Automated Decomposition of Complex Parts for Manufacturing With Advanced Joining Processes

Mirror error compensation is usually employed to improve the machining precision of thin-walled parts. However, this zero-order method may result in inadequate error compensation, due to the time-varying cutting condition of thin-walled parts. To cope with this problem, an on-line first-order error compensation method is proposed for thin-walled parts. With this context, firstly, the time-varying cutting condition of thin-walled parts is defined with its in-process geometric and physical characteristics. Based on it, a first-order machining error compensation model is constructed. Then, during the process planning, the theory geometric and physical characteristic of thin-walled parts are respectively obtained with CAM software and structure dynamic modification method. After process performing, the real geometric characteristic of thin-walled parts is measured, and it is used to calculate the dimension error of thin-walled parts. Next, the error compensated value is evaluated based on the compensation model, from which, an error compensation plane is constructed to modify the tool center points for next process step. Finally, the machining error is compensated by performing the next process step. A milling test of thin-walled part is employed to verify the proposed method, and the experiment results shown that the proposed method can significantly improve the error compensation effect for low-stiffness structure, and thickness precision of thin-walled parts is improved by 71.4 % compared with the mirror error compensation method after machining.

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Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering

Chiu

Anderson²,

Fuge

2021

Volume 3A: 47th Design Automation Conference (DAC)

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Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions — e.g., optimizing aerodynamic lift. In contrast, this paper unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., pressure or electromagnetic fields) without directly specifying a set of objectives to optimize. This paper explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior. We test our contributions via the task of discovering designs that function as fluidic logic gates. We generate synthetic data that mimics fluidic devices and show that our proposed contributions better discover logic gates, as measured by adjusted Rand score. Specifically, combining our Resolution Selection preprocessing and principal component analysis resulted in the highest and tightest spread of adjusted Rand scores on our tested datasets. This opens up new avenues of research wherein computers can automatically explore different types of physics and then derive new device functions, behaviors, and structures without the need for human labels or guidance.

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Automated Decomposition of Complex Parts for Manufacturing With Advanced Joining Processes

Cited by 3 publications

References 20 publications

Optimal process planning for hybrid additive–subtractive manufacturing using recursive volume decomposition with decision criteria

Optimal process planning for hybrid additive–subtractive manufacturing using recursive volume decomposition with decision criteria

Online First-Order Machining Error Compensation for Thin-Walled Parts Considering Time-Varying Cutting Condition

Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering

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