1,720,961 research outputs found
Machining Phenomenon Twin Construction for Industry 4.0: A Case of Surface Roughness
Get PDFIndustry 4.0 requires phenomenon twins to functionalize the relevant systems (e.g., cyber-physical systems). A phenomenon twin means a computable virtual abstraction of a real phenomenon. In order to systematize the construction process of a phenomenon twin, this study proposes a system defined as the phenomenon twin construction system. It consists of three components, namely the input, processing, and output components. Among these components, the processing component is the most critical one that digitally models, simulates, and validates a given phenomenon extracting information from the input component. What kind of modeling, simulation, and validation approaches should be used while constructing the processing component for a given phenomenon is a research question. This study answers this question using the case of surface roughness—a complex phenomenon associated with all material removal processes. Accordingly, this study shows that for modeling the surface roughness of a machined surface, the approach called semantic modeling is more effective than the conventional approach called the Markov chain. It is also found that to validate whether or not a simulated surface roughness resembles the expected roughness, the outcomes of the possibility distribution-based computing and DNA-based computing are more effective than the outcomes of a conventional computing wherein the arithmetic mean height of surface roughness is calculated. Thus, apart from the conventional computing approaches, the leading edge computational intelligence-based approaches can digitize manufacturing processes more effectively
Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing
Get PDFSmart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems
Leveraging DNA-Based Computing to Improve the Performance of Artificial Neural Networks in Smart Manufacturing
No full textBioinspired computing methods, such as Artificial Neural Networks (ANNs), play a significant role in machine learning. This is particularly evident in smart manufacturing, where ANNs and their derivatives, like deep learning, are widely used for pattern recognition and adaptive control. However, ANNs sometimes fail to achieve the desired results, especially when working with small datasets. To address this limitation, this article presents the effectiveness of DNA-Based Computing (DBC) as a complementary approach. DBC is an innovative machine learning method rooted in the central dogma of molecular biology that deals with the genetic information of DNA/RNA to protein. In this article, two machine learning approaches are considered. In the first approach, an ANN was trained and tested using time series datasets driven by long and short windows, with features extracted from the time domain. Each long-window-driven dataset contained approximately 150 data points, while each short-window-driven dataset had approximately 10 data points. The results showed that the ANN performed well for long-window-driven datasets. However, its performance declined significantly in the case of short-window-driven datasets. In the last approach, a hybrid model was developed by integrating DBC with the ANN. In this case, the features were first extracted using DBC. The extracted features were used to train and test the ANN. This hybrid approach demonstrated robust performance for both long- and short-window-driven datasets. The ability of DBC to overcome the ANN’s limitations with short-window-driven datasets underscores its potential as a pragmatic machine learning solution for developing more effective smart manufacturing systems, such as digital twins
Manufacturing Process Optimization Using Open Data and Different Analysis Methods
No full textMaterial removal processes, or machining (encompassing milling, turning, and drilling), constitute an indispensable facet of manufacturing. To attain optimal machining performance—characterized by a high material removal rate, minimal tool wear, and superior surface finish—cutting conditions (such as the depth of the cut, feed rate, and cutting speed) must be meticulously optimized. Traditionally, this optimization has been contingent upon datasets collected from a singular, reliable source. However, in the paradigm of smart manufacturing, this data dependency is transitioning from a single source to a confluence of heterogeneous, open sources. Accordingly, this study elucidates a systematic approach for harnessing open-source machining datasets in a cogent and efficacious manner. Specifically, an open data source pertaining to turning operations, comprising 1013 records related to tool wear, is studied. From this corpus, 289 records corresponding to mild steel (JIS code: S45C) undergo rigorous analysis via Analysis of Variance (ANOVA), Signal-to-Noise Ratio (SNR), and possibility distributions. The empirical findings reveal that possibility distributions exhibit superior efficacy over ANOVA and SNR in extracting salient insights for optimization. Nevertheless, in certain scenarios, an integrative approach leveraging all three methods is requisite to attain optimal results. This study thus proffers a pragmatic computational framework, augmenting the optimization of machining within the purview of smart manufacturing
セマンティックアノテーションおよび時間遅れ型 センサー信号を規範とする機械加工現象のデジタ ルツイン作成システムの開発
Get PDF北見工業大学博士（工学）Manufacturing has rapidly been transforming under the umbrella of the
fourth industrial revolution, known as Industry 4.0 or smart manufacturing,
which diligently utilizes information and communication technology. In
smart manufacturing, cyber-physical systems host Internet-of-Things (IoT)-
based networks and digital twins. The networks integrate manufacturing enablers
such as computer numerical control machine tools, robots, numerous
process and resource planning systems, and human resources. Digital twins
are computable virtual abstractions of real-world entities exhibiting real-time
responsive capacities. The twins work as the brains of the enablers; that is,
the twins supply the required knowledge and help enablers solve problems
autonomously, responding to various sensor signals in real-time.
Remarkably, three types of digital twins (object, process, and phenomenon
twins) must populate the cyber-physical systems. Compared to other twins,
phenomena twins have not yet been researched elaborately. This thesis fills
this gap. The issues underlying semantic annotation and time latency (or
delay) are significant for a phenomenon twin. Time latency or delay occurs
when sensor signals are exchanged through the abovementioned embedded
systems. As a result, the signal at its origin (e.g., machine tools) and signal
received at the receiver end (e.g., digital twin) differ. Moreover, many
datasets of heterogeneous sensor signals are exchanged through IoT-based
networks. Hence, acquiring the right signals for a twin is difficult and timeconsuming.
Semantic annotation-based representation of sensor signals can
solve this problem. Thus, a phenomenon twin must machine-learn the required
knowledge to emulate the phenomenon from the relevant historical
sensor signal datasets, seamlessly interact with the real-time sensor signals,
handle the semantically annotated datasets stored in clouds, and accommodate
the transmission delay or latency.
Accordingly, this thesis presents two systems denoted as Digital Twin
Construction System (DTCS) and Digital Twin Adaptation System (DTAS).
The first system constructs a phenomenon twin, and the other adapts the
constructed twin into a cyber-physical system. Both systems are developed
using a JavaTM-based platform. The modular architectures of the systems
are presented in detail. In addition, real-life machining torque signals are
used to demonstrate the efficacy of DTCS and DTAS.
DTCS consists of five modules denoted as Input, Modeling, Simulation,
Validation, and Output Modules. The Input Module can make sense of the
semantically annotated datasets and helps users select the right ones. It ensures
fast and effective data mining using a human-machine-comprehensible
semantic annotation mechanism (concept map and Extensible Markup Language
(XML) driven). The Modeling Module can extract the required knowledge
to emulate a phenomenon from the information supplied by the Input
Module. This module uses a Markov chain-based machine learning method
and accommodates data transmission delay-related arrangements. The Simulation
Module can operate on the knowledge extracted by the Modeling
Module and simulate the signals of the phenomenon using a discrete eventbased
stochastic simulation method. The Validation Module can validate the
simulated signals of the phenomenon against the real signals using quantitative
measures (e.g., fuzzy numbers). Finally, the Output Module transfers
the selected Modules of DTCS to DTAS. DTAS, in turn, can adapt the constructed
phenomenon twin into the cyber-physical system for monitoring and
troubleshooting.
The thesis is organized as follows. Chapter 1 presents the introduction
of this study. Chapter 2 provides a literature review on the role of cyberphysical
systems and digital twins in smart manufacturing or Industry 4.0.
Chapter 3 describes a semantic annotation-based representation mechanism
of data and knowledge. Chapter 4 describes the role of the delay domain in
mitigating the effect of time delay or latency of signal transmission. Chapter
5 presents the proposed DTCS and DTAS. Chapter 6 demonstrates the
efficacy of DTCS and DTAS using a real-life case of intelligent monitoring
of machining (milling). Chapter 7 discusses the implications of this study
and highlights future research directions. Finally, Chapter 8 provides the
concluding remarks of this thesis.
Since the digital twins of the machining phenomena are needed to make
the machine tools and other programmable devices more intelligent and autonomous,
the presented DTCS and DTAS contribute to the befitting advancement
of Industry 4.0 or smart manufacturing.製造業は、インダストリー4.0またはスマートマニュファクチャリングと
して知られる第4次産業革命のもとで急速に変化している。この革命は
積極的な情報通信技術の利用によってサイバーフィジカルシステムを明
示し、IoTを規範とするネットワークおよびデジタルツインで構成され
る。IoTを規範とするネットワークは、コンピューター数値制御型工作
機械、ロボット、多数のプロセスおよびリソース計画システム、人材な
どの製造イネーブラーを統合する。デジタルツイン（リアルタイムの応
答能力を持つ実世界のエンティティの計算可能な仮想モデル化）は製造
イネーブラーの頭脳として機能している。つまり、デジタルツインは製
造イネーブラーが問題を自律的に解決するために必要な知識の獲得やさ
まざまなセンサー信号のリアルタイムによる応答を支援する。
注目すべき点はサイバーフィジカルシステムには3種類のデジタルツ
イン（物体、工程、および現象ツイン）を用意しなければならない点で
ある。しかし物体及び工程ツインの研究は進んでいるが、現象ツインに
おいてはまだ研究は不十分である。本論文はこのギャップを埋めること
を目的にしている。
現象ツインは、サイバースペースで与えられた現象（切削抵抗、ト
ルク、表面粗さなど）をエミュレートし、製造イネーブラー（工作機械
など）に必要な知識を与えることでそのイネーブラーをより効率的に活
躍させる。しかし現象ツインの作成に当たり、セマンティックアノテー
ション及び時間遅れという問題を考慮した対策を導入する必要がある。
時間遅れは、センサー信号が上記の組み込みシステムを介して交換
されるときに発生する。その結果、発信元の信号（工作機械側）と受信
側で獲得される信号（デジタルツイン側）が異なる。さらに、異種セン
サー信号の多くのデータセットは、IoTを規範とするネットワークを介
して交換される。従って、現象ツインには次の機能が求められる。
1. 与えられたセンサー信号データセットから必要な知識を機械学習に
よって獲得すること。
2. リアルタイムセンサー信号とシームレスに相互作用すること。
3. クラウドに保存されているセマンティックアノテーションデータ
セットを処理すること。
4. センサー信号の時間遅れに対応すること。
本論文ではデジタルツイン構築システム（DTCS）およびデジタルツ
イン適応システム（DTAS）について述べる。DTCSは現象ツインを構築
し、DTASは構築されたツインをサイバーフィジカルシステムに適応さ
せる。本システムはJavaTMプラットフォームによって開発する。各シス
テムの詳細について述べるとともに機械加工のとき発生するトルク信号
を用いて開発されたシステムの有効性を実証する。DTCSは、入力、モ
デリング、シミュレーション、バリデーション、および出力という5つ
のモジュールで構成される。入力モジュールは、セマンティックアノ
テーションされた信号データセットを理解し、ユーザーが選択した適
切なデータセットを獲得することができる。このモジュールは、セマ
ンティックアノテーションメカニズムによって用意されたデータ（概念
マップおよびExtensible Markup Language（XML）型データ）を高速か
つ効果的にマイニングすることができる。モデリングモジュールは、入
力モジュールによって提供されるデータセットから現象をエミュレート
するための知識を獲得することができる。その際、マルコフ連鎖型機械
学習の実施および時間遅れの影響に対応することができる。シミュレー
ションモジュールは、モデリングモジュールによって獲得された知識
に基づいて動作し、離散事象シミュレーションを用いて現象の信号をシ
ミュレートすることができる。バリデーションモジュールは、定量的手
法によって（例：ファジー数）現象のシミュレーションされた信号を実
際の信号に対して検証することができる。出力モジュールは、DTCSか
ら選択されたモジュールをDTASに転送することができる。最後に、モ
ニタリングやトラブルシューティングのため、DTASはDTCSから転送さ
れた現象ツインの各モジュールをサイバーフィジカルシステムに導入す
ることができる。
本論文は次のように構成する。第1章では、第4次産業革命および関
連研究分野の概要を説明する。第2章では、インダストリー4.0における
サイバーフィジカルシステムやデジタルツインの役割に関する文献を
レビューする。第3章では、セマンティックアノテーションに関するメ
カニズムを述べる。第4章では、時間遅れとそのセンサー信号の性質へ
の影響および時間遅れドメイン型信号処理の有効性について述べる。doctoral thesi
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Developing a System for Constructing Digital Twins of Machining Phenomena Based on Semantic Annotation and Time-Delayed Sensor Signals
No full textManufacturing has rapidly been transforming under the umbrella of the
fourth industrial revolution, known as Industry 4.0 or smart manufacturing,
which diligently utilizes information and communication technology. In
smart manufacturing, cyber-physical systems host Internet-of-Things (IoT)-
based networks and digital twins. The networks integrate manufacturing enablers
such as computer numerical control machine tools, robots, numerous
process and resource planning systems, and human resources. Digital twins
are computable virtual abstractions of real-world entities exhibiting real-time
responsive capacities. The twins work as the brains of the enablers; that is,
the twins supply the required knowledge and help enablers solve problems
autonomously, responding to various sensor signals in real-time.
Remarkably, three types of digital twins (object, process, and phenomenon
twins) must populate the cyber-physical systems. Compared to other twins,
phenomena twins have not yet been researched elaborately. This thesis fills
this gap. The issues underlying semantic annotation and time latency (or
delay) are significant for a phenomenon twin. Time latency or delay occurs
when sensor signals are exchanged through the abovementioned embedded
systems. As a result, the signal at its origin (e.g., machine tools) and signal
received at the receiver end (e.g., digital twin) differ. Moreover, many
datasets of heterogeneous sensor signals are exchanged through IoT-based
networks. Hence, acquiring the right signals for a twin is difficult and timeconsuming.
Semantic annotation-based representation of sensor signals can
solve this problem. Thus, a phenomenon twin must machine-learn the required
knowledge to emulate the phenomenon from the relevant historical
sensor signal datasets, seamlessly interact with the real-time sensor signals,
handle the semantically annotated datasets stored in clouds, and accommodate
the transmission delay or latency.
Accordingly, this thesis presents two systems denoted as Digital Twin
Construction System (DTCS) and Digital Twin Adaptation System (DTAS).
The first system constructs a phenomenon twin, and the other adapts the
constructed twin into a cyber-physical system. Both systems are developed
using a JavaTM-based platform. The modular architectures of the systems
are presented in detail. In addition, real-life machining torque signals are
used to demonstrate the efficacy of DTCS and DTAS.
DTCS consists of five modules denoted as Input, Modeling, Simulation,
Validation, and Output Modules. The Input Module can make sense of the
semantically annotated datasets and helps users select the right ones. It ensures
fast and effective data mining using a human-machine-comprehensible
semantic annotation mechanism (concept map and Extensible Markup Language
(XML) driven). The Modeling Module can extract the required knowledge
to emulate a phenomenon from the information supplied by the Input
Module. This module uses a Markov chain-based machine learning method
and accommodates data transmission delay-related arrangements. The Simulation
Module can operate on the knowledge extracted by the Modeling
Module and simulate the signals of the phenomenon using a discrete eventbased
stochastic simulation method. The Validation Module can validate the
simulated signals of the phenomenon against the real signals using quantitative
measures (e.g., fuzzy numbers). Finally, the Output Module transfers
the selected Modules of DTCS to DTAS. DTAS, in turn, can adapt the constructed
phenomenon twin into the cyber-physical system for monitoring and
troubleshooting.
The thesis is organized as follows. Chapter 1 presents the introduction
of this study. Chapter 2 provides a literature review on the role of cyberphysical
systems and digital twins in smart manufacturing or Industry 4.0.
Chapter 3 describes a semantic annotation-based representation mechanism
of data and knowledge. Chapter 4 describes the role of the delay domain in
mitigating the effect of time delay or latency of signal transmission. Chapter
5 presents the proposed DTCS and DTAS. Chapter 6 demonstrates the
efficacy of DTCS and DTAS using a real-life case of intelligent monitoring
of machining (milling). Chapter 7 discusses the implications of this study
and highlights future research directions. Finally, Chapter 8 provides the
concluding remarks of this thesis.
Since the digital twins of the machining phenomena are needed to make
the machine tools and other programmable devices more intelligent and autonomous,
the presented DTCS and DTAS contribute to the befitting advancement
of Industry 4.0 or smart manufacturing.製造業は、インダストリー4.0またはスマートマニュファクチャリングと
して知られる第4次産業革命のもとで急速に変化している。この革命は
積極的な情報通信技術の利用によってサイバーフィジカルシステムを明
示し、IoTを規範とするネットワークおよびデジタルツインで構成され
る。IoTを規範とするネットワークは、コンピューター数値制御型工作
機械、ロボット、多数のプロセスおよびリソース計画システム、人材な
どの製造イネーブラーを統合する。デジタルツイン（リアルタイムの応
答能力を持つ実世界のエンティティの計算可能な仮想モデル化）は製造
イネーブラーの頭脳として機能している。つまり、デジタルツインは製
造イネーブラーが問題を自律的に解決するために必要な知識の獲得やさ
まざまなセンサー信号のリアルタイムによる応答を支援する。
注目すべき点はサイバーフィジカルシステムには3種類のデジタルツ
イン（物体、工程、および現象ツイン）を用意しなければならない点で
ある。しかし物体及び工程ツインの研究は進んでいるが、現象ツインに
おいてはまだ研究は不十分である。本論文はこのギャップを埋めること
を目的にしている。
現象ツインは、サイバースペースで与えられた現象（切削抵抗、ト
ルク、表面粗さなど）をエミュレートし、製造イネーブラー（工作機械
など）に必要な知識を与えることでそのイネーブラーをより効率的に活
躍させる。しかし現象ツインの作成に当たり、セマンティックアノテー
ション及び時間遅れという問題を考慮した対策を導入する必要がある。
時間遅れは、センサー信号が上記の組み込みシステムを介して交換
されるときに発生する。その結果、発信元の信号（工作機械側）と受信
側で獲得される信号（デジタルツイン側）が異なる。さらに、異種セン
サー信号の多くのデータセットは、IoTを規範とするネットワークを介
して交換される。従って、現象ツインには次の機能が求められる。
1. 与えられたセンサー信号データセットから必要な知識を機械学習に
よって獲得すること。
2. リアルタイムセンサー信号とシームレスに相互作用すること。
3. クラウドに保存されているセマンティックアノテーションデータ
セットを処理すること。
4. センサー信号の時間遅れに対応すること。
本論文ではデジタルツイン構築システム（DTCS）およびデジタルツ
イン適応システム（DTAS）について述べる。DTCSは現象ツインを構築
し、DTASは構築されたツインをサイバーフィジカルシステムに適応さ
せる。本システムはJavaTMプラットフォームによって開発する。各シス
テムの詳細について述べるとともに機械加工のとき発生するトルク信号
を用いて開発されたシステムの有効性を実証する。DTCSは、入力、モ
デリング、シミュレーション、バリデーション、および出力という5つ
のモジュールで構成される。入力モジュールは、セマンティックアノ
テーションされた信号データセットを理解し、ユーザーが選択した適
切なデータセットを獲得することができる。このモジュールは、セマ
ンティックアノテーションメカニズムによって用意されたデータ（概念
マップおよびExtensible Markup Language（XML）型データ）を高速か
つ効果的にマイニングすることができる。モデリングモジュールは、入
力モジュールによって提供されるデータセットから現象をエミュレート
するための知識を獲得することができる。その際、マルコフ連鎖型機械
学習の実施および時間遅れの影響に対応することができる。シミュレー
ションモジュールは、モデリングモジュールによって獲得された知識
に基づいて動作し、離散事象シミュレーションを用いて現象の信号をシ
ミュレートすることができる。バリデーションモジュールは、定量的手
法によって（例：ファジー数）現象のシミュレーションされた信号を実
際の信号に対して検証することができる。出力モジュールは、DTCSか
ら選択されたモジュールをDTASに転送することができる。最後に、モ
ニタリングやトラブルシューティングのため、DTASはDTCSから転送さ
れた現象ツインの各モジュールをサイバーフィジカルシステムに導入す
ることができる。
本論文は次のように構成する。第1章では、第4次産業革命および関
連研究分野の概要を説明する。第2章では、インダストリー4.0における
サイバーフィジカルシステムやデジタルツインの役割に関する文献を
レビューする。第3章では、セマンティックアノテーションに関するメ
カニズムを述べる。第4章では、時間遅れとそのセンサー信号の性質へ
の影響および時間遅れドメイン型信号処理の有効性について述べる
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Towards Developing Big Data Analytics for Machining Decision-Making
No full textThis paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, (4) Data Analysis System, and (5) Knowledge Extraction System. The functional requirements of the integrated system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry Electrical Discharge Machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In summary, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out
- …
