最近の研究テーマ (Current Research Topics)
研究詳細 (Research Details)
Three types of digital twins (Object, Process, and Phenomenon twins) are needed for virtualizing a real-life manufacturing process and functionalizing high-level cognitive tasks (monitoring, understanding, predicting, decision-making, and adapting). In this study, we are developing phenomena twins using historic phenomena-relevant sensor signal datasets. For this, two Java-based systems: Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS), are developed for constructing and adapting the twins, respectfully. The DTCS acquires semantically annotated phenomena-relevant sensor signal datasets from cloud, machine learns the knowledge underlying the datasets, simulates the relevant phenomenon using a stochastic simulation process, and validates the simulation outcomes using possibility distributions (fuzzy numbers). The DTAS adapts the validated outcomes for real-time monitoring (one example shown in the video below).
Managing tool-wear is an important issue associated with all material removal processes. This study deals with the application of two nature-inspired computing techniques, namely, artificial neural network (ANN) and (in silico) DNA-based computing (DBC) for managing the tool-wear. Experimental data (images of worn-zone of cutting tool) has been used to train the ANN and, then, to perform the DBC. It is demonstrated that the ANN can predict the degree of tool-wear from a set of tool-wear images processed under a given procedure whereas the DBC can identify the degree of similarity/dissimilar among the processed images.
The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abrasive grains. This condition is ensured by performing dressing operations at regular time intervals. The effectiveness of a dressing operation is determined by measuring the surface topography of the wheel. In many cases, image processing methods are employed to determine the surface topography. However, such procedures must be able to remove the regions where the abrasive grains do not reside while keeping, at the same time, the regions where the abrasive grains reside. Thus, special kinds of image processing techniques are needed to distinguish the non-grain regions from the grain regions, which requires a heavy computing load and long duration. As an alternative, in the framework of the “Biologicalization in Manufacturing” paradigm, this study employs a bio-inspiration-based computing method known as DNA-based computing (DBC). The outcomes of this study can help develop an intelligent image processing system to optimize dressing operations and thereby, grinding operations.
Reverse engineering mimics critical features of an existing object to create its accurate or enhanced virtual/physical models. It is thus useful in creating the digital footprints of an object. It requires sophisticated devices, computing facilities, and high human skills. Thus, a reverse engineering process becomes sustainable if it is less dependent on sophisticated devices, computations, and human skills. From this point of view, this study presents a sustainable reverse engineering process consisting of five steps. The process is compared with the traditional scanned point cloud-based reverse engineering. The comparison shows that it is free from computational complexity, creates comparatively accurate models, and works very fast. Integrating the presented reverse engineering process with a product life cycle engineering can contribute to sustainable product development.
Digital manufacturing technology can be used to preserve craftsmanship (e.g., Ainu pattern-making craftsmanship). From this perspective, this study presents a methodology to create both virtual and physical prototypes of Ainu patterns using digital manufacturing technology. In particular, a point cloud-based approach was adopted to model the patterns. The virtual and physical prototypes of both basic (Hokkaido) Ainu motifs and some synthesized patterns were reproduced using the presented methodology. The point cloud creation algorithm is also effective for geometric modeling and 3D printing of 3D shapes. The findings of this study will help those who want to digitize the craftsmanship of culturally significant artifacts without using a 3D scanner or image processing.
Porous structures exhibit superior properties compared to their non-porous counterparts, though they are challenging to design and fabricate. This study presents a self-contained and user-friendly system that designs a porous structure by filling the spaces between a randomly generated point cloud and a closed boundary. It produces the STL dataset of the designed structure. The system generated STL datasets were used to fabricate some porous structures using a 3D printer. The fabricated structures exhibits randomly sized and distributed pores through which gasses and liquids could pass. These characteristics of the fabricated structures make them suitable for engineering and biomedical applications.
機械加工用データのオントロジーに関する研究(Study on ontology of machining data)
オンリーワン製品のための顧客-生産者-相互作用システムの開発 (Developing a customer-producer-interaction system for one-of-a-kind product)
木工製品の持続可能性とそのデジタル化に関する研究 (Study on the sustainability of wooden products and their digitalization)
解析点群による複雑な形状のリバースエンジニアリングに関する研究 (Study on reverse engineering of complex shapes by analytical point cloud)
反復関数系を用いて作成されたフラクタル図形の再設計に関する研究 (Study on redesigning fractal figures using iterative function systems (IFS))
多孔質構造のより実用的な設計及び製造に関する研究 (Study on more practical design and fabrication of porous structures)
加工オペレーションの切削条件を最適化するビッグデータ分析の開発 (Developing big data analytics to optimize cutting conditions of machining operations)
スマートマニュファクチャリングを規範とするコンテンツの作成とそのシステムの開発 (Developing a system for digitizing manufacturing process-relevant contents)
画像処理による3Dプリントされた多孔質構造の正確度の検証 (Verification of accuracy of 3D printed porous structures by image processing)
フラクタル図形を用いた複雑な物体の作製 (Fabrication of complex objects using fractals)
リバースエンジニアリングを用いたリマニュファクチャリング法 (Study on remanufacturing using reverse engineering)
デジタルツインを規範とするセンサー信号のモデリング法 (Study on modeling methods for sensor signal-based digital twins)
スマートマニュファクチャリングを規範とする生産加工に関する実験データの整理 (Organizing process-relevant experimental data for smart manufacturing)
Q-Sequenceを用いたセンサ信号のモデリング及びシミュレーション法 (Modeling and simulation of sensor signals using Q-Sequence)
植物の成長における多孔質構造の影響 (Effect of porous structure on plant growth)
植物栽培用の多孔質構造の作製 (Fabrication of porous structures for plant cultivation)
画像処理による3Dプリントされた物体の正確さの検証 (Accuracy verification of 3D printed objects using image processing)