Size Effects in Micro-scaled Plastic Deformation

  • Ming Wang Fu
  • Wai Lun Chan
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


Microforming, the so-called micro-scaled plastic deformation, is to fabricate the parts or part features with the dimensions in submillimeter scale. The process has great potential to become a promising micromanufacturing method for its unique characteristics for fabrication of micro-formed parts [1]. Although a comprehensive macroforming knowledge system to support the design of process, tooling, and the metal forming part has been well established and widely used [2, 3, 4, 5, 6], and the development of microparts by microforming, however, cannot totally be based on the traditional macroforming knowledge and the design and development paradigm of macro-formed parts as the size effect affected deformation behaviors and process performance in microforming are different from the ones in macroforming [7, 8, 9]. In microforming, the material deformation behavior is characterized by a few grains in the deformation zone. Different properties of grains make the deformation behavior inhomogeneous and difficult to predict. In addition, there are interactive effects between workpiece size and microstructure on flow stress, flow behavior, fracture behavior, elastic recovery, and surface roughening, etc. These size effect-related deformation phenomena further affect the performance of microforming system and product quality in terms of deformation load, stability of forming system, defect formation, dimensional accuracy, surface finish, and the mechanical properties of the micro-formed parts. This chapter aims at discussing the size effect-related deformation behaviors and the newly identified phenomena, which will help understand the mechanisms and fundamentals of the size effects in microforming processes.


Flow Stress Slip System Deformation Behavior Specimen Size Electrical Discharge Machine Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringThe Hong Kong Polytechnic UniversityHung HomHong Kong
  2. 2.The Hong Kong Polytechnic UniversityHong KongPeople’s Republic of China

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