The optimal process mean problem: Integrating predictability and profitability into an experimental factor space

For complex manufacturing systems, process or product optimization can be instrumental in achieving a significant economic advantage. To reduce costs associated with product non-conformance or excessive waste, engineers often identify the most critical quality characteristics and then use methods to obtain their ideal parameter settings. The optimal process mean problem is one such statistical method; it begins with the assumption of the characteristic parameters, whereby the ideal settings are determined based upon the tradeoff among various processing costs. Unfortunately, however, the ideal parameter settings for a characteristic mean can be unpredictable, as it is directly influenced by changes in the process variability, tolerance, and cost structure. In this paper, a method is proposed that relates the optimal process mean to the ideal settings through experimental design. With the method, one may gain greater predictability of the new optimal process mean when the process conditions are altered. The methodology is illustrated for a process with multiple mixed quality characteristics; such an optimal process mean problem is seldom treated in the literature.     

Optimal determination of the process means,process tolerances,and resetting cycle for process planning under process shifting

A proper process planning can significantly improve a producer’s competitiveness in regard to delivering high-quality and low-cost products with short development-cycle time. Because of process shifting, the produced quality may change during a production run and lead to early product failures. Hence, to compensate for such process shifting, there is a need to determine the optimal resetting cycle before the next setup as well as the initial settings at the beginning of a production run. As the process tolerance is one of the key elements in the production process, determination of the process tolerance must also be considered. Due to the interdependence among decision variables, a model for process planning is proposed to simultaneously determine the initial setting, process tolerance, and resetting cycle, so that the average total cost, in a period of resetting cycle, which includes the setup cost, quality loss, failure cost and tolerance cost, is minimized under process-capability limits, functionality requirements, and conforming rate restrictions.     

Economic design of process parameter control limits and process adjustment intervals for continuous production processes

In this paper, the concept of controlling the process parameters which is referred to as on-line feedback quality control is implemented for continuous production processes. The optimal process parameter control limits and process adjustment intervals which minimize the total quality cost of production are obtained. The quality costs considered are the diagnosis cost, the adjustment cost, the measurement error cost and the cost due to deviation of product characteristics from their target values based on loss function. The relationships between process parameters and quality characteristics of the product can be either linear function or nonlinear function. Using the computer program written in BASIC, the optimal solution can be adjusted resulting in the economical solution for more practical industrial application.     

Mathematical model for quality cost optimization

Quality engineering uses robust design in order to improve quality by reducing the effects of variability. Variability of the product can be reduced by two stages. One is parameter design which is adjustable to the nominal value so that output is less sensitive to the cause of variability. Other one is tolerance design which is to reduce the tolerance in order to control variability. All costs incurred in a product life cycle can be divided into two categories—manufacturing cost before the sale to the customer and quality loss after the shipment of the product to the customer. It is very important to find the optimum tolerances for each of the characteristics. A balance between manufacturing cost and quality loss should be arrived at in the tolerance design for quality improvement and cost reduction. For the case of Nominal-The-Best, a mathematical model is developed in order to determine the optimum product tolerance and minimize the total cost which includes the manufacturing cost and the quality loss. Since the process capability index (C_pm) shows the balance of quality responsibility between the design and the manufacturing engineers, this is taken as the basis in developing the functional relationship between the variability of the product and the tolerance. Based on these relationships, the total cost of model can be expressed as a function of product tolerance from which the optimal tolerance limits can be found out. Finally, using this model a tolerance design approach that increases the quality and reduces the cost can be achieved in the early stages of the product process design stage itself.     

基于装配顺序的飞机装配容差信息建模

论文提出了基于装配顺序的装配容差信息建模方法,以装配仿真路径规划 的装配顺序为基础,提取装配体中各零件的特征约束关系及相关的关键特征,进而获取组成 环容差信息,建立装配容差信息模型。该模型考虑了装配顺序对装配误差累积过程的影响, 减少了人工容差信息建模繁琐的交互操作,提高了装配容差信息的建模质量。基于DELMIA 平台开发了飞机装配仿真系统。该系统已在工程上成功应用,提高了飞机装配仿真的效率, 减少了飞机实际装配生产中的出错率。     

Optimal decision rules for determining the length of the production run

During the production period in some industrial processes, the process mean is shifted owing to the occurrence of an assignable cause. In other situations, simultaneous changes in process mean and process variance are experienced as time passes. When the changes are time dependent, the process mean and the process variance drift steadily as time goes on, and subsequently drive the process into an out-of-control state. Thus, at any point in the, a total change in product quality may result from drift or shift or both. This paper describes how to determine the optimal production run for a process under such conditions. The process is in control at the beginning of the production run and is shut down at the end for resetting. It is assumed that the process is subject to drift and/or shift. A cost function is developed that consists of the cost of resetting the process, the cost of rejected items, the lost product cost due to shutdown and the sampling cost. Optimal production run is determined by minimizing the cost function. A search algorithm as well as a graphical method are employed to locate the optimum solution.     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA

In many manufacturing processes, some key process parameters (i.e., system inputs) have very strong relationship with the categories (e.g., normal or various faulty products) of finished products (i.e., system outputs). The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, a knowledge-based artificial neural network (KBANN) is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. In addition, a genetic algorithm (GA)-based rule extraction approach named GARule is developed to discover the causal relationship between manufacturing parameters and product quality. These extracted rules are applied for diagnosis of the manufacturing process, provide guidelines on improving the product quality, and are used to construct KBANN. Therefore, the seamless integration of GARule and KBANN provides abnormal warnings, reveals assignable cause(s), and helps operators optimally set the process parameters. The proposed model is successfully applied to a japing-line, which improves the product quality and saves manufacturing cost.     

Computerized tolerance techniques

The intense global competition to produce quality product at low cost has led many industrial nations to consider mechanical tolerances as a key factor to bring about cost savings as well as be competitive. In the last two decades, not enough research work has been done in the area of tolerance techniques. In this paper, a comprehensive summary of the state of the art and the projection of future trends in tolerance techniques is presented to help make a decision concerning the improvement techniques today and to aid in guiding research for the future. This paper reviews the status of theory and practice about how manufacturing and assembly processes are characterized for relating tolerances to process or production cost. The specification of tolerancing on the dimension of the manufactured part has a significant impact on the final production cost. Tight tolerances can result in excessive process cost, while loose tolerances may lead to increased waste and assembly problems. This paper systematically reviews the state of art by classifying the papers written so far into three categories. The categories are tolerance chain technique, tolerances based on analysis and synthesis, and tolerances based on cost-tolerance algorithms and design methods. Future areas of research and the unresolved issues have been presented that will serve as a guiding light for the researcher to investigate and bring about the solution in future.     

Conceptual process planning – an improvement approach using QFD,FMEA, and ABC methods

Conceptual process planning (CPP) is an important technique for assessing the manufacturability and estimating the cost of conceptual design in the early product design stage. This paper presents an approach to develop a quality/cost-based conceptual process planning (QCCPP). This approach aims to determine key process resources with estimation of manufacturing cost, taking into account the risk cost associated to the process plan. It can serve as a useful methodology to support the decision making during the initial planning stage of the product development cycle. Quality function deployment (QFD) method is used to select the process alternatives by incorporating a capability function for process elements called a composite process capability index (CCP). The quality characteristics and the process elements in QFD method have been taken as input to complete process failure mode and effects analysis (FMEA) table. To estimate manufacturing cost, the proposed approach deploys activity-based costing (ABC) method. Then, an extended technique of classical FMEA method is employed to estimate the cost of risks associated to the studied process plan, this technique is called cost-based FMEA. For each resource combination, the output data is gathered in a selection table that helps for detailed process planning in order to improve product quality/cost ratio. A case study is presented to illustrate this approach.     

The optimum common process mean and screening limits for a production process with multiple products

In this research, we present a profit model for determining the most profitable common process mean and screening limits for a production process where multiple products are processed. Each outgoing item is inspected with a surrogate variable which is correlated with the quality characteristics of the products, and each item failing to meet the specification limits is scrapped. A profit model is constructed which involves selling prices, costs of production, inspection, and losses due to the penalty and scraps. The quality characteristics of the products are assumed to be normally distributed with known variances and an unknown common process mean to be determined. The optimum common process mean and screening limits are obtained by maximizing expected profit function using the Gauss–Seidel’s iterative method. A numerical example is illustrated along with a sensitivity analysis of design parameters.     

基于多粒度决策粗糙集的云计算产品分类方法

粗糙集理论中一个重要的研究课题是残缺区间决策系统.针对现有决策系统存在分类精度和质量较低的问题,结合可能性相似度容差关系的优良特性,并运用粗糙集理论,设计出一种新的多粒度决策模型.基于属性的视角,重新定义容差关系,并基于此方法,借鉴多粒度决策粗糙集具有一定容错能力和能够多层次处理数据的优势,结合粗糙集的优良特征,提出一...     

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.     

Preliminary design and manufacturing planning integration using web-based intelligent agents

Software agents have been increasingly used in the product and process development in industry over the past years due to the rapid evolvement of the Internet technology. This paper describes agents for the integration of conceptual design and process planning. Agents provide mechanisms to interact with each other. This mechanism is important since both of those processes involve negotiations for optimization. A set of design and planning software agents has been developed. These agents are used in a computer-based collaborative environment, called a multi-agent platform. The main purpose of developing such a platform is to support product preliminary design, optimize product form and structure, and reduce the manufacturing cost in the early design stage. The agents on the platform have access to a knowledge base that contains design and planning rules. These rules are derived from an analysis of design factors that influence process and resource planning, such as product material, form, shape complexity, features, dimension, tolerance, surface condition, production volume, and production rate. These rules are used by process planning agents to provide process planners with information regarding selecting preliminary manufacturing processes, determining manufacturing resources, and constructing feedback information to product designers. Additionally, the agents communicate with WEB servers, and they are accessible by users through Internet browsers. During performing design and planning tasks, agents access the data pertinent to design and manufacturing processes by the programming interfaces of existing computer-aided design (CAD) and manufacturing system. The agents are supported by a developed prototype agent platform. The agents and the platform enable the information exchange among agents, based on a previously developed integrated design and manufacturing process object model.     

Bicharacteristic quality control in manufacturing

In this paper, the importance of bicharacteristic quality control has been discussed, when it is desired to exercise process control on manufacturing products that present two measurable characteristics which can suffer variations and on the other hand are considered significant for the functioning or the acceptance of the product. The verification that the process is in control or not is made by examining periodically the items of the samples drawn and plotting the points representing the mean of the characteristic in analysis on the corresponding charts. Using the computational program developed, a practical application is carried out for a mechanical manufacturing component that presents two critical characteristics. Also, a programmable mathematical model has been developed, aiming to computationally determine the sample size, and the interval between successive samples, which minimize the expected total cost. Idealized numerical application illustrates the use of the cost model that optimizes these parameters.     

Automatically generating assembly tolerance types with an ontology-based approach

In most cases, designers have to manually specify both assembly tolerance types and values when they design a mechanical product. Different designers will possibly specify different assembly tolerance types and values for the same nominal geometry. Furthermore, assembly tolerance specification design of a complex product is a highly collaborative process, in which semantic interoperability issues significantly arise. These situations will cause the uncertainty in assembly tolerance specification design and finally affect the quality of the product. In order to reduce the uncertainty and to support the semantic interoperability in assembly tolerance specification design, an ontology-based approach for automatically generating assembly tolerance types is proposed. First of all, an extended assembly tolerance representation model is constructed by introducing a spatial relation layer. The constructed model is hierarchically organized and consists of part layer, assembly feature surface layer, and spatial relation layer. All these layers are defined with Web Ontology Language (OWL) assertions. Next, a meta-ontology for assembly tolerance representations is constructed. With this meta-ontology, the domain-specific assembly tolerance representation knowledge can be derived by reusing or inheriting the classes or properties. Based on this, assembly tolerance representation knowledge is formalized using OWL. As a result, assembly tolerance representation knowledge has well-defined semantics due to the logic-based semantics of OWL, making it possible to automatically detect inconsistencies of assembly tolerance representation knowledge bases. The mapping relations between spatial relations and assembly tolerance types are represented in Semantic Web Rule Language (SWRL). Furthermore, actual generation processes of assembly tolerance types are carried out using Java Expert System Shell (JESS) by mapping OWL-based structure knowledge and SWRL-based constraint knowledge into JESS facts and JESS rules, respectively. Based on this, an approach for automatically generating assembly tolerance types is proposed. Finally, the effectiveness of the proposed approach is demonstrated by a practical example.     

Tolerance analysis based on Monte Carlo simulation: a case of an automotive water pump design optimization

Successful products are those presenting the highest quality at a fair cost. Although different approaches can be used to define the concept of quality, functional reliability is always a major requirement, due to implications such as safety and user losses regarding maintenance expenses, and product availability. Intelligent designs are robust and result in a fair cost. Robust designs are those insensitive to sources of variation occurring during the product life, keeping their performance under variable use conditions, like thermal and stress effects. The robustness approach is a function of two main design criteria: low complexity and tolerance design. Design for manufacture and assembly is closely related to decreasing complexity. Tolerance design is a tool in which the unavoidable manufacturing variations are considered during product development. This work presents a proposal for an intelligent design in an actual application by considering design simplification through the reduction of parts for an automotive water pump. The tolerance analysis is performed by means of a powerful statistical approach—a Monte Carlo simulation—in which process behavior is randomly simulated representing a high production volume. Additionally, service thermal effects are also contemplated, and assembly tests are proposed for automatic rejection of non-conforming parts, assuring high reliability and full compliance with functional requirements. This is an example of integrated design–manufacturing work aiming at both cost saving and improved reliability.

     

Manufacturing evaluation using resource-based, template-free features

This paper addresses the theory and tools that promote rapid product and process development of machined products. A foundation of this approach is the application of a new definition of manufacturing features called resource-based free-form features. Using free-form features we demonstrate how cost, time and quality predictions, based on available equipment, are computed. We also introduce the concept of intelligently clustering alternative features to achieve better process alternatives. The approach presented herein is appropriate throughout the product design life-cycle as an aid to faster product realization by evaluating the parochial manufacturability of a completed design. In particular, this approach will provide the design team with cost, time and quality predictions, based on available equipment, that can be used to guide the design process. It also provides the manufacturing team with an approach to evaluating producibility and generating a production plan for a product model using available equipment in terms of cost, time and quality.     

面向工业过程软测量建模的概念漂移检测综述

基于数据驱动的软测量模型广泛用于工业过程中产品质量与环保指标等难测参数的在线测量,该过程中存在的概念漂移问题易导致模型精度下降.如何有效识别过程概念变化并精准检测漂移样本是提高模型测量性能的关键.本文总结并分析目前漂移检测的研究思路与进展,为面向工业过程软测量的漂移检测算法提供设计指导.首先,介绍了概念漂移的通常定义与其在工业过程中的表现形式;然后,从检测依据与检测对象两个视角分析了目前具有代表性的检测方法;接着,讨论了这些算法的技术特点和当前工业领域的研究难点;最后,展望了未来的研究方向.     

Tolerance design optimization on cost–quality trade-off using the Shapley value method

Part tolerance design is important in the manufacturing process of many complex products because it directly affects manufacturing cost and product quality. It is significant to develop a reasonable tolerance scheme considering the demands of cost and quality to reduce the production risk and provide a guide for supplier management. Traditionally, some kinds of cost objective functions or variation propagation models are often applied in part tolerance design. Moreover, designers usually solve the tolerance design problem by constructing a single-objective model, dealing with several single-objective problems, or establishing a comprehensive evaluating function combining several optimization objectives with different weights. These approaches may not adequately consider the interdependent and the interactional relations of various demands and balance them. This paper presents a kind of tolerance design approach at the early design stage of automotive parts based on the Shapley value method (SVM) of coalitional game theory considering the demands of manufacturing cost and product quality. First the part tolerance design problem is defined. The measuring data in regular production is collected instead of working on specific objective functions or design models. Then how the SVM is adopted to solve the tolerance design problem is discussed. Lastly, a tolerance design example of a vehicle front lamp demonstrates the application and the performance of the proposed method.