Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints

Afzal, Zahra Rahimi; Prabhakar, Pavana; Prabhakar, Pavithra

doi:10.1109/icc47138.2019.9123182

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…The liquid crystal spinning of silks was modelled by using nematodynamics, nematostatics, and interfacial thermodynamics, and the resulting semi-quantitative prediction was consistent with the birefringence observed in the native spinning gland [ 55 ]. 3D printing is one of the more recent developments in LCE manufacturing, and it has a variety of advantages, including the ability to construct a wide range of geometries, as well as precise spatial deposition and temporal control [ 56 ]. In many ways, this technique shares similarities with silk-spinning.…”

Section: Biomedical Advances Inspired By Lcesmentioning

confidence: 99%

Liquid Crystal Elastomers for Biological Applications

et al. 2021

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The term liquid crystal elastomer (LCE) describes a class of materials that combine the elastic entropy behaviour associated with conventional elastomers with the stimuli responsive properties of anisotropic liquid crystals. LCEs consequently exhibit attributes of both elastomers and liquid crystals, but additionally have unique properties not found in either. Recent developments in LCE synthesis, as well as the understanding of the behaviour of liquid crystal elastomers—namely their mechanical, optical and responsive properties—is of significant relevance to biology and biomedicine. LCEs are abundant in nature, highlighting the potential use of LCEs in biomimetics. Their exceptional tensile properties and biocompatibility have led to research exploring their applications in artificial tissue, biological sensors and cell scaffolds by exploiting their actuation and shock absorption properties. There has also been significant recent interest in using LCEs as a model for morphogenesis. This review provides an overview of some aspects of LCEs which are of relevance in different branches of biology and biomedicine, as well as discussing how recent LCE advances could impact future applications.

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Section: Biomedical Advances Inspired By Lcesmentioning

confidence: 99%

Liquid Crystal Elastomers for Biological Applications

et al. 2021

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“…Another problem studied in automated planning is the complete Coverage Path-Planning (CPP) problem, where the objective is to find an optimal or quasi-optimal path that covers every area in the region (we call such a path a complete coverage path of the region). This problem has many practical applications, such as: a) robotic vacuum-cleaning ( Viet et al., 2013 ; Yakoubi and Laskri, 2016 ; Edwards and Sörme, 2018 ; Liu et al., 2018 ); b) underwater autonomous vehicles (AUVs) ( Zhu et al., 2019 ; Han et al., 2020 ; Yordanova and Gips, 2020 ); c) 3d printing using fused deposition modeling ( Lechowicz et al., 2016 ; Afzal et al., 2019 ; Gupta, 2021 ); d) window washer robots ( Farsi et al., 1994 ; Dr.; John Dhanaseely and Srinivasan, 2021 ); e) disinfection of regions ( Conroy et al., 2021 ; Nasirian et al., 2021 ; Vazquez-Carmona et al., 2022 ); f) minesweeping ( Healey, 2001 ; Williams, 2010 ; Ðakulovic and Petrovic, 2012 ); g) agriculture and farming ( Oksanen and Visala, 2009 ; Jin, 2010 ; Santos et al., 2020 ); h) surveillance drones ( Ahmadzadeh et al., 2008 ; Modares et al., 2017 ; Vasquez-Gomez et al., 2018 ); i) search and rescue aerial drones ( Hayat et al., 2020 ; Ai et al., 2021 ; Cho et al., 2021 ). …”

Section: Introductionmentioning

confidence: 99%

“…c) 3d printing using fused deposition modeling ( Lechowicz et al., 2016 ; Afzal et al., 2019 ; Gupta, 2021 );…”

Section: Introductionmentioning

confidence: 99%

Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function

Gareau

Beaudry²

2023

Front. Robot. AI

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This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given region completely. First, we propose a CPP-solving baseline algorithm based on the iterative deepening depth-first search (ID-DFS) approach. Then, we introduce two branch-and-bound strategies (Loop detection and an Admissible heuristic function) to improve the results of our baseline algorithm. We evaluate the performance of our planner using six types of benchmark grids considered in this study: Coast-like, Random links, Random walk, Simple-shapes, Labyrinth and Wide-Labyrinth grids. We are first to consider these types of grids in the context of CPP. All of them find their practical applications in real-world CPP problems from a variety of fields. The obtained results suggest that the proposed branch-and-bound algorithm solves the problem optimally (i.e., the exact solution is found in each case) orders of magnitude faster than an exhaustive search CPP planner. To the best of our knowledge, no general CPP-solving exact algorithms, apart from an exhaustive search planner, have been proposed in the literature.

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“…Steuben et al use level sets of fields over the geometry to determine toolpaths for additive manufacturing but does not provide a framework for explicitly optimizing an objective function 22 . Afzal et al devise a mixed integer linear program for path planning, but impose linear constraints on the temperature field rather than attempting to optimize functions of it 23 …”

Section: Introductionmentioning

confidence: 99%

“…22 Afzal et al devise a mixed integer linear program for path planning, but impose linear constraints on the temperature field rather than attempting to optimize functions of it. 23 In the Numerical Experiments Section (Section 5), we will pose the problem of determining a high-quality scan strategy as a HTSP, transform it into an E-GTSP, and compute the solution in different settings using an E-GTSP solver as a demonstration of the transformation we introduce here.…”

Section: Introductionmentioning

confidence: 99%

Temperature field optimization for laser powder bed fusion as a traveling salesperson problem with history

Wang,

Darve,

Lew

2023

Numerical Meth Engineering

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Laser Powder Bed Fusion (LPBF) is a form of metal additive manufacturing in which a laser traces a path on a metal powder bed to heat powder particles and progressively melt and/or fuse them together to form a part. Outcomes of LPBF are functions of the metal's temperature history and are strongly affected by the path traced by the laser. Using temperature field optimization for LPBF as a motivating application, we focus on a class of combinatorial problems where admissible control policies are a permutation of a set of control actions. We abstract this class of problems as an optimal control problem with the objective of identifying an admissible control policy that minimizes a cost function of a dynamical system's state. While, in principle, the effect of the control actions on the cost function can last an infinitely long time, we will consider systems in which the effects of these control actions can be considered to last short (finite) periods of time. In this paper, we formalize this class of combinatorial problems as a Traveling Salesperson Problem with History and prove its equivalence to an Equality Generalized Traveling Salesperson Problem (E‐GTSP), enabling the use of well‐developed E‐GTSP solvers. We demonstrate this equivalence by computing the solutions obtained using an E‐GTSP solver for an LPBF‐inspired application.

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Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints

Cited by 5 publications

References 28 publications

Liquid Crystal Elastomers for Biological Applications

Liquid Crystal Elastomers for Biological Applications

Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function

Temperature field optimization for laser powder bed fusion as a traveling salesperson problem with history

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