Reliability-Based Design Optimization of Surface-to-Surface Contact for Cutting Tool Interface Designs

Author(s):  
Soner Camuz ◽  
Magnus Bengtsson ◽  
Rikard Söderberg ◽  
Kristina Wärmefjord

In recent years, cutting tool manufacturers are moving toward improving the robustness of the positioning of an insert in the tool body interface. Increasing the robustness of the interface involves designs with both chamfered and serrated surfaces. These designs have a tendency to overdetermine the positioning and cause instabilities in the interface. Cutting forces generated from the machining process will also plastically deform the interface, consequently, altering the positioning of the insert. Current methodologies within positioning and variation simulation use point-based contacts and assume linear material behavior. In this paper, a first-order reliability-based design optimization framework that allows robust positioning of surface-to-surface-based contacts is presented. Results show that the contact variation over the interface can be limited to predefined contact zones, consequently allowing successful positioning of inserts in early design phases of cutting tool designs.

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879333 ◽  
Author(s):  
Zhiliang Huang ◽  
Tongguang Yang ◽  
Fangyi Li

Conventional decoupling approaches usually employ first-order reliability method to deal with probabilistic constraints in a reliability-based design optimization problem. In first-order reliability method, constraint functions are transformed into a standard normal space. Extra non-linearity introduced by the non-normal-to-normal transformation may increase the error in reliability analysis and then result in the reliability-based design optimization analysis with insufficient accuracy. In this article, a decoupling approach is proposed to provide an alternative tool for the reliability-based design optimization problems. To improve accuracy, the reliability analysis is performed by first-order asymptotic integration method without any extra non-linearity transformation. To achieve high efficiency, an approximate technique of reliability analysis is given to avoid calculating time-consuming performance function. Two numerical examples and an application of practical laptop structural design are presented to validate the effectiveness of the proposed approach.


AIAA Journal ◽  
2014 ◽  
Vol 52 (4) ◽  
pp. 711-724 ◽  
Author(s):  
Ricardo M. Paiva ◽  
Curran Crawford ◽  
Afzal Suleman

2004 ◽  
Vol 127 (5) ◽  
pp. 851-857 ◽  
Author(s):  
Anukal Chiralaksanakul ◽  
Sankaran Mahadevan

Efficiency of reliability-based design optimization (RBDO) methods is a critical criterion as to whether they are viable for real-world problems. Early RBDO methods are thus based primarily on the first-order reliability method (FORM) due to its efficiency. Recently, several first-order RBDO methods have been proposed, and their efficiency is significantly improved through problem reformulation and/or the use of inverse FORM. Our goal is to present these RBDO methods from a mathematical optimization perspective by formalizing FORM, inverse FORM, and associated RBDO reformulations. Through the formalization, their relationships are revealed. Using reported numerical studies, we discuss their numerical efficiency, convergence, and accuracy.


Author(s):  
Kyung K. Choi ◽  
Jian Tu ◽  
Young H. Park

Abstract This paper presents a design potential concept for reliability-based design optimization by integrating the probability analysis into the design optimization process. The reliability-based system parameter design is illustrated by the design potential concept in a unified system space, where design potential surfaces are derived from first-order reliability methods and an adaptive strategy is developed for robust and more efficient probabilistic constraint evaluation. More important, the design potential concept provides an in-depth understanding of reliability-based design optimization and leads to a design potential method that can significantly accelerate the process of robust system parameter design optimization.


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