Published papers

より優れた光コムや光センサの開発に向けて，高性能な微小光共振器が求められている．光学特性に優れた単結晶MgF2を超精密切削することで実現できる可能性があるが，硬脆材かつ異方性を有するため微小き裂のない表面品位を維持することが難しい．そこで，本研究では楕円振動切削を適用することで，臨界切り込み深さの向上を目指した．加工面や切りくず観察を通して，振動条件が臨界切り込み深さに与える影響を調べた．

回転エンコーダは、回転スケールの偏心や変形によって角度誤差が生じる。このとき、回転スケールの円筒面に角度信号の検出面を有する場合、偏心や変形によって検出面とセンサヘッドとのギャップ量が変化するため、センサヘッドが検出する角度信号の強度が変化する。本研究では、回転エンコーダを構成するセンサヘッドでこの強度変化を検出し、回転スケールの偏心や変形の量を逆算することで角度誤差を計測する方法を提案する。

複雑形状部品を一体造形ができる付加製造技術は，自動車産業においても注目を集めている．本研究は指向性エネルギー堆積法を用いた電気自動車のモータ製造法の確立を目指し，磁芯に利用される軟磁性材料FeSi，FeCoVを用いた造形物の磁性と結晶粒組織を評価した．安定造形が可能な造形パラメータを同定し，FeCoV造形物についてレーザ出力を増加させることで鉄損の原因となる保磁力を低めることができた．

金属積層造形の一方式である指向性エネルギー堆積法（DED）を用いた高速コーティングは，高効率な金属皮膜技術として期待されている．しかし，DEDではレーザによる入熱や自己冷却が繰り返され，それらの熱履歴が機械的性質に複雑に影響するため，所望の機械的性質を得るためには熱履歴に対する加工条件の影響を調べる必要がある．本研究では，伝熱解析により高速コーティングによる被膜層や母材の熱履歴を評価する．

超硬合金の金型を製造するための切削加工技術が求められている．超硬合金の切削では，粉末状の微細な切りくずが工具と工作物の界面に入り込み工具摩耗を促進する．そこで，本研究では，切りくず排出性を向上させるために，工具逃げ面に微細溝テクスチャを施した切削工具を提案し，これを用いて超硬合金の楕円振動切削試験を行った．テクスチャ形状と切りくず排出性の関係を実験的に調べ，提案工具の耐摩耗性を評価した．

指向性エネルギー堆積法（DED）は金属コーティング技術としての応用が期待されているが，内部欠陥などの技術課題が残る．本研究では，Inconel625と20MnCr5の粉末を使用し，DEDによる金属コーティングを行った．また，生成したコーティング層にレーザ再溶融処理を実施し，内部欠陥の削減を図り，造形物表面の硬さへの影響についても評価した．

Electro adhesive gel (EAG) shows a unique characteristic changing its surface adhesive property according to the applied electric field, which is named as electro-adhesive effect (EA effect). Our recent research results indicated that the EAG is feasible to be applied to fixture, clutch, damping, and brake mechanisms. While the performance of EAG has been dramatically enhanced over the past decade, the individual difference caused by the production process has been still problem. In other word, adhesive area of EAGs under the same electric filed is non-uniform and uncontrollable. In this research, to ensure stable EA effect, an electro-adhesive surface (EAS) applying 3-dimensional micro structure alternative to the EAG is proposed. The proposed EAS is composed of the 3-D micro-mesh structure fabricated by the photolithography and silicone gel. The prototype of the EAS was developed and its characteristic was experimentally investigated by microscopic observation and shear test. The result obviously shows that the EAS generates larger adhesive force in higher intensity of electric filed. Furthermore, a uniform and regular pattern of adhesion area is clearly observed. From these results, EAS has a strong possibility to improve the stability of EA effect and enhance the performance of its application devices.

Chatter vibration deteriorates the machining accuracy and shortens the tool life. Thus, In-process monitoring methods have been proposed by using additional sensors such as dynamometers and acceleration sensors. However, introducing additional sensors leads to high cost and reduction of machine-tool stiffness. To solve these problems, a sensor-less monitoring method applying the disturbance observer theory was proposed in our previous research which has experimentally shown that chatter vibration can be detected only from the servo information in milling. For further demand, because chatter vibration can be classified into self-excited vibration and forced vibration, an assorted detection method is required. In this study, we propose a novel frequency analysis method combining moving variance and moving Fourier transform algorithms, which can obtain the power spectrum density of each chatter vibration type separately with small computational load. The validity of the proposed method is evaluated through milling tests with different rotational speeds.

The dynamic behavior of a machine tool is affected by the forces applied to the structure. However, cutting processes have been analyzed considering the linear systems in most of the studies so far. In this study, a machine tool structure was excited by using an electromagnetic shaker and the force-dependant frequency response characteristics were measured. Based on the measured result, the nonlinear characteristics of the machine tool structure was identified, and a machine tool with the nonlinear characteristics was mathematically reproduced, which enabled to analyse the nonlinear frequency response characteristics and simulate cutting processes.

Various cutting-force estimation methods based on different disturbance observers have been proposed. However, each method has a modeling error, decreasing the estimation accuracy in a certain frequency band. In order to compensate for the modeling error and improve the performance, we have proposed that three different cutting estimation methods are integrated by using a neural network. In this study, the influence of friction compensation and input data on the cutting force estimation is analyzed. The results showed that estimation accuracy remarkably decreased when friction compensation was not implemented, and the accuracy decreased including the experimental conditions of interpolation and extrapolation experiment conditions when directly inputting servo informations to a neural network.

大口径の光学ガラスレンズの形状創成において，ガラスを割らずに加工する延性モード研削の適用とその効率向上が求められている．安定した延性モード研削を行うためには，研削加工中に延性／脆性モードを判別する技術が必要不可欠である．そこで，本研究では，工作物主軸に組み込んだAEセンサと各軸のサーボ情報を用いた加工状態監視手法を提案した．信号波形を解析し，加工状態に応じて各信号に生じる特徴を明らかにした．