Computational Metrology for the Design and Manufacture of Product Geometry: A Classification and Synthesis

2006 ◽  
Vol 7 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Vijay Srinivasan

The increasing use of advanced measurement tools and technology in industry over the past 30 years has ushered in a new set of challenging computational problems. These problems can be broadly classified as fitting and filtering of discrete geometric data collected by measurements made on manufactured products. Collectively, they define the field of computational metrology for the design specification, production, and verification of product geometry. The fitting problems can be posed and solved as optimization problems; they involve both continuous and combinatorial optimization problems. The filtering problems can be unified under convolution problems, which include convolutions of functions as well as convolutions of sets. This paper presents the status of research and standardization efforts in computational metrology, with an emphasis on its classification and synthesis.

2021 ◽  
Vol 11 (14) ◽  
pp. 6449
Author(s):  
Fernando Peres ◽  
Mauro Castelli

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.


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