=Paper=
{{Paper
|id=Vol-3144/RP-paper4
|storemode=property
|title=Electrospindle 4.0: Towards Zero Defect Manufacturing of Spindles
|pdfUrl=https://ceur-ws.org/Vol-3144/RP-paper4.pdf
|volume=Vol-3144
|authors=Francesco Amadori,Michele Bardani,Eleonora Bernasconi,Federica Cappelletti,Tiziana Catarci,Gianluca Drudi,Mario Ferretti,Luigi Foschini,Paolo Galli,Michele Germani,Giuseppe Grosso,Francesco Leotta,Jerin George Mathew,Luca Manuguerra,Nicola Mariucci,Massimo Mecella,Flavia Monti,Fabrizio Pierini,Marta Rossi
|dblpUrl=https://dblp.org/rec/conf/rcis/AmadoriBBCCDFFG22
}}
==Electrospindle 4.0: Towards Zero Defect Manufacturing of Spindles==
https://ceur-ws.org/Vol-3144/RP-paper4.pdf
Electrospindle 4.0: Towards Zero Defect
Manufacturing of Spindles
Francesco Amadori1 , Michele Bardani2 , Eleonora Bernasconi3 , Federica Cappelletti4 ,
Tiziana Catarci3 , Gianluca Drudi1 , Mario Ferretti1 , Luigi Foschini2 , Paolo Galli1 ,
Michele Germani4 , Giuseppe Grosso1 , Francesco Leotta3 , Jerin George Mathew3 ,
Luca Manuguerra4 , Nicola Mariucci2 , Massimo Mecella3 , Flavia Monti3 ,
Fabrizio Pierini1 and Marta Rossi4
1
HSD SpA, via della Meccanica 16, 61122 Pesaro, Italy
2
EN4 Srl, via Salvatore Allende 11, 06073 Corciano (PG), Italy
3
Sapienza Università di Roma, Dipartimento di Ingegneria informatica, automatica e gestionale Antonio Ruberti (DIAG),
via Ariosto, 25, 00185 Rome, Italy
4
Università Politecnica delle Marche, Dipartimento di Ingegneria Industriale e Scienze Matematiche (DIISM), via Brecce
Bianche, 60131 Ancona, Italy
Abstract
Industry 4.0 represents the last evolution of manufacturing. With respect to Industry 3.0, which intro-
duced the digital interconnection of machinery with monitoring and control systems, the fourth industrial
revolution extends this concept to sensors, products and any kind of object or actor (thing) involved in
the process. The tremendous amount of data produced is intended to be analyzed by applying methods
from artificial intelligence, machine learning and data mining. One of the objectives of such analysis is
Zero Defect Manufacturing, i.e., a manufacturing process where acquired data during the entire life cycle
of products are used to continuously improve the product design in order to provide customers with
unprecedented quality guarantees. In this paper, we discuss the goals of the Electrospindle 4.0 project,
which aims at applying Zero Defect Manufacturing principles to the production of spindles.
Keywords
Industry 4.0, Zero Defect Manufacturing, Design for X, Artificial Intelligence
Joint Proceedings of RCIS 2022 Workshops and Research Projects Track, May 17-20, 2022, Barcelona, Spain
Envelope-Open francesco.amadori@hsd.it (F. Amadori); michele.bardani@en4tech.it (M. Bardani); bernasconi@diag.uniroma1.it
(E. Bernasconi); f.cappelletti@pm.univpm.it (F. Cappelletti); catarci@diag.uniroma1.it (T. Catarci);
Gianluca.Drudi@hsd.it (G. Drudi); Mario.Ferretti@hsd.it (M. Ferretti); luigi.foschini@en4.it (L. Foschini);
Paolo.Galli@hsd.it (P. Galli); m.germani@staff.univpm.it (M. Germani); Giuseppe.Grosso@hsd.it (G. Grosso);
leotta@diag.uniroma1.it (F. Leotta); mathew@diag.uniroma1.it (J. G. Mathew); l.manuguerra@staff.univpm.it
(L. Manuguerra); nicola.mariucci@en4.it (N. Mariucci); mecella@diag.uniroma1.it (M. Mecella);
monti@diag.uniroma1.it (F. Monti); Fabrizio.Pierini@hsd.it (F. Pierini); marta.rossi@staff.univpm.it (M. Rossi)
Orcid 0000-0003-3142-3084 (E. Bernasconi); 0000-0001-5592-3150 (F. Cappelletti); 0000-0002-3578-1121 (T. Catarci);
000-0003-1988-8620 (M. Germani); 0000-0001-9216-8502 (F. Leotta); 0000-0002-4626-826X (J. G. Mathew);
0000-0003-0832-3886 (L. Manuguerra); 0000-0002-9730-8882 (M. Mecella); 0000-0003-3349-7861 (F. Monti);
0000-0001-9287-8109 (M. Rossi)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
1. Introduction
Industry 4.0 represents the last evolution of manufacturing. With respect to Industry 3.0, which
introduced the digital interconnection of machinery with monitoring and control systems, the
fourth industrial revolution extends this concept to sensors, products and any kind of object or
actor, i.e., thing, involved in the process. Internet-of-Things (IoT) enters in the manufacturing
sector. The tremendous amount of data produced is intended to be analyzed by applying
methods from artificial intelligence, machine learning and data mining.
One of the objectives of such analysis is Zero Defect Manufacturing [1], i.e., a manufacturing
process where acquired data during the entire life cycle of products is used to continuously
improve the product design in order to provide customers with unprecedented quality guarantees.
The achievement of such an objective also requires to rethink the entire manufacturing process
and the entire supply chain, in order to consider every single phase of the product life cycle.
In this paper, we discuss the goals of the Electrospindle 4.0 project, which aims at applying
Zero Defect Manufacturing principles to the production of spindles.
A spindle is a rotating motor device employed in manufacturing for machining different types
of material, by applying different possible tools. Such machine undergoes severe functioning
conditions in terms of vibrations and shocks, thus reliability is a fundamental feature for
customers, which do not want to sustain inactivity periods due to maintenance operations.
HSD is an Italian company and one of the international leading companies in the production
of spindles for manufacturing customers. The Electrospindle 4.0 project has started in June
2021 and has a duration of three years. Its objectives are coherent with some of the lines of
action proposed by the Italian Cluster “Fabbrica Intelligente” (CFI), which clearly identifies HSD
as a Light House Plant for Zero Defects Manufacturing, i.e., a national reference point for what
concerns technological innovation. The identified lines of action are: (i) production systems for
a personalized production, (ii) efficient production systems and (iii) adaptive and evolutionary
production systems; that are also coherent with the European strategies for the development of
a resilient country in the manufacturing sector.
The nationally funded Electrospindle 4.0 project should sustain the effort of HSD in the devel-
opment of a new family of spindles respondent to the principle of Zero Defect Manufacturing.
The new family of spindles will be equipped with special sensors that will enable them to
independently transmit data related to their status and working conditions while performing
their functions on the machine tool on which they are installed. Such devices will be the result
of the application of several techniques, including cloud computing, machine learning (ML),
artificial intelligence (AI), digital twins (DT) [2] and Design for X [3]. The new manufacturing
process will promote a continuous improvement of design and manufacturing processes, with
the goal of creating more reliable and efficient products.
The paper is organized as follows. Section 2 summarizes the project organization, Section 3
outlines the objectives and Section 4 presents the current status of the project.
2. Summary of the project
The project involves four partners specialized in various aspects of the project. HSD (https:
//www.hsd.it/) and EN4 (https://en4.it/) are the industrial partners of the project. HSD, already
introduced in Section 1, is the project leader that will develop the new spindle family, the use
cases and the Zero Defect pilot production process. EN4 is a leading company in the production
of automated test systems and will devise new Industry 4.0 compliant smart testers. Sapienza
Università di Roma and Università Politecnica delle Marche (UnivPM in short) are the academic
partners of the project and will provide technological transfer from research to practice. UnivPM
will focus, in particular, on the technological solutions needed for the design and production
processes, whereas Sapienza will focus on software solutions to manage and elaborate the big
amount of data of the entire production process.
The project aims at applying innovative technologies to realize a family of Zero Defects
products (X-CORE spindles), produced through a Zero Defects Manufacturing process. Two
distinct and strictly connected results have been identified. The first one is the development of
the new product line, whereas the second one is the definition of an innovative Zero Defects
production process (process line). See Section 3 for further details about these objectives.
The X-CORE spindle will be a highly digitized product intended to be part of the HSD Industry
4.0 ecosystem. The integrated sensors and the interconnectivity features will allow a complete
control of the spindle, and to collect data during the entire product life cycle allowing to evaluate
the performance with respect to actual employment conditions. This will, in turn, allow to
improve the manufacturing and design processes.
The Zero Defects production process will be the result of a continuous improvement thanks
to the analysis of data acquired from multiple sources including raw materials and components
provided by suppliers, machines and humans on the manufacturing line, design phase projects,
maintenance information, and of course data coming from the new X-CORE spindle. This will
allow to make the production process iteratively more and more reliable and responsive.
3. Objectives and expected tangible results
The project is organized into eleven workpackages (WPs). The first five WPs (WP1-WP5)
have industrial research objectives, whereas the last six (WP6-WP11) represent prototype
development activities. In particular, the WP6-WP11 temporally comes at the end of WP1-WP5,
representing their implementation phases. In the following, we show how these workpackages
contribute to the two objectives defined in Section 2.
The X-CORE devices are the evolution of the E-CORE spindles, patented by HSD in 2007,
which are intelligent devices able to self-monitor their status and conditions. E-CORE spindles
are examples of devices for the Industrial Internet of Things (IIoT) and the X-CORE family will
improve the functions, performances, duration and reliability of the product. Innovative sensors
will be added and the spindle will be able to communicate with an Edge/Cloud architecture able
to manipulate the huge amount of produced data for different purposes, including predictive
and preventive maintenance strategies. The design and development of the new X-CORE family
are the goals of WP1 and WP6 respectively.
Figure 1: End-to-End production chain data flow
A new end-to-end manufacturing process will be developed with the purpose of (i) turning
the process from reactive to proactive, (ii) designing a new logistic for raw materials and
components, (iii) studying the possibility of applying augmented reality to production, and (iv)
studying the functionalities of the new Manufacturing Execution System (MES). All of these
objectives are the goal of WP2 and WP7.
As can be seen from Figure 1, in the new process data will come from the suppliers, from the
HSD factory and from the customers. These data will be used to establish a circular feedback
to improve the design and manufacturing processes. The new production strategies will be
defined to guarantee real-time monitoring, reduce products defects, decrease costs and reduce
downtime.
The definition of a new family of spindles will be coupled with a new family of testers (called
smart testers), which will support the new features introduced by the X-CORE spindles. These
new testers will be additionally integrated with the cloud infrastructure defined for the project.
The design and development of smart testers are the goals of WP3 and WP8 respectively.
The novel manufacturing process will follow the product throughout the entire life cycle. This
means also to consider what to do with the product at the end of its life. In particular, WP4 and
WP9 will take care of so called Design-for-X, where X can be in the Electrospindle 4.0 product
End-Of-Life (EoL) or environmental sustainability. The following objectives must be considered
during the design phase: costs, defects, maintainability, energy saving, product sustainability,
EoL management. A system will be developed to assist the designer in the identification of
product’s critical points and the alternative technical solutions to cover, e.g. optimization of
the high added value components disassembly suitable for De/Re-manufacturing strategies [4].
The same WPs will also define the new services offered by HSD to its customers thanks to the
newly developed product line.
The data gathered in the context of the project must be analyzed by using appropriate facilities.
WP5 and WP10 have as main goal the definition of a cloud/edge architecture allowing to develop
a multidimensional analysis including data mining, pattern analysis and visual analytics. WP5
and WP10 are also in charge of defining dashboard and visual analytics tools.
An important role in the project will be played by so called Digital Twins (DTs). The term
is used in the project with a double meaning [2]. DTs will be employed in WP1 and WP6 for
simulation purposes. In addition, DTs for monitoring, retrieving data and perform predictive
and prescriptive maintenance tasks will be defined in the context of WP5 and WP10 [5].
Finally, WP11 is the integration WP, where results obtained in the other WPs are combined
and evaluated.
4. Current project results
The Electrospindle 4.0 project started in June 2021. So far, only industrial research activities
have been performed. In particular:
• The main characteristics of the new X-CORE family in terms of sensors and interesting
measures have been chosen. In particular, these focus on three main components of
the spindle: bearings, docking system and drive unit. Accordingly, several sensors were
identified to measure temperature, vibration, speed, rotation, electromagnetic field, tie-rod
position and impacts. The X-CORE circuit board will compute additional parameters that
can be derived from the collected measurements. Finally, testing parameters, such as
mechanical and electrical safety parameters, were also identified to contribute to Zero
Defects Manufacturing. Also, networking hardware has been defined in order to ensure
the necessary bandwidth to transmit all the obtained information.
• The AS-IS production process has been analyzed through different techniques to find
the main characteristics that are needed to reach the Zero Defect Manufacturing. The
Value Stream Analysis (VSA) [6] method was used to define the production process’s Key
Performance Indicators (KPIs), e.g., stocks, total process space, transportation, cycle time,
set-up time. Next to the KPIs, the product and process sources of error were determined
by performing the Failure Mode and Effects Analysis (FMEA) [7]. Another important
analysis made was about the value stream of each product, that identified the value-added
activities, the non-value-added necessary activities and the non-value-added unnecessary
activities. Finally, the Critical-To-Quality (CTQ) value (perceived by the customers) was
defined, by following the Lean Six Sigma [8] principles, to improve the products and
services qualities.
• A cloud system and a dashboard were devised to collect Smart Tester data in real time
with the purpose of monitoring and visualizing the test performances. The architecture is
deployed on an AWS virtual machine where Docker containers instantiate the PostgreSQL
database to store data, the MQTT broker to receive messages from the tester and the REST
API to communicate with the implemented dashboard. The developed system results to
be highly scalable allowing to integrate Smart Testers in the Zero Defect production.
• The Design for X tool was defined to support the designer in the development phase.
CAD models, data stored in the central database and additional details defined by the user
are analyzed with the goal to improve the product recyclability and re-manufacturing
and to optimize its assembly and disassembly. The analysis produces KPIs and design
rules validations that are used to improve the design.
• The solution will be based on a mix of edge, public cloud and private cloud computing [9].
In the context of the project:
– Machine learning (both training and evaluation) and data mining tasks will be
executed using resources from the public cloud (e.g., Azure Machine Learning).
Identified tasks fall in the categories of descriptive, predictive (e.g., Remaining Useful
Life [10]) and prescriptive maintenance. In addition, correlation techniques will be
employed in order to correlate online data with design choices and constructive
features of materials used for manufacturing. Process mining will be also used to
analyze the end-to-end process [11].
– The private cloud will be used to store data from HSD own information systems.
These systems include the ERP, the CRM and the MES, which is one of the intended
outcomes of WP2.
– Data from X-CORE spindles will be stored in a public cloud. This public cloud is
already available and called MyHSD.
– Part of the models will be trained in the public cloud and will be evaluated directly
on the spindle using edge computing [12]. To this aim, the X-CORE family will be
equipped with computing capabilities.
5. Concluding remarks
In this paper, we have introduced the Electrospindle 4.0 project, whose aim is to design a
completely new family of spindles together with their production process towards Zero Defect
Manufacturing. The project is in an initial phase where only industrial research WPs have
already started. Nonetheless important practical challenges have already been identified.
First of all, one of the technical challenge consists in the collection of a dataset big enough to
allow for machine (deep) learning training. Unfortunately, the available data could be severely
unbalanced. High resolution spindle data will be likely available only in certain phases of the
life cycle, namely manufacturing and maintenance, whereas the data coming from the customer
will be at a lower resolution (i.e., one measurement every 10 seconds), making it difficult to
detect short term phenomenon. This is due to the necessity of reducing the data transmitted
by customers for their installed spindle. This challenge could be addressed, in principle, by
adding a fog layer to the architecture, but during the analysis phase the consortium decided
that placing an additional infrastructure at the customer side is not feasible for security reasons.
Runtime data is needed first of all for predictive and prescriptive maintenance tasks, but it
also important to help the designers to find correlations between issues detected at the spindle
and characteristics of the raw materials and semi-product employed during the manufacturing
phase. This last task must be feasible either using (semi-)automatic techniques or by providing
suitable graphical user interfaces for visual analytics.
Another challenge under study, is how to track the product during shipping and installation
operations, when no power source is available. Certain events during shipping (e.g., impacts)
may indeed tamper the integrity of the spindles, thus influencing the life expectation. To this
aim, a possible solution would be the employment of low energy, battery powered, solutions.
A last challenge worthy to be mentioned is the safe interaction between the HSD private
cloud and the public cloud solutions that will be used for training purposes. In order to preserve
the confidentiality of company’s data, data transfer flow must be designed in order to keep the
data in the public cloud only at training time. Also, the cost of the public cloud solutions is an
important aspect for the sustainability of the project in the future.
Acknowledgements
The work of all the authors is funded by the Italian Ministry of Economic Development (Ministero
dello Sviluppo Economico - MISE) with the project Electrospindle 4.0 (id: F/160038/01-04/X41).
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