Abstract

Fixture synthesis addresses the problem of fixture-elements placement on the workpiece surfaces. This article presents a novel variant of the simulated annealing (SA) algorithm called declining neighborhood simulated annealing (DNSA) specifically developed for the problem of fixture synthesis. The objective is to minimize measurement errors in the machined features induced by the misalignment at locators-workpiece contact points. The algorithm systematically evaluates different fixture layouts to reach a sufficient approximation of the most robust layout. For each iteration, a set of previously accepted candidates are exploited to direct the search toward the optimal region. Throughout the progress of the algorithm, the search space is reduced and the new candidates are designated according to a declining probability density function (PDF). To assure best performance, the DNSA parameters are configured using the Technique for Order Preference by Similarity to Ideal Solution (TOPOSIS). Moreover, the parameters are set to auto-adapt the complexity of a given input based on a Shanon entropy index. The optimization process is carried out automatically in the computer-aided design (CAD) environment NX; a computer code was developed for this purpose using the application programming interface (API) NXOpen. Benchmark examples from industrial partner and literature demonstrate satisfactory results.

Highlights

  • Fixture refers to the device used to immobilize and localize the workpiece during machining

  • This paper proposes a new approach for robust fixture synthesis in a point-set domain implemented in the Computer-Aided Design (CAD) environment [39]

  • Undesirable infinitesimal movement pivoting on the locating points can distort the relative distancing of these features

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Summary

Introduction

Fixture refers to the device used to immobilize and localize the workpiece during machining. Positional errors are measurement errors in the machined features (features of strict tolerances requirements are of main concern) To minimize these errors, Locators should be robustly positioned on the workpiece surfaces. The exterior surfaces of the workpiece is assumed to be a vast collection of discrete points These points represent all available positions for locator placement. Fixture synthesizing problems have been conquered in the literature using traditional methods such as linear and nonlinear programming [40, 5, 43]. These methods do not guarantee a global or near-global solution. Nontraditional methods such as evolutionary algorithms are ca-

Related works
Fixture model
Discretization
Optimization algorithm
The proposed algorithm
Cooling schedule
Generation mechanism
Setting up the algorithm parameters
Initial configuration of parameters
Adaptation to input complexity
Part one
Part two
Exploration and exploitation
Memory usage search
Characteristic graphs
Deterministic locating
Accuracy of the obtained result
Conclusion and future work
Code availability
Ethics approval

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