Metrology in Industry 4.0. Metromeet

Transcription

1 Metrology in Industry 4.0 Metromeet Toni Ventura-Traveset DATAPIXEL Innovalia Metrology Pag. 1

2 Industry 4.0

3 Cyberphysical systems Interoperable Virtualization Decentralized Self-configuration Self-optimization Self-diagnosis. Intelligent Manufacturing Real Time Physical Digital

4 Industry 4.0 Metrology 1.0 Metrology 2.0 Metrology 3.0 Metrology 4.0?

5 Not so new.

6 Holon Arthur Koestler ( ) Although it is easy to identify sub-wholes or parts, wholes and parts in an absolute sense do not exist anywhere Agent Autonomous Self-regulating Cooperating Information flow Holo (whole) + on (parts) 1967

7 The holonic factory (Christensen 1994) Brain Body

8 1966- Theory of self-reproducing automata (edited by Arthur Burks) Von Neumman ( )

9 Von Neumman Theory of self-reproducing automata Agent (cognitive automata) Autonomous: decide for themselves Active: active control Flexible: Reactive, pro-active, social Intelligence: goal oriented, learning, adaptative, cognitive

10 Multi-agent System (MAS) Self-organisation Decentralization Local views Multiple cognitive entities acting in communities

11 Mapping physical to virtual Multiple cognitive entities acting in communities

12 Industry 4.0

13 Revolution!!!!

14 Revolution a sudden, extreme, or complete change in the way people live, work, etc.

15 Industry 4.0

16 Industry 4.0 WHY?

17 Energy

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19 Energy Productivity

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22 Less people Robotics China: 25% : 1.3 M robots

23 FOXBot iphone manufacturer, Foxconn, which employs 1.2 million workers, has announced that will deploy 1 million robots at their production lines in five years (70% automation) At Foxconn, the robots will only replace 30% of workers, all of them will be promoted to higher tasks Foxconn produces robots/year

24 Energy Productivity Quality

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26 owner-reported problems in the first 90 days of new-vehicle ownership the industry experiences a 3% year-over-year improvement in initial quality

27 Energy Productivity Quality?

28 Energy Productivity Quality Efficiency

29 Input Output Efficiency Energy Products Labour Services Material Customer satisfaction

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31 Energy

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33 New (old) materials

34 Energy Productivity Quality Efficiency Sustainability

35 400 ppm!!

36 Source : European Commission Competitiveness report 2013

37 Source : European Commission Competitiveness report 2013

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40 Energy Productivity Quality Efficiency Sustainability?

41 General Systems Theory

42 Systems Theory Ludwig von Bertalanffy (1901, 1972) Organismic Theory Kurt Goldstein (1878, 1965)

43 Systems Theory Holism: system, macro, micro Open systems and isomorphism Meta-theory Law of exponential growth Xt = X0(1+r) t

44 Law of exponential growth and demography

45 Law of exponential growth and GDP

46 Law of exponential growth Moore s law Energy consumption Productivity Internet of things

47 Law of exponential growth limits

48 Law of exponential growth of knowledge Arthur Conan Doyle ( ) The Great Kleinplatz Experiment Knowledge begets knowledge as money bears interest

49 Law of exponential growth of knowledge Knowledge is doubling every 8 years

50 Law of exponential growth and publications No evidence of exponential increase of traditional scientific publications (peer-reviewed journals) Publications using other channels is growing fast (internet, conferences ) (but not measured ) Traditional scientific dissemination ( and academia knowledge generation) is not able to address new knowledge creation needs

51 Next Generation Sequencing (base pairs)

52 Typical video game makes 60 to 70 percent of its money in the first four or five days LED TVs have 18 to 24 month to maximize revenues before being replaced by next generation

53 Law of exponential growth of manufacturing knowledge Traditional manufacturing model is not able to address new knowledge generation needs Cyberphysical systems Machines that can store and generate knowledge Continuous process transformation

54 Organismic theory Humans instinctively dissect the situation in an attempt to understand it better. Yet, in doing so, humans miss the essence or intrinsic nature of the organism itself. Interpretations should be taken in as a whole without giving special preference towards one part of the phenomena. When describing the phenomena, attention should not be diverted to one aspect of the phenomena that may be of interest. The holistic approach calls instead for the phenomena to be described from all angles without bias towards one part.

55 Manufacturing as organismic entities From narrow manufacturing knowledge mgmt to wide holistic knowledge mgmt Open innovation The customer. The Factory.. The individual process and product

56 Growing Mastering the environment Maintaining coherence

57 Richard Ryan / Edward Deci Self-determination Organismic meta-theory

58 (Meta-theory: growing, mastering environment, coherence) Learning Autonomy Richard Ryan / Edward Deci Self-determination Organismic meta-theory Relational

59 Relational Colaborating to learn Learning to colaborate Learning Techno-social network Self-monitoring Self-configuration Self-adaptation Autonomy

60 Relational Colaborating to learn Learning to colaborate Learning Techno-social network Self-monitoring Self-configuration Self-adaptation Autonomy

61 Learning systems

62 Jean Piaget ( ) Theory of cognitive development (Genetic epistemology) Sensorimotor stage Preoperational stage (symbolic, intuitive thought) Operational stage (logical thinking, classes) Formal operational stage (metacognition)

63 Jean Piaget ( ) Theory of cognitive development Sensors and maps (schemata) Symbols (ontologies) Patterns and simple algorithms Analytics and complex algorithms

64 Is not only about productivity and quality Is not only about efficiency Is not only about sustainability Is about holistic evolution of intelligence of complex systems Is about human growth, creativity, energy, passion. Art&Science

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67 Dicebamus hesterna die.

68 Agents in Manufacturing?

69 Agents in Manufacturing?

70 Agents in Manufacturing?

71 Agents in Manufacturing?

72 Human Machine Product

73 Human Machine Product Society Industry Organisation Manufacturing Unit Individual

74 Human Machine Product Society Industry Intelligence Organisation Manufacturing Unit Individual Relational Learning Autonomous

75 Dicebamus hesterna die.

76 Intelligence Robert Sternberg, Department of Human Development at Cornell University

77 Intelligence the ability to adapt to the environment and to learn from experience (Sternberg & Detterman, 1986).

78 Intelligence 1) the ability to achieve business goals, given a technological, sociocultural, economical context 2) by capitalizing on strengths and correcting or compensating for weaknesses; 3) in order to adapt, to shape, and select environments; and, 4) through a combination of practical, analytical and creative abilities (Adapted from Sternberg, 1997, 1998, 1999)

79 Intelligence 1) the ability to achieve business goals, given a technological, sociocultural, economical Goal context oriented 2) by capitalizing on strengths and correcting or compensating for weaknesses; Learning 3) in order to adapt, to shape, and select environments; and, Adaptative 4) through a combination of practical, analytical and creative abilities Cognitive abilities (Adapted from Sternberg, 1997, 1998, 1999)

80 Intelligent agent System 2: System 1: agent System 3,4: System 5

81 Intelligent agent System 2: simulation System 1: sensors agent System 3,4: cognition System 5 Techno-Social

82 SYSTEM 1 Sensors Body map Body schema Body image

83 SYSTEM 1: Smart Sensors

84 SYSTEM 1 Sensors Map Schema Edge Distributed sensing-measurement Semantic representation Perimetral / extended data Filtered information

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86 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Big Data Edge Computing Externalist Schema Internalist map Sensimotor: physical actuators & sensors

87 SYSTEM 2: Simulation

88 Stimulus Action

89 Simulation Stimulus Simulation of action Action Stimulus Simulation of action Blocked Action Visualization Simulation of action Blocked Action

90 SYSTEM 2: Simulation Simulation of System 1: sensing + map Simulation of the environment Simulation of actions Digital Factory

91 SYSTEM 2: Simulation Embedded Simulation in Machines Simulation systems embedded in the real machines Real mode Simulation mode

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93 Repetition Diversity Wide / Open mind

94 Stable production

95 Learning by doing Learning by visualizing

96 Learning by simulating Creating diverse simulated conditions to estimulate factory learning

97 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Big Data Edge Computing Externalist Schema Internalist map Sensimotor: physical actuators & sensors

98 SYSTEM 3 SYSTEM 4 Cognition

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100 x 4

101 x 4 8 x 1

102 How much does the ball cost?

103 17 x 14

104 17 x x 23

105 x 14 Fast system Slow system

106 x 14 Fast system Fast, automatic, frequent, emotional, stereotypic, subconscious Slow system Slow, effortful, infrequent, logical, calculating, conscious 99% 1% Daniel Kahneman: Nobel prize 2002

107 x 14 Fast system Based on patterns Intuitive intelligence Learning Slow system Based on analysis Analytical intelligence 99% 1%

108 Situation Pattern? Algorithm repository Pattern based intelligence Analytical Intelligence New Algorithm

109 SYSTEM 3: Fast thinking: Pattern based Intelligence SYSTEM 4: Slow thinking: Analytical Intelligence

110 SYSTEM 3: Fast thinking: Pattern based Intelligence Pattern based algorithms Automation PLC systems Logic systems / Fuzzy logic Smart Factory Neural networks Control systems

111 SYSTEM 4: Slow thinking: Analytical intelligence Data Mining systems Statistical analysis systems Agent-based planning systems Deep-learning Learning Factory

112 How to grow the algorithm repository?

113 Diversity of Situations Algorithm? Algorithm repository System 3: Pattern based intelligence System 4: Analytical Intelligence New Algorithm

114 7 month Novelty seeking The relationship of novelty preferences during infancy to later intelligence and later recognition memory, Joseph F. Fagan (1984)

115 7 month 5 years Novelty seeking ++ Intelligence The relationship of novelty preferences during infancy to later intelligence and later recognition memory, Joseph F. Fagan (1984)

116 Laboratories for creative exploratory learning Creating diverse conditions to estimulate factory learning

117 Learning sources Reality Simulation Exploration Labs Cognitive system Pattern based intelligence Analytics

118 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema Internalist map Sensimotor: physical actuators & sensors

119 Exploratory mode of intelligence Creative intelligence System 1 Sensing + mapping System 3 Intuition System 2 Simulation System 4 Analytical

120 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema Internalist map Sensimotor: physical actuators & sensors

121 System 5 Social cognition: knowing about relationships To know and To be known Inter-agent ties Cooperative agents Techno-social intelligence

122 To know and to be known: internet of things To know To be known

123 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors

124 Standards QIF Quality information framework

125 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors

126 Metrology 4.0 THE CHALLENGES

127 Human Machine Product Society Industry Organisation Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors 1

128 1st challenge for metrology 4.0 To allow future growth of manufacturing, metrology has to exponentially grow in terms of information generation and knowledge sharing

129 High density metrology Physical Digital

130 Metrology knowledge sharing

131 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors 1

132 2nd challenge for metrology Metrology has to expand their narrow view inside the laboratory towards a wide holistic metrology

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134 Metrology everywhere

135 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors 1 3

136 3rd challenge for metrology Metrology has to develop more powerful simulation tools to facilitate integration in the digital factory

137 Metrology simulation Simulation of sensors Simulation of interaction with the parts Simulation of sources of error (noise) Simulation of influence of humans..

138 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual Techno-social intelligence Big Data 4 Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors 1 3

139 4th challenge for metrology Metrology has to provide analytical tools for algorithm generation and deep-learning

140 The algorithm marketplace

141 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence 4 5 Pattern based intelligence Edge Computing Externalist Schema IoT Internalist map Sensimotor: physical actuators & sensors 1 3

142 5th challenge for metrology Metrology has to provide tools for creative exploratory learning labs (disruptive metrology)

143 Disruptive metrology

144 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual Techno-social intelligence Big Data Analytic intelligence 4 5 Pattern based intelligence Edge Computing Externalist Schema IoT 6 Internalist map Sensimotor: physical actuators & sensors 1 3

145 6th challenge for metrology Metrology has to develop standards and practical examples regarding IoT standards

146 Metrology and standards

147 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual 7 Big Data Techno-social intelligence Analytic intelligence 4 5 Pattern based intelligence Edge Computing Externalist Schema IoT 6 Internalist map Sensimotor: physical actuators & sensors 1 3

148 7th challenge for metrology Metrology has to provide/adapt solutions for big data storage and analytics

149 Data replication Auto-associative patterns Hierarchical levels Invariant patterns Federated DataBases Ontological networks BIG DATA need to be organised and contextualized for fast retrieval and analysis

150 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual 7 Big Data Techno-social intelligence Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema IoT 6 Internalist map Sensimotor: physical actuators & sensors 1 3

151 8th challenge for metrology Metrology has to develop new methods and tools for advanced human-machine interaction in order to deal with complexity

152 Metrology for humans

153 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual 7 Big Data Techno-social intelligence Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema 9 IoT 6 Internalist map Sensimotor: physical actuators & sensors 1 3

154 9th challenge for metrology Metrology has to facilite more multi-disciplinary knowledge generation, sharing and colaboration (multi-disciplinary innovation)

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156 Multi-disciplinary metrology

157 Human Machine Product Society Industry Organisation 2 Manufacturing Unit Individual 7 Big Data Techno-social intelligence Analytic intelligence Pattern based intelligence Edge Computing Externalist Schema 9 10 IoT 6 Internalist map Sensimotor: physical actuators & sensors 1 3

158 10th challenge for metrology Metrology has to facilite more multi-disciplinary education, gain visibility and increase attractiveness to young talents

159 Metrology as creative space of value creation and business opportunities

160 Metrology challenges for Industry Metrology growth, high density metrology 2- Metrology everywhere 3- Simulation 4- Analytical tools 5- Disruptive metrology, innovation labs 6- Standards 7- Big data storage 8- Human machine interaction 9- Multi-disciplinary innovation 10- Multi-disciplinary education

161 Industry 4.0 is driven by the global evolution of intelligence and complex systems Exponential growth of knowledge generation Reference model of intelligent manufacturing The challenges of metrology 4.0

162 Thank You!!