3. Hi I’m Arief
A new Faculty
Member of
MTD Binus
Oct 1, 2020
4. Research Interests
• Additive Manufacturing in Industry 4.0
• Smart (Enabled by AI) Energy Systems (Solar PV, Energy Storage,
Electric Vehicle)
• Engineering Design/Design for Reliability (DfR)
• Product Design/Innovations
• Industrial System Design for Innovations in Industry 4.0
5. Arief S. Budiman
S2 TEKNIK INDUSTRI
1. Program riset dan kelompok bidang keahlian (KBK) di bidang Manufakturing
(sebagai sebuah track spesialisasi di Teknik Industri di Binus) yang berbasiskan IT
(Additive Manufacturing/3D Printing, Smart Manufacturing enabled by AI)
2. Riset dan KBK di bidang DfX – Design for X (Quality, Reliability and
Manufacturability) yang berbasiskan IT (Computer-Aided Design with
Visualization, Modeling, Simulation, Prototyping and Reliability
Prediction/Assessment using Machine Learning)
3. Edukasi/teaching di bidang-bidang di atas yang intensif dengan studi kasus
engineering dari real world experiences di industri teknologi (HP, AMD, Intel,
SunPower, REC Solar, PT Impack Pratama, etc.) di Silicon Valley (AS), Singapore dan
Indonesia
4. Pengabdian ke masyarakat secara lebih luas (seperti Agriculture 4.0), seperti juga
melalui riset dan KBK (Li-ion battery projects with the Mobil Listrik Nasional)
serta edukasi/pendidikan dan penyuluhan/consulting (buat industri, misalnya)
6. POSSIBLE RESEARCH TOPICS
1. Additive Manufacturing for Energy Storage for EV
in Industry 4.0
2. PV for the People (Start up PV)
3. Smart (Enabled by AI) Manufacturing for
Fracture-Free Thin Silicon Solar Cell PV
4. PV + Battery + AI = Agriculture 4.0 for Indonesia
7. Tunable Impact Properties Based on Novel Helicoidal
3D Architecture Enabled by Integrated Additive
Manufacturing Methodologies
R. Sahay, K. Agrawal, I. Radchenko, A. Baji, A.S.
Budiman*
Xtreme Materials Laboratory (XML)
Singapore University of Technology & Design (SUTD)
SINGAPORE
*suriadi@alumni.stanford.edu
INSTITUTE OF TECHNOLOGY, BANDUNG (ITB)
Mon, February 17, 2020
8. Bioinspired helicoidal structures found in animals and
additively manufactured version
[1] Yu Chen et al. Acta Biomaterialia 4(3):587-96. DOI:10.1016/j.actbio.2007.12.010
[2] Weaver et al. Vol. 336, Issue 6086, pp. 1275-1280. DOI: 10.1126/science.1218764
a) b)
Top overview (with
regular optical
imaging and SEM)
and Cross sectional
SEM view of
helicoidal PVDF fibers
1 cm
Agarwal, K., Sahay, R., Baji, A., &
Budiman, A. (n.d.). Biomimetic tough
helicoidally structured material
through novel electrospinning based
additive manufacturing. MRS
Advances, 1-10.
doi:10.1557/adv.2019.313
9. PV Energy: Indonesian Style
(The Business Side of the Story)
1. The Business Case
a) Why PV Indonesian Style?
b) Indonesian Innovations in the Technology World?
c) Global Markets for the Taking!
2. Market Research
3. A bit about the Technology
4. Process of Innovations
5. Conclusions
10. More Integration with Urban Structures
Source: http://solarwa.org/potm/project-month-april-2012
• Light weight
• Low material cost
• Low installation cost
• Power with esthetics
11. But it must be lightweight, suitable for Indonesian
conditions and, if possible, with some aesthetics please
SOLAROOF –
An idea was born
16. PV Module – Most Typical Structure
Aluminium Frame
Frontsheet (Glass – most commonly)
Front Encapsulant (EVA or Polyolefin)
Silicon Cells (Mono or Poly-crystalline)
Back Encapsulant (EVA or Polyolefin)
Backsheet (TPE – most commonly)
J-Box (Electrical Inverter)
TPE = Tedlar/PET/EVA
(Basically PET with outer layer being Teflon/Tedlar/PVDF so it protects from harsh
climate outside and inner layer being EVA so it becomes one with the Back EVA)
17. SOLAROOF
Integrated PV-Rooftop Solution
Mostly we will just change the Frontsheet and the Backsheet
Frame
Frontsheet (PC – transparent, strong)
Front Encapsulant (EVA or Polyolefin)
Silicon Cells (Mono or Poly-Crystalline)
Back Encapsulant (EVA or Polyolefin)
Backsheet (Alderon – opaque, stiff)
J-Box (Electrical Inverter)
21. The Business Case
Solaroof
• PC-integrated PV panels – lighter, cheaper, more suitable to Indonesia’s
tropical and economical climates
• Tropical climates: more humid, hotter, windier
• Near oceans: saltier, more corrosive
• Remote areas (daerah 3T): longer transportation, no PLN power, lack of infrastructure
• Developing regions: maybe 25 yr warranty is not needed, esp. LTSHE!
• Rooftop-integrated PV for factories, plants especially in remote areas
• Factories, plants all over the world have been changing their roofs from metals to PC rooftops
• Now, with just a little increase in weight and cost, they get more than their money’s worth
• Other applications are abound – for cheap, light, even foldable PV design!
• Military needs for remote area operation, surveillance, etc.
• Civil needs for disaster management, emergency operations, etc.
• More aesthetic PV for our bridges, bus stops, parking areas, etc.
• Global markets for the taking
• The sun belt regions: India, Brazil – some of the emerging economies around thew world!
23. SMART ENERGY 4.0 –
Artificial Intelligence (AI) Enabling Low-Cost,
Self-Sufficient & Sustainable Integrated Energy System for
Poverty Eradication in Indonesia
Arief S. Budiman*
1Engineering Products Design (EPD),
Singapore University of Technology & Design (SUTD),
SINGAPORE 487372
In Collaboration with Massachusetts Institute of Technology (MIT),
Cambridge, MA and Anhalt University, Germany
3Advanced Light Source (ALS),
Lawrence Berkeley National Laboratory (LBNL),
Berkeley, CA 94720
*suriadi@alumni.stanford.edu
IAPE 2019 – Sustainable Energy & Storage
Oxford, Mar 14, 2019
24. 24
The Solar Dome is with no
energy, so only for
daytime!
Similar
drying/storing
system is already
used in
Indonesia, esp.
remote areas
25. Our climate is a challenge for Coffee Agriculture
Coffee must be kept in an optimal storage
temp (14.5 – 20 deg C) and relative humidity
(60%), 24/7!
Low-cost storage with self-sufficient energy (generation + storage) will
enhance coffee agriculture in Indonesia and help eradicate poverty
26. Environ
mental
Sensors
SMART ENERGY 4.0
MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
(AI)-ENABLED
OPTIMIZATION SYSTEM
SMART ENERGY 4.0
AI-ENABLED ENERGY
INTEGRATED SYSTEMS (SOLAR
PV, ENERGY STORAGE,
HEATING/COOLING)
• Temperature,
humidity, pressure
• Solar irradiance
• Wind
ENVIRONMENTAL SUB-SYSTEM
ENERGY PRODUCTION/STORAGE
SUB-SYSTEM
Inverter
LOW-COST, SELF-
SUFFICIENT
ARTIFICIAL INTELLIGENCE
(AI)-ENABLED CONTROLLED
ENVIRONMENT
PV + Regular Battery + AI = Agriculture 4.0 for Indonesia
Regular Battery = Lead-Acid Battery, or NiCd/NiMH
27. ICMAT 2017: Solar PV Systems – Materials, Manufacturing & Reliabiilty
Invited Talk, Thurs, Jun 22, 2017
A.S. Budiman1,2*
1Singapore University of Technology & Design (SUTD),
Engineering Products Design (EPD) Pillar,
20 Dover Drive, SINGAPORE 138682
2Advanced Light Source (ALS),
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA 94720
*suriadi@alumni.stanford.edu
DfX (Design for X;
X = Reliability, Quality, Manufacturability) –
Smart (Enabled by AI) Manufacturing for Minimizing Defect
in Silicon Photovoltaics (PV)
27
28. Fracture in Silicon PV/Solar Cells
5
Typical Si crack direction for
monocrystalline Si solar cells –
Following (111) planes
Cracks do not follow
(111) planes
(a) (b)
(a) (b) (c) (f)
(e)
(d)
Silicon wafers are cracking in the field
due to various loadings – snow, wind,
thermal extremes/cycles, hail impacts,
people!
Cracks in PV modules after
mechanical loading (2400
kPa) to simulate wind load
Cracks were observed originating from
solder interconnect areas during
module processing and thermal cycling
M. Sander, S. Dietrich, M. Pander, M. Ebert, S. Thormann, J. Wendt, J. Bagdahn. Investigations on cracks in encapsulated solar cells after thermal and
mechanical loading. Proc. EUPVSEC (2012) 3188-93
29. Minimizing Defects in Silicon PV Manufacturing
• Fracture in Silicon (Brittle Materials)
- Primary driving force is always mechanical
stress (precursor)
- But fracture rate/occurrence is probabilistic
in nature
• Stress is deterministic Smart Stress Sensing
• Fracture is probabilistic Smart Fracture Sensing
and Prediction using AI (Artificial Intelligence)
• Smart Stress + Smart Fracture = Manufacturing 4.0
30. (a)
(b)
(c)
Figure: Typical schematic illustrations of the wafer curvature stress measurementsusing
existing laser technology; (a) block diagram representation of the experimental setup, and (b)
schematic of the fast waver curvature measurement using laser methodology. The typical
system measures wafer curvature by monitoring the deflection of parallel beams of laser (due
to surface tilt or misorientation) and mapping would be enabled by high precision, servo
motor controlled x-y stage.
Smart Stress Sensing
31. Figure: Schematic illustration of optical (laser-based) inline metrology system and
the data processing via Neural Network and big data analysis by cloud computing
machine algorithms
Smart Fracture Sensing & Prediction
(in Collaboration with Joerg Bagdahn, Center for Silicon Photovoltaics, and Ingo Chmielewski, Axxeo GmbH)
32. Implementing in High-Vol Manufacturing
(In Collaboration with REC Group, Singapore) Solar PV Laminator
Solar PV Stringer
Tippabhotla et al.
(Solar Energy
Materials & Solar
Cells 2019) – Co-
publication with
REC Group
PV Frac-sense
PV Stress-sense