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 perse 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.67747

Keywords Blackboard framework · Graph-based · 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 perse knowledge to solving progressive die design problems, including strip layout design automation. The perse 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 perse 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.