A new process knowledge representation approach using parameter flow chart

Computers in Industry 62 (2011) 9–22

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Computers in Industry journal homepage: www.elsevier.com/locate/compind

A new process knowledge representation approach using parameter flow chart W.L. Chen a, S.Q. (Shane) Xie a,*, F.F. Zeng b, B.M. Li a a b

Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 21 September 2009 Received in revised form 7 April 2010 Accepted 31 May 2010 Available online 3 July 2010

Knowledge in various stages of the product development process has become increasingly important for manufacturing companies to improve their performance, especially for those One-of-a-Kind Production (OKP) companies producing highly customized products. Process knowledge is a very special type of knowledge that controls how products are best manufactured. This knowledge can help OKP companies produce high value-added products with better quality at shorter times and at a competitive cost. Process knowledge is normally hard to capture and manage and is even more difficult to represent. This paper proposes a Parameter Flow Chart (PFC) based new knowledge representation method which efficiently combines parameter information, flow chart technology, and visualization technology. The goal of this research is to provide a user-friendly and effective way of representing process knowledge for OKP companies so they can develop and accumulate their own process knowledge repository. The basic definition and principle of the approach will be introduced first and the software system model then proposed. Two related key issues, the modeling of various chart units used in the PFC approach and dealing with expressions containing various parameters, are discussed in detail. The prototype version of the system has been developed and demonstrated with case studies, which has proven the feasibility of the proposed approach. ß 2010 Elsevier B.V. All rights reserved.

Keywords: Knowledge representation Knowledge management Knowledge-based engineering Computer-aided process planning

1. Introduction The knowledge-based economy has become the major trend in international society in the 21st century [1]. The manufacturing industry has changed greatly and can be characterized by the following buzzwords: customer-oriented, globalization, and timedriven competition. The new trend not only imposes challenges, but also creates opportunities for many manufacturing companies, especially for One-of-a-Kind Production (OKP) companies. Compared to mass production companies, most OKP companies are small and medium-sized and their market strategies range from make-to-stock, assembly-to-order and make-to-order. Therefore, some main problems OKP companies face include high customization, ‘once’ successful approach, loose or fatter production, and complicated product data and information flow [2]. For companies of this type, their survival and success largely depend on their ability to produce newer, better and more innovative products, rather than simply relying on their size and strength like before. In order to meet these new requirements, computer-aided process planning (CAPP) systems which contain knowledge about the design of the product and how it is made are widely used [3]. This

* Corresponding author. E-mail addresses: [email protected] (S.Q. (Shane) Xie), [email protected] (F.F. Zeng). 0166-3615/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2010.05.016

has attracted much research interest in the areas of Knowledgebased Engineering (KBE) and Knowledge Management (KM) over the past years [4–8]. Product process knowledge in the manufacturing industry is a very special type of knowledge that illustrates how products can be best produced. These process knowledge and other intellectual capital have become the most important and valuable assets for OKP companies. Process knowledge, including standards, machining data, machine tools, stock availability as well as many trivial aspects [9], still rests in the minds of experienced designers. They need to practice in this area for several years before becoming experts of process planning. Hence, without these experts, the knowledge is not available to be shared or to be employed (for specific advice) as needed [10]. For most OKP companies in New Zealand, the shortage of knowledge workers is one of the biggest obstacles standing in the path of their growth. This has become a problem for most New Zealand small- and medium-sized companies. New strategies need to be developed to tackle such a problem. With the development of product modeling and computer software technologies, process knowledge modeling, capturing and representation are now possible. The capture and representation of knowledge is still difficult but serves as the foundation in managing knowledge [11]. This paper aims to develop an open system to represent process flow knowledge for OKP companies to help them accumulate their own process knowledge repository easily without much knowledge on information technology and software programming.

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[(Fig._1)TD$IG]

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2. Literature review 2.1. Computer-aided process planning approaches In industry, there are two approaches for computer-aided process planning systems to make use of databases. One is variant approach, while another one is generative approach. Unlike traditional approaches relying on the experience of skilled operators; both of these approaches are based on pre-built databases by which users can easily find the information of former parts and processes. Through this, costs and time of developing a new product will be reduced while profits and quality will be improved. In the variant approach, similar parts of a new product can be found from database and then the corresponding plan for manufacturing can be selected. This approach is based on information of previous products which normally contain certain primitives or entities such as points, lines, arcs, circles, polygons, and a higher level grouping of the aforementioned [12]. Wang and Bourne developed a computer-aided system based on feature information for process planning of sheet metal parts [13]. Though the variant approach largely improves the process planning procedure, it is not free of deficiencies. It lacks flexibility due to the fact that if user cannot find a similar part from the database, a new feature has to be created and stored for future use. However, there are many types of features which exist in manufacturing, thus making this approach less practical. Unlike the variant approach in which process knowledge is sorted by parts, the generative approach, on the other hand, automatically generates an optimal process plan according to the part’s features and manufacturing requirements. Most of the generative systems are knowledge-based systems [14]. This approach focuses on representing the various product knowledge and human intent. Therefore, the key and basis for this approach is to effectively and efficiently represent process knowledge of different former designs. A system of optimal manufacturing processes, including choosing efficient clamping fixtures and reasonable assembly processes, was developed by Liou and Suen through feature extraction techniques which use geometric descriptions of the relevant manufacturing environment in order to find the optimal clamping position of fixtures [15]. Pan and Rao presented an integrated knowledge-based CAD/CAM system which integrates the functions of part modeling, nesting, process planning, NC-programming, simulation and reporting for sheet metal process planning using object-oriented data structure [16]. Coupled with the fast development of manufacturing industries, there has been an increase in the number of types of part features and information that require collection and sorting. In particular, OKP companies have even more kinds of products and relevant knowledge, including feature knowledge and process knowledge that need to be organized and represented in detail. Hence, OKP is usually achieved through sophisticated computer systems to automate design and manufacturing processes [17]. In the past decade, a considerable number of process knowledge system and methodologies have been developed for process knowledge modeling in OKP companies [18]. Due to the shortcomings of traditional variant databases, knowledge-based generative systems attract more and more attention for process planning of OKP companies. 2.2. Process knowledge representation Process knowledge can be normally classified into three main types based on their forms, as shown in Fig. 1. They are: (1) Knowledge of process flow. This kind of rule-based knowledge includes the feature process, the product process, and the

Fig. 1. Product process knowledge composition and classification model.

typical process. A feature is the definition of a component’s basic geometric entities for manufacturing which can include cylinder, hole, plane, etc. Product process knowledge refers to process route information of a product family or similar products, which may change according to the input manufacturing data. The typical process knowledge is the mature process route information which has been validated by practice and normally used more frequently. (2) Knowledge of resource. This refers to static manufacturing resource information, which includes all kinds of process resources, such as machine tools, fixtures, cutters, machining data, and materials. (3) Knowledge of calculation. This refers to knowledge obtained through calculation. In process planning, the selection of working hours and material quota is a regular process. Process knowledge has been regarded as an important factor to consider in order for OKP companies to manufacture high valueadded products with the best possible quality, short lead times, and at a competitive cost. In a detailed survey of 22 large and small companies using CAPP systems, the following estimated cost savings were achieved: 58% reduction in process planning effort, 10% saving in direct labor, 4% in material, 10% in scrap, 12% in tooling and a 6% reduction in work-in-process [19]. However, capture and representation of knowledge is difficult due to its inherent nature. Moreover, the customer-oriented OKP companies are characterized by dynamic and user-driven product development and manufacture. As a result, product process knowledge in OKP companies has the following unique characteristics: (1) Dynamic and changing requirements. Process knowledge needs to be updated frequently when customer requirements change. This results in the corresponding changes of product designs, production plans and manufacturing constraints. (2) Diverse formats. There is a variety of process knowledge formats, including description sentence, tables, pictures, drawings, formulas, and numerous other representations. (3) Universality. Process knowledge is made up of both internal knowledge within an OKP company and external knowledge

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from its suppliers and customers. Internal knowledge includes all the knowledge from market surveys to product maintenance after sale. (4) Uncertainty. The machining process can still be changed even after the manufacturing process of a new product begins. From the above mentioned, it can be recognized that process flow knowledge is difficult to capture and represent in OKP companies due to its rule-based and dynamic nature and effects from unpredictable factors such as experienced process planners. Knowledge representation is one of the main research issues and in some ways the most familiar area in Artificial Intelligence (AI) [20,21]. The function of any representation scheme is to capture the essential features of a problem domain and make that information accessible to a problem-solving procedure. In recent times, considerable research effort has been put into the area of knowledge representation and many computer-aided systems have been developed to address specific machining processes. Knowledge modeling has been extensively researched and different representation schemas have been proposed such as semantic networks, frame-based structures, logical formalisms, rule-based representations and object-oriented representations as listed below [22]. (1) Network approaches. A network consists of nodes that denote concepts or objects, links between the nodes that denote object relations (also called associations), and link labels that denote specific relations. The labeled links can be used to express various forms of relations, such as is-part-of and x-contains-y. Amaitik and Kilic¸ presented an intelligent process planning system for prismatic parts. This feature-based intelligent system uses a hybrid approach which combines neural networks and fuzzy logic to avoid acquisition problems of complex knowledge [23]. Yang et al. presented a method of product knowledge representation using semantic net technology [24]. Hao et al. proposed a process planning mode to convert a series of geometric features into machining operations and sequences in a feasible and money-saving order. This model consists of a features framework of the design model, a semantic net of precedent relationships and a sequenced mathematical model. Machining process plan sequences were calculated with the help of the semantic net by reflecting the precedence relationships among the machine operations [25]. Lopez-Morales and Lopez-Ortega introduced a methodology based on topology to model a semantic network for a collaborative system and developed a prototype system for process planning. The knowledge-based agents used in this methodology were represented by semantic networks [26]. (2) Frame. A frame is defined as a structure which contains a number of slots. The attributes of the frame are sorted by these slots. The slots in a frame are usually filled with values. Frames are used in stereotyped problems that can be demonstrated hierarchically with a structured knowledge base [27]. Tu et al. reported a framework for computer-aided process planning (CAPP) in a virtual One-of-a-Kind Production company to address problems raised by OKP. This new method can help data and process modeling [28]. Based on machining features definition with representation, Waiyagan and Bohez designed a new group code to define and classify prisronal parts defined by authors as ‘‘a set of parts having primitive shapes with one common centerline, such as cylindrical shapes’’, and then proposed a process planning system for five-axis mill-turn parts [29]. (3) XML-based approaches. Coupled with the development of the semantic web, XML-based knowledge representation approach has attracted more and more research work in recent years. A

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method based on XML for standardizing CAPP information for parts and machining knowledge modeling was developed by Sun and Wang in 2007 [30]. Liang et al. introduced an XMLbased representation and application of collaborative design knowledge for mould and die. A uniform representation mode named knowledge cell was proposed, in which each element was defined by means of XML [31]. Hu et al. developed an XMLbased method for route sheet to support context-sensitive and web-based process planning. The route sheet document includes a declarative user interface, a tree-style data structure, and processing rules embedded in documents to support context-sensitive information processing. A XML application named Manufacturing Process XML was developed as a metalanguage for the description of manufacturing process information [32]. Manufacturing operations were represented in XML schema by Kojima et al. in 2008. The schema was also used to convey knowledge such as operation standards and manufacturing troubleshooting on the shop floor. A Webbased Q&A system was also developed [33]. (4) Rule-based approaches. Using logical rules to combine knowledge, many systems can easily define the represented knowledge stored in the database. For example, some of the rule-based approaches are made up of IF. . .THEN rules. The IF part of the rule contains one or more conditions and is called the antecedent, while the THEN part is the consequent. Based on IF–THEN rules, Chen and Rao developed a MRM approach by which the matrix representation and mapping operations will be performed to extract rules automatically [34]. Besides the use IF. . .THEN rules, some knowledge representation approaches also use other rules, such as AND. . .OR rules. Among the approaches listed above, the ones commonly used for knowledge representation are network and frames approaches [35]. Mathematical logic approach is another popular method for representing process knowledge as it is familiar to many people due to its long history and its solid mathematical background [36]. All of these developed knowledge representation technologies and methods have significantly improved the performances of product process representation. Moreover, more and more hybrid systems have been developed to solve complex problems. However, to the best of our knowledge, many of these still have limitations and there are issues and challenges that need further investigation. They include: (1) Limited functionality to represent dynamic and flow type of process knowledge. A complete process planning is related to many factors such as the selection of material, machine tools, fixtures, and cutters. The different choices of any factor might result in different process flows, fixture types, cutting parameters, etc. This kind of process flow knowledge, is thus, dynamic and is the most difficult to represent. Many of the state-of-the-art representation methods focus on representing the static process knowledge or representing the dynamic process knowledge through software programming. This is not a suitable approach for experts to establish their own process knowledge repository. (2) Limited representation of uncertain process knowledge. There is a lot of fuzzy knowledge which is inaccurate that require representation used in product process planning. Here is an example of a knowledge sentence describing the recommended cutting parameters when reaming using a carbide alloy reamer: if the machined material is carbon steel or alloy steel, the recommended range of feed rate is 50–125 mm/min, while the range of cutting speed is 50–100 m/min. Obviously, this knowledge is not exact and provides a range of information which may lead to confusion in the machining decision making

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system. This type of knowledge already exists in engineering. Most of the proposed methods only represent certain and exact knowledge and cannot deal with fuzzy process knowledge adequately. (3) Limited expansibility. Most of the existing approaches and systems are based on fixed process knowledge representation schema or system architecture. They are applied to a particular kind of manufacturing environment and are very specific, such as mill-turn parts and annotation parts mentioned above. (4) Limited openness. Due to the fact that process knowledge representation and inference mechanism differ from systems and manufacturing fields, current approaches have limitations of openness. Most of the current approaches are accomplished through programming or special computer workers, such as the network approach and the mathematical logic approach. These are not suitable for employees who have limited knowledge about programming or IT technologies, to build their own process knowledge repository for manufacturing companies. This paper aims to represent flow type knowledge with Parameter Flow Chart (PFC) approach to avoid the listed limitations. Firstly, using parameter technology, operators can select different and relevant parameters accurately in accordance to different machining processes as processes may vary from one to another. This will make the proposed system suitable for different applications. Secondly, applying flow chart technology provides visualized operation process decisions which will enable operators to combine process flow knowledge clearly. 3. Knowledge representation approach based on PFC 3.1. Definitions and concepts related with PFC To overcome the issues discussed in Section 2, a novel knowledge representation approach using PFC is proposed. The basic concepts and definitions are first introduced before the model and principle of the PFC approach are presented. Definition 1. Parameter (P)P, a sub-function of process planning, is an element that stands for a changeable value for product process knowledge. It tends to be considered and determined by experienced engineers or by the machining data handbook due to its complicity and importance. For example, when a hole feature in a part needs to be machined, the diameter, machining precision, surface roughness of the hole feature may influence the machining sequence. In this proposed approach, these factors can be defined as P which is a set of driving parameters in the PFC approach. They are similar to the variables used in computer programming, and are normally represented by numeric values, characters or character strings. For example, P can be expressed using the following expression: P ¼ < a1 ; a2 ; . . . ; an > a1, a2, an are the basic attributes of P, such as name, data type, action scope, value source, etc. These attributes of P can be extended according to the need of knowledge. The data type of P can be integer, float, or character strings. In terms of the action scope, there are two parameter types: global parameters and local parameters. A global parameter has its effect in every scope of the knowledge representation, while a local parameter only has effect in the sub-process of the knowledge representation where it is declared. Meanwhile, there are five types of parameters according to different sources of assignment value. These are: (1) Input directly. Values of this kind of parameter will be input by users at the beginning of the reasoning knowledge representation, such as the diameter of a hole or the length of a part.

(2) Select directly. Values of this kind of parameter can only come from the given list of choices. E.g., the way heat treatment in a manufacturing enterprise can either be ‘‘quenching and tempering treatment’’ or ‘‘solution aging treatment’’, the parameter-heat treatment way can be regarded as a type of selection directly. (3) Select based on rule. Values of this kind of parameter are determined by the result of a reasoning rule. The different values will be assigned respectively under different conditions. Taking the selection of machine tool models as an example, when a part is manufactured in a manufacturing enterprise, if the diameter of a hole feature is more than 230 mm, the selected model of the machine tool is ‘‘CA6163’’, otherwise, it is ‘‘CA6150’’. (4) Calculate. Values of this kind of parameter come from the result of calculation by formula. Since the data type of parameters can be numbers or character strings, formulae should support the computing for hybrid data types and the range of parameters should be extended as well. (5) Query database. Values of this kind of parameter come from the query results of database tables that are two dimension data tables with fields. These fields are predefined as input fields or output fields by query conditions. As conditions meet the requirement, the value of output fields of the selected records will be assigned to the parameter.

Definition 2. Parameter Table (PT)PT is a collection of parameters using a table form. It can efficiently support parameter storage, classification, discovery, and comparison. PT ¼ ðPT a ; P; UI; MÞ PTa refers to the attributes of PT, P stands for the parameter set in PT, UI is the user interface set of the PT, M is the manipulation set of PT, e.g., add, delete and edit. Definition 3. Chart Unit (CU)CU is the basic node that makes up the PFC, and presents an action of the knowledge expression through simulating human beings. These actions vary depending on the different application domains as well as the tasks and aims of these actions. This results in different attributes of CU. The types and attributes of CU can be built and extended according to the requirements of knowledge representation in different domains. Nine types of CU have been constructed in our research. CU can be expressed as a collection of these nine attributes. CU ¼ fBU; EU; VU; RU; OU; IU; NU; LU; SPFC g BU refers to the beginning of a PFC (see Fig. 2, BU1); EU refers to the end of a PFC (see Fig. 2, EU1); VU is responsible for assigning value for parameters (see Fig. 2, VU1, VU2); RU depicts the reasoning logics according to conditions. RU has two types: a single branch (see Fig. 2, RU1) or multiple branches (see Fig. 2, RU2). The RU with a single branch denotes the judgment of YES or NO logic, while the RU with multiple branches defines more than two output decisions with several conditions. OU defines the output information(see Fig. 2, OU1, OU2, OU3). For process planning, the work operations and their contents are the important output information with the attributes of operation name, operation content, machine tools, and tooling. IU is a message unit (see Fig. 2, IU1), which provides some useful information during knowledge reasoning; NU is a connector unit, which helps to connect different flow charts according to the same marker number; LU is the line with a arrow at one end side that connects various chart units. It indicates the flow direction of the diagram; SPFC represents a sub-process and nesting is available in the PFC (see Fig. 2, SPFC1). By establishing the database of SPFC, the whole knowledge can be broken up into parts

[(Fig._2)TD$IG]

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CR is classified as joined CR, unjoined CR, paratactic CR and branchless CR according to their orders when representing knowledge. Definition 5. Sub-Parameter Flow Chart (SPFC)SPFC is a sub-parameter flow chart that represents individual knowledge and is nested in the main parameter flow chart. It is similar to a subprogram in computer programming. By creating and calling several multi sub-PFCs, the nested PFC is composed as a result, which in turn, represents the whole complicated knowledge. 3.2. Principle of knowledge representation using PFC

Fig. 2. An example of representation form based on PFC.

and is available through calling the SPFC in the main process. So SPFC is useful for representing complicated and abundant knowledge. SPFC is often used to define typical process of a machining part or the process route of a manufacturing feature. Fig. 2 is a simple example of the representation form for process flow knowledge based on the PFC method. It is a visual and directional knowledge chart. The whole flow starts from BU1 and ends at EU1. VU1 and VU2 are used to define values of parameters. RU1 and RU2 are responsible for judging the input data and determining the next flow step. SPFC1 is a sub-PFC that is defined in another place. OU1, OU2, OU3 are chart units of output, such as work operations. Through ‘‘drawing’’ such a flow chart with predefined chart units, a rule-based process knowledge can then be represented. Definition 4. Parameter Flow Chart (PFC)PFC, is a directional chart for knowledge description and reasoning, which consists of parameter tables, chart units and the logical routes among the chart units. The contents and the structure of the knowledge are present in the PFC method. PFC ¼ < A; PT; CU; CR > A, PT, CU and CR refer to a set of attributes, parameter tables, chart units and the relations among the chart units of PFC respectively. [(Fig._3)TD$IG]

The approach based on PFC represents process knowledge through a directional graph called Parameter Flow Chart (PFC) which combines parameter technology, flow chart technology, and visual technology. By simulating the reasoning style of human beings and providing a visual environment, the structure and contents of process knowledge have been ‘‘captured’’. The description for knowledge is achieved through various CUs, e.g., conceptions and facts knowledge can be described by VU (assignment value chart unit) and control knowledge can be depicted by RU (reasoning chart unit). For process route knowledge, PFC itself is a directional graph of knowledge reasoning and the structure of the graph represents the process of knowledge reasoning. Compared to other approaches, operators can more clearly and vividly organize different process knowledge into the database. In order to represent uncertain and fuzzy process knowledge, the concept of parameter is introduced in this paper to solve this problem. By defining attributes of parameters and judging by rules and finally drawing the graph of knowledge reasoning process, dynamic and variable knowledge can be represented effectively. The different values of parameters will result in different judging conditions, which will then further the knowledge reasoning process. The structure of PFC itself stands for a path diagram of knowledge reasoning from which people can clearly see and easily understand how the rules and conditions affect the process of knowledge reasoning. From this point, PFC helps improve the visualization and intelligibility of process knowledge. The principle of knowledge representation approach based on PFC is divided into 5 tiers as shown in Fig. 3. (1) Abstract tier. The main aim of this tier is to abstract out process knowledge and related parameters which might affect the contents and reasoning process of knowledge. This is done by skilled employees or experts in a manufacturing enterprise.

Fig. 3. Principle of knowledge representation based on PFC.

[(Fig._4)TD$IG]

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via interactive interfaces including extraction and semantic description of the parameters. The process flow chart is defined using special software tools by which flow chart files are defined and ‘‘drawn’’. During the process of explaining and reasoning knowledge, the proposed PFC approach will read the related information from flow chart files and then load corresponding files through the interface before finally explaining and reasoning the flow chart. When this has been done successfully, the results will be produced via a neutral file, such as TXT and XML. 4.2. Model of the software system

Fig. 4. Process of representing knowledge based on PFC approach.

(2) Parameter tier. The task of this tier is to select, extract and define parameters used in PFC. Every selected parameter will be given its attributes, such as name, data type, action scope, value source, etc. All the defined parameters will form the PT. (3) Flow chart definition tier. The main task of this tier is to define and draw knowledge diagrams using the given chart units, such as BU, EU, VU, RU, etc. The uncertain contents are expressed by various parameters while the changeable process of reasoning knowledge is described using these parameters and RU, and thus the process knowledge chart is constructed in the end. (4) Explaining and reasoning tier. The tasks of this tier include reading the flow chart, validating and testing the knowledge by explaining and reasoning the parametric knowledge according to the given values of parameters. (5) Output tier. The defined flow charts can be classified and stored in a knowledge repository after validation. Results of explanation and reasoning of knowledge can be exported in some formats, such as TXT and XML. As a result, the parametric knowledge repository for manufacturing enterprises is built and will be helpful for reusing knowledge in future. 4. Approach implementation of process knowledge representation based on PFC 4.1. Process of representing knowledge using PFC approach The complete process of representing knowledge based on the [(Fig._5)TD$IG] approach is shown in Fig. 4. The parameters are first defined PFC

To implement the approach based on PFC, a model of the software prototype system has been constructed as shown in Fig. 5. The model is divided into four layers and seven functional logical units. Each layer is devoted to a specific task with the links between the frameworks of abstractions and functionalities from different levels. The database layer stores the system’s process knowledge defined by PFC. It also includes static resource knowledge such as machine tools, fixtures, cutters, machining data and materials, which might be queried by parameters defined in PFC. The knowledge application layer is responsible for validating the legitimacy of knowledge for the purpose of explaining and reasoning the knowledge, as well as classifying and storing the knowledge. It also deals with expression sentences with parameters. The knowledge definition layer is responsible for defining parameters and ‘‘drawing’’ flow charts through various chart units. The interface layer provides interoperable operations of human– computer interface to help knowledge experts use the software easily. To develop the proposed software system, there are two key issues that need to be solved. The first issue is the modeling of various chart units used in building process knowledge and the definition of the flow chart in an efficient and user-friendly manner. The other issue is to deal with expressions containing various parameters effectively. These are discussed in the following sections. 4.3. Modeling chart unit in PFC The distinct characteristic of the PFC approach is its visual representation of process knowledge using flow charts. The performance of creating and defining diagrams is very important as this may affect the level of visualization and readability in the approach. Microsoft Visio Standard 2003 is a very popular and

Fig. 5. software system model for approach based on PFC.

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widely used software tool and was selected as the basic tool for modeling flow charts and designing flow diagrams in our research. Microsoft Visio has been customized and integrated into the proposed software system. Microsoft Visio is a part of the Microsoft Office Suite of products which consists of Microsoft Word, Microsoft Excel, Microsoft Visio, Microsoft Access and Microsoft Project. Microsoft Visio is a diagramming tool that can be used to visually communicate technical as well as non-technical representations of ideas, processes, structures, layouts, software models, etc. It eliminates the laborious process of creating diagrams by providing the tools to create complex diagrams in a visual manner. It can therefore be

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used to help an engineer, as well as any non-technical user, to easily create many different kinds of diagrams. Although Microsoft Visio is a professional software tool for creating flow diagrams, it is not designed for knowledge representation and reasoning. To apply it in the representation of process knowledge, it has to be customized to meet the requirements of knowledge representation and reasoning. Shape is the essential element in a diagram of Microsoft Visio and each has its own attributes. Visio provides various shapes grouped into stencils, which can be used for creating different kinds of diagrams. To satisfy the need of knowledge representation, some special shapes as well as related programming interfaces to access them

Table 1 Chart units developed in PFC approach. Chart unit

Shape

Description

Example

Explanation of example

Assign value (VU)

[TD$INLE]

Assign values for parameters in PFC

[TD$INLE]

Assign value for parameter F_GXMC, CLPH, and IT. The value is ‘rough turning’, ‘HT200’, and 7 respectively

Define output contents of work operation in process

[TD$INLE]

Define the contents of a work operation, the Operation Name is ‘Chamfer’, the Operation Content is L1.5  458’, the Machine tool is ‘EQM8060’, and the Tooling is ‘J4-02P200  100  100’

Condition judgment of YES or NO logic

[TD$INLE]

There is two output branches according to the value of parameter YTJG, the value is either ‘Y’ or ‘N’

[TD$INLE]

Output work operation (OU)

Single branch (RU)

[TD$INLE]

Multiple branch (RU)

[TD$INLE]

Condition judgment with more than two output branches

[TD$INLE]

There is three output branches according to the value of parameter HJ, it can be ‘Friction welding’, ‘No welding’ or default value.

Sub-process (SPFC)

[TD$INLE]

Call a sub-process that is defined in another place

[TD$INLE]

Call sub-process named ‘‘working hour selection’’, pass the parameter value to sub-procedure, such as Z_Zhijing, Z_GXMC, Z_FBLX

Message (IU)

[TD$INLE]

Message box giving warning information or reminder

[TD$INLE]

Pop-up dialog box with information ‘‘Warning: The number of this part does not begin with ‘‘8EE.34’’, it can not be used here’’

Entrance (NU)

[TD$INLE]

Connect flow chart with the same number

[TD$INLE]

To find and connect with the shape numbered 10

Connector line (LU)

[TD$INLE]

Connect chart unit

[TD$INLE]

Connect chart unit of a single branch with chart unit of an output work operation

Begin/end (BU/EU)

[TD$INLE]

Indicate the begin/end of PFC

[TD$INLE]

The begin/end of flow chart when representing knowledge

[(Fig._6)TD$IG]

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Table 3 Mathematical operators, functions, and logical operators supported in PFC approach. Mathematical operators Mathematical functions

[(Fig._7)TD$IG]

Logical operators

+, , *, /, %, ( ), = sin( ), cos( ), tan( ), atan( ), atan2( ), asin( ), acos( ), exp( ), log( ), log10( ), sqrt( ), max( ), min( ), fabs( ), limit( ) AND, OR, NOT

Fig. 7. An example of a through hole.

Fig. 6. Chart units are grouped into stencil opened in Microsoft Visio 2003.

are developed using Microsoft Visio 2003 Software Development Kit (SDK). These shapes are called chart units in the PFC approach. To make a process knowledge diagram using these chart units, all you have to do is drag them into place, type some text, resize them a little, and connect them using connectors which are lines connecting shapes in Microsoft Visio. The developed shapes called chart units in the PFC approach and are introduced and shown in Table 1. All these developed chart units are grouped into stencils that can be used for creating various knowledge of process flow. The working interface for these chart units in Microsoft Visio is shown in Fig. 6. 4.4. Computing of expression containing parameters Parameter is a very important concept in the PFC approach for representing process knowledge. The description of knowledge is achieved using a number of parameters and combinations of characters, and the structure of knowledge is defined by a flow diagram consisting of various connected chart units. The judgment,

statement, and control of process knowledge are all realized through chart units. The diagram represents a variable knowledge and its real contents are determined by the value of parameters. The descriptions of process knowledge are called expressions and are often combined with numbers, character strings, and parameters. The value of a parameter is usually a scope and can be assigned as medium value, max value or min value. Therefore, the computing of expressions is mixed with mathematical, logical, and relational operations. Owing to these specific characteristics of process knowledge, there must be an effective way to deal with expressions containing various parameters. Some new relation operators of expression are extended in our research as shown in Table 2 (* refers to the new extended relation operators). The mathematical operators, functions, and logical operators supported in the PFC approach are shown in Table 3. 5. Case study A software prototype system has been developed to validate and demonstrate the feasibility and compatibility of the proposed PFC approach for representing process knowledge. Microsoft Visual C++ 2005 is employed to develop the framework and functional modules. Microsoft SQL Server 2005 is utilized to construct the system’s process knowledge repository. Microsoft Visio 2003 is customized and integrated into the system for ‘‘drawing’’ process flow charts. Three typical examples of process knowledge representation based on PFC are used to illustrate the related functionality of the prototype system.

Table 2 Relational operators supported in PFC approach.

[TD$INLE]

Operator

Function

Operator

Function

> < <= >= <>

Greater than Less than Less than or equal to Greater than or equal to Not equal to

Is * Is not * [ ]* Ø* *

=

Equal to

*

*

Include Linear interpolation

"* v*

A string is equal to another string A string is not equal to another string Judges whether the value of a parameter is in the given range or not Null word Search the datum in database record whose value is greater than the parameter value and the absolute value of the error is less Search the datum in database record whose value is less than the parameter and the absolute value of the error is least Judges whether the front characters of a parameter is equal to the compared character string or not Judges whether the back characters of a parameter is equal to the character string or not

W.L. Chen et al. / Computers in Industry 62 (2011) 9–22

5.1. Representation of process knowledge for manufacturing features using PFC A manufacturing feature can be simply regarded as a geometric

[(Fig._9)TD$IG]

[(Fig._8)TD$IG]shape and its manufacturing information to create the shape. In

17

process planning, it is very important to determine the detailed machining information for each manufacturing feature by identifying the operation sequence of the designed part, the operation type, the machine tools, the cutting tools, and the removed machining volume for each feature. Some common

Fig. 8. Define parameters used in PFC for hole feature.

Fig. 9. Represent process route of through hole using PFC.

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Table 4 Partial recommended machining approach for through hole [37]. Conditions

Recommended machining approach

Accuracy grade (IT)

Surface roughness Ra (mm)

Material

11–12 9–10 7–8 6–7

12.5–6.3 3.2–1.6 1.6–0.8 0.8–0.4

Steel Steel Steel Steel

(except (except (except (except

hardened hardened hardened hardened

steel), steel), steel), steel),

rough rough rough rough

has has has has

hole hole hole hole

[(Fig._10)TD$IG]

Fig. 10. Drawing of an example valve.

manufacturing features such as holes, slots, and pockets have their own typical machining approaches which have been validated by industrial practice. Thus these manufacturing approaches can be predefined in the database as a kind of process knowledge. Here, a through hole feature is utilized as an example to introduce how its machining knowledge can be represented using the PFC approach. The through hole feature shown in Fig. 7 can be described by three main parameters from the view of manufacturing—the hole diameter (shown as D in Fig. 7), the surface roughness (shown as Ra in Fig. 7), and accuracy grade (shown as IT in Fig. 7). The machining route of the hole feature may vary depending on the value of these parameters. This has been summed up from practice and partially shown in Table 4. To represent the process knowledge shown in Table 4 by PFC, parameters are first extracted and defined in the prototype system. Diameter, surface roughness, accuracy grade and material of the through hole are defined as global parameters and named as D, Ra, [(Fig._1)TD$IG]IT, CL respectively. Their data types and ways of assigning values

Rough boring Rough boring, semi-fine boring Rough boring, semi-fine boring, fine boring Rough boring, semi-fine boring, fine boring, fine boring with floating boring tool

are also defined. The machining allowance of operations varies with operation type and can be obtained by query standard machining database. Three local parameters entitled YL1, YL2, YL3 stand for machining allowance in the operations. A local parameter named SBXH is defined to represent machining tools used in operations. The definition interface of parameters is shown in Fig. 8. After defining parameters, Microsoft Visio is called to define and draw the flow chart using the developed chart units. The process knowledge of the hole feature shown in Table 4 can be represented as a diagram through using PFC as shown in Fig. 9. The description of process knowledge for the through hole starts with the chart unit ‘begin’ and ends with the chart unit ‘end’. The whole process flows following the direction of the arrows as seen in Fig. 9. The value of parameter IT is judged at the beginning. If IT  8, it goes down along the Y direction, otherwise, it goes right along the N direction. If IT > 8 and IT  10, the output operation is rough boring and semi-fine boring respectively as indicated by the suggested machining route in Table 4. The output operation has three attributes, which are namely the Operation Name, Operation Content, and Machine Tool. The value of the attribute is also defined by parameters, e.g., for the operation of ‘‘Rough boring’’, its content is expressed as ‘Boring hole to dimension L’+str(D-YL1). The statement is a sentence with parameter computing. When D is set valued 100 andYL1 is set valued 3, the real statement is a character string of ‘Boring hole to dimensionL97’. Hence, the content of knowledge is changeable and is determined by the given values of parameters. The structure of knowledge is also changeable according to a parameter’s value and judging conditions. A whole parametric knowledge description for the hole feature is then achieved using different chart units and defining attributes of these chart units. Knowledge representation for other manufacturing features can be achieved in a similar manner.

Fig. 11. A typical process route of valves.

[(Fig._12)TD$IG]

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Fig. 12. Define parameters used in PFC for valve.

Finally, the complete process route for a valve can be represented using various chart units.

5.2. Process knowledge representation for machining parts using PFC We demonstrated and validated the developed prototype in an auto parts company which mainly develops and manufactures valves of various auto engines. This valve example is given to illustrate the implementation of PFC for machining parts of an auto engine (see Fig. 10). The structure of the valve seems quite simple but plays a key part for engine control in terms of air admittance and exhaust and its working environment is formidable. Thus, the requirements for mating precision, capability and quality are very high. There are many factors influencing the process planning of a valve and the process route is quite complicated, including cold working, hot working, etc. Sometimes the process route might even consist of nearly 40 operations. A typical process route for valve is showed in Fig. 11. The key elements which influence the process route of valves include material, the diameter of the pole (d1 in Fig. 10), the diameter of the tray (d2 in Fig. 10), welding processes, and so on. These are extracted and defined as parameters. Each parameter needs to be defined in terms of its action scope, data type, way of assigning value, etc. The definition interface of a valve’s parameters is shown in Fig. 12. After finishing the parameter definition, Microsoft Visio is then called to define and draw the flow chart using the developed chart units. Fig. 13 shows the partial diagram of knowledge representation using PFC. First, a parameter (HJ, welding mode) is judged in a RU with multiple branches. If HJ equals to ‘‘Friction welding’’, the subprocess named ‘‘Material selection’’ will be called. If HJ equals to ‘‘No welding’’, the work operation named ‘‘punch blanking’’ will be produced. The operation content is expressed by a string ‘‘L = 288  0.5,’+str(d)+’h10’’, here d is the diameter of the pole, str(d) depicts the numeric parameter d to be transformed into a string. For example, if the value of d is 8.5, the operation content will be explained as ‘‘L = 288  0.5,8.5h10’’. For the other value of parameter HJ, the default output of current RU is the work operation named ‘‘Cutting material’’. Parameter YTJG in Fig. 13 indicates whether the round head of a valve is machined or not; if it is, the sequencable work operations of ‘‘chamfer’’, ‘‘turn round head’’ and ‘‘grind end surface’’ are generated, otherwise, only one work operation of ‘‘chamfer, grind end face’’ is produced.

[(Fig._13)TD$IG]

5.3. Process knowledge representation for a component of product using PFC The proposed PFC approach has also been used and validated in a large enterprise in China which develops and manufactures large power plant boiler products. An important component of a boiler

Fig. 13. Represent process route of valve using PFC.

[(Fig._14)TD$IG]

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Fig. 14. Drawing of power boiler’s header.

product, the boiler’s header shown in Fig. 14, is used to illustrate the implementation of PFC. The process route for a boiler’s header is quite complex and has dozens of operations. Its process planning might be influenced by some key elements such as material, trademarks, diameters of ontology, hand opercle devices, and the argon arc welding for pipe fittings. Based on the case examples discussed above, the definition interface of a boiler header’s parameters is shown in Fig. 15. After defining parameters, Microsoft Visio is called to define and draw the flow chart using the developed chart units. Fig. 16 shows the partial diagram of process route knowledge representation using PFC. It follows another PFC numbered A. First, a parameter (SKG, hand opercle device) is judged in a RU with single branch. If SKG equals to ‘‘No’’, the process route will go along the right side of the figure. Then if parameter fkxl is ‘N’, the work operation whose content is ‘‘Install hand hole cover and plug

[(Fig._15)TD$IG]

screw’’ will be produced. From the figure, it can be seen its next work operation’s content is to inspect final dimension. Then, another RU appears, it has five conditions which are numbered from 1 to 5. The logical relationship’s expression of this RU is ‘‘1j2j(3&4&5)’’. If the calculation result is 1, it means YES, and the next work operation is to ‘Install plastic tube caps for all pipe fittings to prevent sundries fall into part’. If the calculation result is 0, it means NO, and the process route of boiler’s header is finished. By drawing various chart units and defining the flow direction of these chart units, the complex and whole process route of the boiler’s header can be represented clearly. The process knowledge of more than twenty components of the power boiler has been built using the PFC approach in this company. All of these were created by the workers themselves through using our developed software tool based on the proposed approach.

Fig. 15. Define parameters used in PFC for boiler’s header.

[(Fig._16)TD$IG]

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Fig. 16. Representing the process route of a boiler’s header using PFC.

6. Conclusion and future work This paper proposed a new approach for representing process knowledge using PFC. The goal of this research is to develop a new method for capturing and representing process knowledge. This is important for OKP companies producing customized products of various types simultaneously. The proposed PFC-based method can be used as a standard tool for these companies to establish their unique process knowledge base. Three case studies were carried out to validate and demonstrate the developed prototype system. The following conclusions are drawn based on our investigation and experiments: (1) The PFC approach is able to effectively represent the type of process knowledge that carries flow information. This has been demonstrated in our case studies. The flow type of knowledge is defined using various chart units in Microsoft Visio 2003. The flow diagram drawn by Microsoft Visio represents the knowledge.

(2) Uncertain process knowledge can also be represented efficiently by proper parameters whose value can be selected and defined by users. Using the proposed method, the accuracy of traditional machining processes was modeled and compensated. (3) The proposed approach is open and extensible. It can be used to represent various flow types of knowledge for manufacturing features or machining parts as validated in our case studies. (4) The proposed approach is user-friendly. Microsoft Visio, the most popular diagramming tool, is easy to learn and use and is utilized to define process flow knowledge. Skilled employees or experts can build process knowledge themselves without having much knowledge on information technology and software programming. Future work includes the following three major aspects: (1) The prototype system and the model of chart units and software modules need to be further tested and validated in different OKP companies.

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(2) The chart unit in the PFC approach needs to be further expanded to improve its flexibility. This will also enhance the ability of representing other engineering knowledge aside from process knowledge. (3) The PFC approach needs to be integrated with other product development systems such as computer-aided process planning (CAPP) and computer-aided manufacturing (CAM).

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[26] V. Lopez-Morales, O. Lopez-Ortega, A distributed semantic network model for a collaborative intelligent system, Journal of Intelligent Manufacturing. 16 (4–5) (2005) 515–525. [27] M.R. Gholamian, S.M.T. Fatemi Ghomi, Meta knowledge of intelligent manufacturing: an overview of state-of-the-art, Applied Soft Computing 7 (1) (2007) 1–16. [28] Y. Tu, X. Chu, W. Yang, Computer-aided process planning in virtual one-of-a-kind production, Computers in Industry 41 (1) (2000) 99–110. [29] K. Waiyagan, E.L.J. Bohez, Intelligent feature based process planning for five-axis mill-turn parts, Computers in Industry 60 (5) (2009) 296–316. [30] B. Sun, H. Wang, Research on standardization of CAPP information models based on XML, Journal of Xi’an Technological University (2007). [31] S. Zhang, S. Laigang, Knowledge and XML based CAPP system, Chinese Journal of Mechanical Engineering (English Edition) 19 (3) (2006) 344–347. [32] C.J. Hu, Z.Z. Li, L. Zheng, N. Li, P.H. Wen, An XML-based implementation of manufacturing route sheet documents for context-sensitive and web-based process planning, International Journal of Computer Integrated Manufacturing 21 (6) (2008) 647–656. [33] T. Kojima, S. Ohtani, T. Ohashi, A manufacturing XML schema definition and its application to a data management system on the shop floor, in: Robotics and Computer-Integrated Manufacturing, ICMR2005: Third International Conference on Manufacturing Research, vol. 24, issue 4, 2008, 545–552. [34] C.H. Chen, Z. Rao, MRM: a matrix representation and mapping approach for knowledge acquisition, Knowledge-Based Systems 21 (4) (2008) 284–293. [35] S.J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Englewood Cliffs, 2003. [36] K.H. Yang, D. Olson, J. Kim, Comparison of first order predicate logic, fuzzy logic and non-monotonic logic as knowledge representation methodology, Expert Systems with Applications 27 (4) (2004) 501–519. [37] H. Chen, Manual of Machining Process, China Machine Press, Beijing city, 2008. Wanling Chen is currently a postdoctoral research fellow working at the Department of Mechanical Engineering, the University of Auckland, New Zealand. He received his BE, ME, and PhD degrees in mechanical engineering from Huazhong University of Science and Technology (HUST), China. His current research interests include CAD/CAPP/ PLM, knowledge-based engineering, enterprise integration, mass customization, etc.

S.Q. (Shane) Xie received the MSc and PhD degrees from Huazhong University of Science and Technology (HUST), China, in 1992, 1995, and 1998 respectively. He also received a PhD degree from the University of Canterbury, Canterbury, New Zealand, in 2002. He was a research associate and Postdoctoral Fellow at the University of Canterbury. He joined the University of Auckland, Auckland, New Zealand, in 2003, and is currently a senior lecturer in the area of mechatronics and he leads a group working in medial robotics and infomechatronics. His current research interests are mechatronics, smart sensors and actuators, rehabilitation and medical robots, MEMS, modern control technologies and applications, and rapid product development technologies, methods and tools. He is the editor of two international journals, and is the guest editor, member of editorial boards and reviewer of many international journals and conferences. He has also published more than 150 papers in refereed international journals and conferences. Fenfang Zeng is currently an associate professor at School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China. Her current research interests include CAD/CAPP/PLM/MES, system integration, etc.

Bomiao Li received her BE (2006) and ME (2009) from Huazhong University of Science and Technology (HUST), China. She also received a BSc degree (2006) from Wuhan University, China. Currently she is a PhD candidate in mechatronics at the University of Auckland and doing research in the field of One-of-a-Kind Production development technologies, methods and tools.