Designing Volumetric Truss Structures

We present the first algorithm for designing volumetric Michell Trusses. Our method uses a parametrization approach to generate trusses made of structural elements aligned with the primary direction of an object's stress field. Such trusses exhibit high strength-to-weight ratios. We demonstrate the structural robustness of our designs via a posteriori physical simulation. We believe our algorithm serves as an important complement to existing structural optimization tools and as a novel standalone design tool itself

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

International audienc

An empirical investigation into branch coverage for C programs using CUTE and AUSTIN

Automated test data generation has remained a topic of considerable interest for several decades because it lies at the heart of attempts to automate the process of Software Testing. This paper reports the results of an empirical study using the dynamic symbolic-execution tool. CUTE, and a search based tool, AUSTIN on five non-trivial open source applications. The aim is to provide practitioners with an assessment of what can be achieved by existing techniques with little or no specialist knowledge and to provide researchers with baseline data against which to measure subsequent work. To achieve this, each tool is applied 'as is', with neither additional tuning nor supporting harnesses and with no adjustments applied to the subject programs under test. The mere fact that these tools can be applied 'out of the box' in this manner reflects the growing maturity of Automated test data generation. However, as might be expected, the study reveals opportunities for improvement and suggests ways to hybridize these two approaches that have hitherto been developed entirely independently. (C) 2010 Elsevier Inc. All rights reserved

Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks

In many cases, the efficient operation of Additive Manufacturing (AM) technology relies on build volumes being packed effectively. Packing algorithms have been developed in response to this requirement. The configuration of AM build volumes is particularly challenging due to the multitude of irregular geometries encountered and the potential benefits of nesting parts. Currently proposed approaches to address this packing problem are routinely evaluated on data sets featuring shapes that are not representative of targeted manufacturing products. This study provides a useful classification of AM build volume packing problems and an overview of existing benchmarks for the analysis of such problems. Additionally, this paper discusses characteristics of future, more realistic, benchmarks with the intention of promoting research toward effective and efficient AM build volume packing being integrated into AM production planning methodologies

Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks

In many cases, the efficient operation of Additive Manufacturing (AM) technology relies on build volumes being packed effectively. Packing algorithms have been developed in response to this requirement. The configuration of AM build volumes is particularly challenging due to the multitude of irregular geometries encountered and the potential benefits of nesting parts. Currently proposed approaches to address this packing problem are routinely evaluated on data sets featuring shapes that are not representative of targeted manufacturing products. This study provides a useful classification of AM build volume packing problems and an overview of existing benchmarks for the analysis of such problems. Additionally, this paper discusses characteristics of future, more realistic, benchmarks with the intention of promoting research toward effective and efficient AM build volume packing being integrated into AM production planning methodologies

A MILP model for an extended version of the Flexible Job Shop Problem

A MILP model for an extended version of the Flexible Job Shop Scheduling problem is proposed. The extension allows the precedences between operations of a job to be given by an arbitrary directed acyclic graph rather than a linear order. The goal is the minimization of the makespan. Theoretical and practical advantages of the proposed model are discussed. Numerical experiments show the performance of a commercial exact solver when applied to the proposed model. The new model is also compared with a simple extension of the model described by \"Ozg\"uven, \"Ozbakir, and Yavuz (Mathematical models for job-shop scheduling problems with routing and process plan flexibility, Applied Mathematical Modelling, 34:1539--1548, 2010), using instances from the literature and instances inspired by real data from the printing industry.Comment: 15 pages, 2 figures, 4 tables. Optimization Letters, 201