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大連交通大學(xué)2017屆本科生畢業(yè)設(shè)計(jì)外文翻譯
knowledge-based blackboard framework for stamping process planning in progressive die design
S.B. Tor · G.A. Britton · W.Y. Zhang
Springer-Verlag London Limited 2004
Abstract： It is widely accepted that stamping process planning for the strip layout is a key task in progressive die design. How-ever, stamping process planning is more of an art rather than a science. This is in spite of recent advances in the field of artificial intelligence, which have achieved a lot of success in incorporating built-in intelligence and applying diverse know-ledge to solving this kind of problem. The main difficulty is that existing knowledge-based expert systems for stamping process planning lack a proper architecture for organizing heterogeneous knowledge sources (KSs) in a cooperative decision making en-vironment. This paper presents a knowledge-based blackboard framework for stamping process planning. The proposed ap-proach speeds up the progressive die design process by automat-ing the strip layout design. An example is included to show the effectiveness of the proposed approach.
Keywords ：Knowledge-based · Object-oriented · Progressive die design · Stamping process planning
1.Introduction
Progressive dies for producing sheet metal parts in mass pro-duction have been widely applied in various industries such as aerospace, electronics, machine tools, automobiles, and re-frigeration. These dies can perform piercing, notching, cut-off, blanking, lancing, bending, shaving, drawing, embossing, coin-ing, trimming, and other miscellaneous forming operations at a single setup. Hence, a progressive die is generally very com-plex. Stamping process planning and die structure design are difficult and demanding tasks.
Stamping process planning starts with an unfolding of a model of stamped metal part to produce a flat pattern, followed by nesting the pattern to produce a blank layout. Next, stamping operations are planned and operations are assigned to die sta-tions. The resulting plan is typically represented as a strip layout, which guides the subsequent die structure design. The produc-tivity, accuracy, cost, and quality of a progressive die mainly depends on the strip layout, and hence a stamping process. How-ever, stamping process planning still remains more of an art rather than a science. Historically, this activity is mainly car-ried out manually, based on designers’ trial-and-error experience, skill and knowledge.
Recent advances in the field of artificial intelligence (AI) have given rise to the possibility to construct AI-based systems that incorporate built-in intelligence and apply diverse knowledge to solving progressive die design problems, including strip layout design automation. The diverse knowledge sources (KSs) re-lated to stamping process planning include unfolding knowledge to produce a flat pattern, nesting knowledge to produce a blank layout, mapping knowledge to transform stamping features into stamping operations, and staging knowledge to sequence the stamping operations. A discussion of some knowledge-based pro-gressive die design work related to our study can be found in Sect. 2. However, the existing work is based on the conventional architecture of knowledge-based expert systems, which are in-capable of managing heterogeneous KSs effectively. This limits both their practicability and scalability.
To address the above issue, it is necessary to provide a coop-erative problem solving strategy that can foster communication between diverse KSs, and accommodate different knowledge representation schemes within an integrated framework. Hence, a knowledge-based blackboard framework consisting of a black- board control system and a few independently executing KSs have been developed. This framework provides a cooperative de-cision making environment and facilitates a hybrid knowledge representation scheme, including procedures, production rules, and object-oriented representations.
A prototype system has been implemented using the object-oriented expert system shell CLIPS (C Language Integrated Pro-duction System) [1], which is interfaced with a parametric- and feature-based CAD system, Solid Edge through C++. An ex-ample is provided to demonstrate our approach and to show its effectiveness in stamping process planning.
2.Related work
Research in the computer-aided stamping process planning has been widely reported since the 1970s. The advantages of auto-mated process planning are productivity improvements, cost re-ductions, and design automation.
From the mid 1970s to mid 1980s, the first generation of CAD/CAM systems for progressive die design were de-veloped [2–5], though few of them are based on AI techniques. These early systems are characterized by basic computer graph-ics facilities, standardization of die components, and standard-ization of design procedures. They reduced design and drafting lead time. However, as these systems represent design know-how in the form of conventional procedural programming languages, only generation of the die part list and drafting of the assembly and part drawings are executed using computers. The designer still needs to decide most of the important decisions interactively, including strip and die layouts.
Since the late 1980s, significant efforts have been made by worldwide researchers to integrate a wide variety of AI and traditional CAD approaches to develop dedicated progressive die design automation systems, including strip layout design automation.
Knowledge-based approach is a popular AI technique that has been used in intelligent stamping process planning and die design system. For example, researchers at the University of Massachusetts, USA have described a knowledge-based sys-tem for design of progressive stamping dies for a simple hinge part [6]. The system generates the flat pattern geometry and de-velops a strip layout automatically. Researchers at the National University of Singapore have been developing an intelligent pro-gressive die (IPD) design system since the late 1980s. They used feature modeling and rule-based approach to realize automatic punch shape selection, strip layout development, and 3-D die configuration [7, 8]. Based on a feature-relationship tree that de-scribes the stamped metal part and its topological information, model-based reasoning and spatial reasoning techniques have been employed to reason out certain stamping processes and guide the overall planning process to develop the strip layout automatically. Researchers at the Indian Institute of Technology have developed a computer-aided die design system, CADDS, for sheet-metal blanks [9], based on heuristic rule-based reason-ing and parametric programming techniques. The greatest advan- tage achieved by the system is the rapid generation of the most efficient strip layouts. Researchers at the University of Liverpool have worked on design automation for progressive piercing and blanking dies [10, 11]. Their work is based on applying a coding technique to characterize the stamped part geometric features, which is subsequently used to generate the type and layout of the die punches, and then develop the strip layout automatically. Researchers at Huazhong University of Science and Technol-ogy, China, have developed an intelligent progressive die design system, HPRODIE [12]. With feature mapping, rule-based rea-soning and case-based reasoning techniques, most of the design processes including strip layout design can be carried out auto-matically. Researchers at Pusan National University, Korea, have developed a compact computer-aided process planning (CAPP) system for progressive die design [13]. Based on production rules, the work is capable of carrying out an intelligent stamp-ing process planning work with automatic development of blank layout, strip layout and die layout.
Though knowledge-based systems have achieved a lot of suc-cess in stamping process planning, most of the intelligent pro-gressive die design automation prototypes reviewed above are rather restricted to specific application domains, or still need considerable interactive input from experienced designers to de-velop strip layouts. This is because they still inherit the disadvan-tages of the conventional architecture of knowledge-based expert systems, which are incapable of managing heterogeneous KSs effectively.
Researchers at the National Taiwan Institute of Technology have adopted various AI techniques including fuzzy reasoning, pattern recognition, rule-based reasoning, back-propagation neu-ral network, genetic algorithms and Petri nets for the stamping process planning and design of progressive shearing cut and bending dies [14–16]. However their work lacks an explicit and consistent model to integrate these AI techniques into a compre-hensive design environment.
In this paper, another popular AI technique, blackboard ar-chitecture, is adopted to develop a blackboard-based stamping process planning system. In the last two decades, blackboard ar-chitecture has been successfully used in a wide variety of areas, such as speech recognition, signal processing, engineering de-sign and process planning. Thompson and Lu [17] used a black-board architecture to provide a cooperative decision making en-vironment that is suitable for concurrent product and process design. Srihari et al. [18] developed a real-time CAPP system for printed circuit board (PCB) assembly by integrating multiple KSs, including planning expert and dynamic information pro-cessing modules in the blackboard architecture. Chen et al. [19] developed a concurrent product design evaluation system, using a blackboard architecture to classify knowledge into diverse KSs suitable for qualitative and quantitative evaluation, respectively.
In the past few years, blackboard architecture has proven to be suitable for tooling design such as fixture design [20] and in-jection moulding design [21], though this kind of application is still in its infancy stage. Roy and Liao [20] report the preliminary work that investigates the suitability of using a blackboard archi-tecture as a [K1]problem solving model for fixture design. It de- scribes the creation of various functional KSs for fixture design and their organization in a cooperative problem solving environ-ment. Kwong et al. [21] proposes a blackboard-based system for concurrent process design of injection moulding, which facili-tates the simultaneous considerations of moulding part design, tool design, machine-selection, production scheduling, and cost as early as possible in the conceptual design stage. However, we have not found in the literature any attempt to apply the blackboard architecture to stamping process planning for sheet metal parts. It has been mentioned in our earlier work [22] that a blackboard architecture is well suited for constructive prob-lem solving, like process planning of stamping operations, where the problem space is large and knowledge from many different sources must be integrated to achieve a solution. This topic is now to be extensively elaborated in the present paper.
3.Blackboard framework for stamping process planning
Cooperative decision making for knowledge-based stamping process planning involves a variety of KSs such as unfolding knowledge to produce flat pattern, nesting knowledge to produce blank layout, mapping knowledge to transform stamping features into stamping operations, and staging knowledge to sequence the stamping operations. These KSs may be expressed in different representation schemes such as procedures, rules, and objects. This justifies the use of a blackboard framework that can man- age heterogeneous KSs effectively. The KSs interact through the blackboard to develop a solution incrementally.
The proposed blackboard framework consists of three major components: the blackboard data structure, KSs, and a control module (Fig. 1), and was developed using object-oriented expert system shell CLIPS. The different components of the blackboard framework are described as follows.
3.1 Object-oriented blackboard data structure
The blackboard is a globally accessible database, which con-tains the data and partial solutions and is shared by a number of independent KSs. The KSs contribute their partial solutions to the blackboard, which lead to a final solution incrementally. The blackboard is structured as a hierarchy of solution parti-tion levels, which represent different aspects or stages of the solution process. Partial solutions are associated with each level and may be linked to information on other levels using algorith-mic procedures or heuristic rules. Each level contains planning objects that are used to represent the solution space in an object-oriented manner. This leads to the added advantage in knowledge system development because object-oriented approach supports software modularity, reusability, and scalability.
Referring to Fig. 1, the planning solution is partitioned into four different object levels: stamping part, stamping features, stamping operations, and stamping process plan, each represent-ing initial input or different partial solutions posted on the black-board by the specialist KSs. They are described as follows.
3.1.1 Input data to the blackboard
Input data to the blackboard mainly includes the part and press ob-jects. The generic declaration of a part object includes the basic attributes such as part type, part dimensions, weight, surface treat-ments, blank thickness, blank material, annual production, blank dimensions, etc., and points to its constituent stamping feature ob-jects that will be elaborated later on. The press object contains the attributes such as press type, press tonnage, bolster dimensions, bed open dimensions, shut height, number of strokes, etc.
3.1.2 Object-oriented feature modeling to stamped metal parts
Since traditional geometric modeling techniques do not capture design intent (e.g., design for manufacturing), they are in gen-eral unable to support sophisticated and intelligent reasoning capabilities, e.g., knowledge-based process planning. Recently, the concept of machining features has been introduced to cre-ate a direct link between design and manufacturing [23]. Feature modeling is a relatively new way of storing design and manu-facturing information in CAD/CAM/CAPP systems. Similarly, stamping features of a stamped metal part can enable stamping process planning tasks to be performed directly from the geo-metric model. Stamping features are information carriers that are used to model a stamped part with a set of design and manu-facturing information including geometric and non-geometric at-tributes. Each of these stamping features can be manufactured with a specific stamping operation or a combination of stamping operations.
Using the hierarchical classification structure of general de-sign features by Chen et al. [24], a stamped metal part can be modeled with four categories of stamping features:
Primary features: flat, drawing, etc.;
Positive secondary features: tab, curl, emboss, hem, bead, flange, etc.;
Negative secondary features: hole, extrusion hole, profile, de-form, slot, step, etc.; andConnective secondary features: bend, blend, etc.
In this work, the object-oriented feature representation is em-ployed to encapsulate design and manufacturing information in a stamping feature object. For example, a hole feature object contains the basic attributes such as feature type, feature ID, pri-mary feature ID, position, orientation, depth, diameter, precision, roughness, etc., and methods to calculate perimeter.
Besides representation of individual stamping features, a comprehensive representation of feature relations guarantees that all the stamping features associated with stamping process planning are considered. In addition, the data on feature relations are useful for determining the sequence of stamping operations and sometimes the stamping operations themselves. Four criti-cal types of relations among stamping features – “is-in”, “is-on”, “adjacent-to” and “precision-associated” are identified, which have been elaborated in our previous work [25] and won’t be repeated in this paper for conciseness. For example, a precision-associated relation represents design constraints that arise when a stamping feature does not directly connect to, but is associ-ated with, another stamping feature by a toleranced dimension. The feature relation data is also included in the feature object for more complete feature modeling.
3.1.3 Stamping operation objects mapped from stamping feature objects
On the blackboard, the stamping operation objects are in a lower level than the stamping feature objects, and are used to define the manufacturing process from metal strip to the formed metal part. Stamping features constitute a stamped part, while stamp-ing operations are selected as elements of a stamping process plan. Essentially, the stamping process planning task is to trans-form a set of stamping features into a set of stamping opera-tions, and to describe the relations between these. The generic declaration of a stamping operation object includes stamping op-eration type, geometric shapes, geometric constraints, precision, roughness, relationships with stamping features, control param-eters, etc. Typical stamping operation objects include piercing, notching, cut-off, blanking, lancing, shaving, drawing, emboss-ing, coining, trimming, and other miscellaneous forming opera-tions. A stamping feature may be manufactured with a specific stamping operation (one-to-one mapping) or a combination of stamping operations (one-to-many mapping). Several stamping features may also be manufactured with a single stamping oper-ation (many-to-one mapping).
3.1.4 Graph-based stamping process plan
After the mapping from stamping features to a set of stamping operations, the remaining process planning task is to assign each stamping operation to the relevant die station according to an op-timal sequence of stamping operations. Stamping operations are sequenced in a progressive manner by creating stamping opera-tion relations and using them to form a stamping process plan. This formal description of operation relations forms the founda-tion of automatic strip layout design.
A graph-based approach is used to arrange the stamping op-eration objects in a stamping process plan. The graph consists of a set of nodes that store information about the stamping opera-tions, and a set of arcs that store information about the operation relations. Stamping operations are related to one another through two kinds of relationship, “cluster” or “precedence” relations. Cluster stamping operations are executed simultaneously and can be staged at the same die station. Stamping operations in prece-dence must be performed in sequence and so they are staged in adjacent die stations. Cluster relation, and precedence rela-tion are represented by dashed ellipses and directed solid line, respectively, as shown in Fig. 2. Note that stamping operations B and C work simultaneously, and are staged at the same die station, while stamping operation A precedes operation B, and is staged in a die station immediately prior to the one for the operation B.
The strip layout can be generated by a computer automat-ically using the graph-based stamping process plan, which is suited for computer implementation and leads to efficient formu-lation and solution procedures.
以沖壓工藝規(guī)劃知識進(jìn)行的級進(jìn)模設(shè)計(jì)
摘 要
人們普遍認(rèn)為沖壓工藝規(guī)劃的布局是級進(jìn)模設(shè)計(jì)中的關(guān)鍵任務(wù)。有史以來，沖壓工藝規(guī)劃是一門藝術(shù)，而不是一門科學(xué)。雖然人工智能在將內(nèi)置的智能和應(yīng)用多樣化的知識窗臺解決這類問題已經(jīng)取得了很多最新進(jìn)展。而現(xiàn)在主要的困難是，現(xiàn)有的基于知識的專家系統(tǒng)—沖壓工藝規(guī)劃缺乏適當(dāng)?shù)募軜?gòu)組織異構(gòu)知識源（KSS）的合作決策EN-vironment。本文提出了沖壓工藝規(guī)劃知識型面板框架。建議AP-proach通過自動(dòng)售貨機(jī) - 荷蘭國際集團(tuán)的帶狀布局設(shè)計(jì)，加快了級進(jìn)模設(shè)計(jì)過程。
關(guān)鍵詞：基于知識 面向?qū)ο?級進(jìn)模具設(shè)計(jì) 沖壓工藝規(guī)劃
1.引言
級進(jìn)模在大眾中生產(chǎn)鈑金件已被廣泛應(yīng)用于各種行業(yè)，如航空航天，電子，機(jī)床，汽車。這些模具可在一次裝夾完成穿孔，開槽，切斷，落料，彎曲，刮，拉絲，浮雕，修剪，和