A level-based optimization algorithm for complex part localization
A level-based optimization algorithm for complex part localization
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DOI：
10.1016/j.precisioneng.2004.07.002
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发表时间：
2005-04
影响因子：
3.6
通讯作者：
J. Chatelain
中科院分区：
文献类型：
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作者：
J. Chatelain
本文提出了一种工件平衡问题的算法。该方法允许在机床中实现零件的最佳定位，以确保加工过程中有足够的库存余量。对于复杂的应用，如果在去除过程中发现某些材料缺失，平衡技术会优先定位材料缺失的方向，以最大程度地减少零件的返工。该技术是指毛坯的最佳点对齐，通过点数据集相对于标称零件的相应 CAD 表示来表示。约束对齐基于目标函数的优化，该目标函数是专门开发的，用于强制每个点按照规定的优先顺序位于实体模型之外。这种优先优化的目的是将任何短缺的材料定向到可以返工的区域。提出了两个目标函数（包括惩罚项）并在结果和效率方面进行了比较：最小二乘和对数公式。本文的主要范围是开发这些功能，以严格惩罚与特定零件区域相关的任何未满足的约束，从而防止加工过程中出现任何材料短缺。介绍了对数惩罚函数的完整开发，并通过应用示例介绍和比较了这两个函数的行为。结果表明，采用对数公式的零件对准比采用最小二乘法收敛得快得多，并且更适合材料平衡问题。研究发现，所开发的优先优化方法可以很好地解决通过稀疏点数据集的空白零件的平衡问题。这种创新方法对于任何其他需要根据特定优先级顺序满足一组约束的应用程序来说都是有希望的。
An algorithm for the workpiece-balancing problem is proposed in this paper. The approach allows optimal part positioning in machine tools in order to ensure sufficient stock allowance during the machining process. For complex applications, where some material is found to be missing during the removal process, the balancing technique preferentially orients the lack of material in order to minimize the rework for the part. The technique refers to a best point alignment of the blank, which is represented through a dataset of points, with respect to a corresponding CAD representation of the nominal part. The constrained alignment is based on the optimization of an objective function that is specially developed to force each point to lie outside the solid model under a prescribed order of priority. This preferential optimization aims to orient any shortage of material to areas where the rework is made possible. Two objective functions, including a penalty term, are proposed and compared in terms of results and efficiency: a least-squares and a logarithmic formulation. The main scope of this paper is the development of those functions in such a way as to severely penalize any unsatisfied constraint related to specific part areas, which shall be protected from any shortage of material during machining. The complete development is presented for the logarithmic penalty function, and the behavior of both functions is presented and compared through application examples. The results show that the part alignment with the logarithmic formulation converges much faster than with the least-squares and is more appropriate to the material-balancing problem. The preferential optimization approach developed is found to work well for the balancing problem of blank parts through sparse datasets of points. This innovative approach is promising for any other application requiring a set of constraints to be satisfied based on a specific order of priority.