiple product quality characteristics. This approach first transforms the original quality variables into standardized normal variables. MPCI measures are then provided based on the Taam index. Moreover, the statistical properties of these MPCIs, such as confidence intervals and lower confidence bound, are given to let the practitioners understand the capability indices as random variables instead of deterministic variables. A real manufacturing data set and a synthetic data set are used to demonstrate the effectiveness of the proposed method. An implementation procedure is also provided for quality engineers to apply our MPCI approach in their manufacturing processes. The case studies demonstrate the effectiveness and feasibility of this new kind of MPCI, which is easier to be used in production practice. The proposed research provides a novel approach of MPCI calculation.     

A modified multivariate process capability vector

Process capability indices have been widely used in industries to assess the performance of the manufacturing processes. Various different multivariate capability indices have been introduced. In this paper, a new multivariate capability vector is proposed under the assumption of multivariate normality, to assess the production capability of the processes that involve multiple product quality characteristics. Also, we investigate the relation between this index and process centering, as well as the relation between this index and the lower and upper bounds of percentage of non-conforming items manufactured. Two real manufacturing data set are used to demonstrate the effectiveness of the proposed index.     

A study on mutative scale straightness measurement based on uncertainty analysis

Mutative Scale Measurement is often used to measure the characteristics of mechanical and electrical equipments/components in manufacturing industry. A typical case is the straightness measurement of large-scale rails. Due to the uncertainty of surface error distribution, and difference in the size and the locations of sample points, the evaluated value has a certain degree of uncertainty, which reduces the accuracy of the measurement. Since the decrease of uncertainty is at the expense of higher measurement cost, optimization of measurement cost for an acceptable uncertainty becomes a significant issue. This paper describes the composition of the uncertainty that caused by the measuring process and analyzes the uncertainty caused by sample size insufficiency. On the basis of these arguments, a model has been proposed to calculate the minimum sample size which meets the requirement of measurement uncertainty. Case studies are carried out to demonstrate the application of the derived methodology on sampling strategy.     

Evaluating a task-specific measurement uncertainty for gear measuring instruments via Monte Carlo simulation

Evaluating the measurement uncertainty for gears with analytical or experimental methods is usually very time- and cost-consuming. In this paper we therefore present a Monte Carlo based method for evaluating the measurement uncertainty of gear measurements on gear measuring instruments, the VCMM-Gear, which is based on the method of the VCMM for coordinate measuring machines. Necessary extensions of the mathematical model of the measurement process in order to consider the significant uncertainty influences from rotary tables, workpiece clamping and scanning are described. Additionally the statistical reliability of the evaluated measurement uncertainty and the consideration of systematic error contributions to the measurement uncertainty are discussed. Finally the results of some first verification measurements are presented, giving a reliable impression of the capability and suitability of the VCMM-Gear.     

Study on a process-centric modeling methodology for virtual manufacturing of ships and offshore structures in shipyards

The production processes in a shipyard are highly complex and take various forms owing to the project-like characteristics of the products. Thus, a shipyard’s competitiveness is determined by the level of production management capability that it possesses. Many shipyards have made great efforts to improve their productivity by introducing higher level planning systems and process management systems. More recently, they have extended these efforts through the introduction of virtual manufacturing. Virtual manufacturing makes it possible to realize an improved productivity through the use of modeling and simulation technology. As a result, it is possible to leave behind decision making based on mere intuition or past experience by establishing improved planning based on concrete quantitative data. This study newly defines a simulation modeling methodology that is suitable for the characteristics of shipbuilding. On the foundation of existing virtual manufacturing modeling methodologies, we apply the product, process, resource, and schedule (PPR-S) model, which is suitable for a shipyard production environment. A process-centric simulation modeling methodology is defined based on the process-centric nature of ship production, and a system for applying this methodology is designed. Since it is designed considering a variety of virtual manufacturing software, it has a high level of flexibility for application in the field of shipbuilding.     

On the process capability of the solid free-form fabrication: a case study of scaffold moulds for tissue engineering

This study applies the methodology and procedure of process capability to investigate a solid free-form fabrication technique as a manufacturing method to produce scaffold moulds for tissue engineering. The process capability Cpk and process performance Ppk of scaffold mould manufacture using a solid free-form fabrication technique has been analysed with respect to the dimension deviations. A solid free-form fabrication machine T66 was used to fabricate scaffold moulds in this study and is able to create features that ranged from 200 microm to 1000 microm. The analysis showed that the printing process under the normal cooling conditions of the printing chamber was in statistical control but gave low process capability indices, indicating that the process was 'inadequate' for production of 'dimension-consistent' scaffold moulds. The study demonstrates that, by lowering the temperature of the cooling conditions, the capability Cpk of the printing process can be improved (about threefold) sufficiently to ensure the consistent production of scaffold moulds with dimension characteristics within their specification limits.     

Framework to evaluate manufacturing capability using analytical hierarchy process

Manufacturing capability of a firm is given by the level of output generated by the production system employed which in turn decides the competitiveness in the market. The pattern of structural and infrastructural decisions in the manufacturing affects the capability of the production system. Therefore, it is crucial to know the overall manufacturing capability of a firm and the contribution of each decision in it. This paper presents quantification of manufacturing capability based on multi-criteria approach. A total of 33 decisions (criteria) and their corresponding choices have been identified from the literature analysis to quantify the manufacturing capability. These decisions are then classified into six decision areas (group criteria), and the manufacturing capability evaluation problem is modeled by using analytical hierarchy process (AHP) framework. The approach facilitates computation of manufacturing capability index by evaluating the decisions taken in the manufacturing, which is then mapped into four levels, namely, infant, industry average, adult, and world class as proposed by the Miltenburg. The methodology is illustrated with a numerical example for a job shop manufacturing system.     

Measuring manufacturing capability based on lower confidence bounds of C pmk applied to current transmitter process

Several process capability indices, including C_p, C_pk, and C_pm, have been proposed to provide numerical measures on manufacturing potential and actual performance. Combining the advantages of those indices, a more advanced index C_pmk is proposed, taking the process variation, centre of the specification tolerance, and the proximity to the target value into account, which has been shown to be a useful capability index for manufacturing processes with two-sided specification limits. In this paper, we consider the estimation of C_pmk, and we develop an efficient algorithm to compute the lower confidence bounds on C_pmk based on the estimation, which presents a measure on the minimum manufacturing capability of the process based on the sample data. We also provide tables for practitioners to use in measuring their processes. A real-world example of current transmitters taken from a microelectronics device manufacturing process is investigated to illustrate the applicability of the proposed approach. Our implementation of the existing statistical theory for manufacturing capability assessment bridges the gap between the theoretical development and the in-plant applications.     

Evaluating measurement and process capabilities by GR&R with four quality measures

This paper proposes a procedure for assessing a measurement system and manufacturing process capabilities using Gage Repeatability and Reproducibility (GR&R) designed experiments with four quality measures. In this procedure, a GR&R study is conducted to obtain replicate measurements on units by several different operators. The gage and part variance components are then estimated by conducting analysis of variance (ANOVA) on the GR&R measurement observations. Finally, the acceptance and rejection criteria of the precision-to-tolerance ratio (PTR), signal-to-noise ratio (SNR), discrimination ratio (DR), and process capability index (C_p or C_pk), are employed to assess the measurement and process capabilities. Three previously studied case studies are provided for illustration; in all of which the procedure provided efficient capability assessments at minimal computational and statistical efforts. In conclusion, the procedure proposed in this research using GR&R designed experiments provides valuable procedure and helpful guidelines to quality and production managers in assessing the capabilities of a measurement system and manufacturing process, and deciding the needed actions for improving performance.     

Powder sampling and characterization by digital image analysis

The knowledge and understanding of powder shape and size are essential to the improvement and control of several industrial processes. This paper describes an offline methodology used in the characterization of powder properties based on digital image analysis. The method provides a reliable sample preparation consisting of a mixing operation and a statistical test validation. A versatile digital image procedure is presented: image calibration, enhancing and clustering permit to automatically detect particles and retrieve 2D properties. Shadow casting is applied to achieve the third dimension. The method can deliver statistically significant size and shape information in a single measurement characterizing the salient parameters of powder. The potential of the proposed method is shown with its application to some industrial powders. As is suggested it fulfils the identified needs of product and process monitoring applications for technological/industrial manufacturing, for quality control and improvement purposes.     

Information requirements analysis for holonic manufacturing systems in a virtual environment

The design and development of holonic manufacturing systems requires careful, and sometimes risky, decision making to ensure that they will successfully satisfy the demands of an ever-changing market. In this paper, the authors propose a methodology for a holonic manufacturing systems requirement analysis that is based on a virtual reality approach and aimed at assisting designers of such systems along the entire systems design and development process. Exploiting virtual reality helps the user collect valid information quickly and in a correct form by putting the user and the information support elements in direct relation with the operation of the system in a more realistic environment. A prototype software system tool is designed to realise the features outlined in each phase of the methodology. A virtual manufacturing environment for matching the physical and the information model domains is utilised to delineate the information system requirements of holonic manufacturing systems implementation. A set of rules and a knowledge base is appended to the virtual environment to remove any inconsistency that could arise between the material and the information flows during the requirement analysis.     

A Six-sigma approach for benchmarking of RP&M processes

This paper presents a methodology of using six-sigma quality tools for benchmarking of rapid prototyping & manufacturing (RP&M) processes. It involves the fabrication of a geometric benchmark part and a methodology to control and identify the best performance of the process to reduce the variablity in the fabricated parts. The approach is demonstrated with a case study based on the direct laser sintering (DLS) process for prototyping using plastic powder. In the case study an identified set of six-sigma/ statistical process control tools is employed to determine and best tune factors affecting the desired outcomes of the built parts.     

A study on the weighted composite dispatching rule in modular production systems

Scheduling semi-conductor manufacturing process systems is a complicated and difficult job, due to such characteristics as re-entry into manufacturing processes, high uncertainty of processes, and products and technologies changing rapidly. The researchers have carried out many studies to find efficient techniques for semi-conductor manufacturing systems with a view to accomplishing the goals of systems, such as saving cycling time and increasing production quantity per unit time. The production flow in the semi-conductor industry has the most unique characteristics, which makes it difficult to plan production and to schedule semi-conductor manufacturing. Currently, the scheduling methods in semi-conductor assembly processes follow the dispatching rule on a simple first come first serve (FCFS) basis, and a backlog is operated as a buffer based on the daily production quantity. In this study, therefore, we will apply various dispatching rules on a real-time basis and verify the effect and result of exact scheduling through simulation, based on the assumption that competitive advantages in production come from efficient inventory control and exact scheduling.     

Integrated Modeling Framework for Manufacturing Systems: A Unified Representation of the Physical Process and Information System

The task of process modeling in a manufacturing environment centers around controlling and improving the flow of materials. This flow comprises a complicated web of control and physical systems. Despite a variety of manufacturing system modeling approaches, more rigorous process modeling is required. This paper presents an integrated modeling framework for manufacturing systems (IMF-M). Conceptual modeling of physical materials flow supported by a graphical representation facilitates improvement of operations in manufacturing environments. A declarative and executable representation of control information systems helps to improve information management by managing a variety of information models with improved readability and reusability. A unified representation of the physical process and information system provides a common modeling milieu in which efforts can be coordinated among several groups working in the different domains of scheduling, shop floor and logistics control, and information system. Since the framework helps adapt to the changes of the physical process and information system affecting each other in a consistent manner, the modeling output enhances integration of the manufacturing system.     

Design of a vibrating MEMS gyroscope considering design variable uncertainties

Recently, several micro electro-mechanical systems (MEMS) such as a MEMS gyroscope have been developed by using micro manufacturing technologies. Micro scale products, however, usually have a relatively large manufacturing uncertainty compared to normal macro scale products. It is quite expensive to lower the variance of material properties as well as the geometric properties of a micro scale product. The material and geometric uncertainties caused by a micro manufacturing process inevitably lead to the uncertainty of the product performance. Therefore, to achieve a reliable design of a product, the performance uncertainty of the product, which is often expressed by the variance or the standard deviation, needs to be estimated in a reliable way. In this paper, the equations of motion of a MEMS gyroscope model are derived to analyze the system performance indices (sensitivity and bandwidth). The mean values of the design variables are determined from the requirements of product size, maximum vibration amplitude, and driving frequency. Then the standard deviation of some critical design variables is determined from the performance requirements. Finally, a statistical analysis procedure based on sample statistics is proposed to estimate the confidence interval of the performance index statistics.     

电机转轴加工质量检测与控制系统的开发

电机转轴加工质量检测与控制系统,可实现电机转轴轴承档直径的自动检测,然后通过与计算机连接,实现数据的实时采集,最后利用休哈特控制图及工序能力指数等统计过程控制工具,实现电机转轴加工工序的质量控制。