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{{Paper
|id=Vol-3214/WS4Paper5
|storemode=property
|title=Manufacturing Line Qualification and Reconfiguration to Improve the Manufacturing Outcomes
|pdfUrl=https://ceur-ws.org/Vol-3214/WS4Paper5.pdf
|volume=Vol-3214
|authors=Estela Nieto,Ana Gomez,Myrsini Ntemi,Anna M. Nowak-Meitinger,Jan Mayer,Robert Trevino,Miguel A. Mateo-Casalí,Georgia Apostolou,Raul Poler,Ilias Gialampoukidis,Stefanos Vrochidis
|dblpUrl=https://dblp.org/rec/conf/iesa/NietoGNNMTMAPGV22
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==Manufacturing Line Qualification and Reconfiguration to Improve the Manufacturing Outcomes==
https://ceur-ws.org/Vol-3214/WS4Paper5.pdf
Manufacturing Line Qualification and Reconfiguration to
Improve the Manufacturing Outcomes
Estela Nieto1, Ana Gomez1, Myrsini Ntemi2, Anna M. Nowak-Meitinger3, Jan Mayer3,
Robert Trevino3, Miguel A. Mateo-Casalí4, Georgia Apostolou2, Raul Poler4, Ilias
Gialampoukidis2, and Stefanos Vrochidis2
1
Ikerlan Technology Research Center, Basque Research and Technology Alliance (BRTA), Pº J.Mª.
Arizmendiarrieta 2, 20500 Arrasate, Spain
2
Centre for Research and Technology Hellas, Egialias 52, 151 25 Marousi, Greece
3
Technical University of Berlin, Pascalstr. 8-9, 10587 Berlin, Germany
4
Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València
(UPV), Camino de Vera s/n, 46022 Valencia (Spain)
Abstract
Increased consumerism and the competitiveness of the global market have led to more
stringent requirements in terms of product quality and manufacturing lines. The i4Q
European project’s Rapid Manufacturing Line Qualification and Reconfiguration set of
solutions aims to develop new and improved strategies and methods for process qualification,
process reconfiguration and optimization using existing manufacturing data and intelligent
algorithms. The set of solutions provides manufacturing lines’ diagnosis and prescription,
process capacity forecasting, manufacturing line reconfiguration propositions, and data
quality certifications and audit procedures. With this information, plant managers can make
the required changes to the plant to improve manufacturing products’ quality, machines’ life
cycle, plants’ productivity and so on.
Keywords 1
i4Q, machine learning, smart manufacturing, process qualification, diagnosis and prognosis,
digital twin, manufacturing line reconfiguration.
1. Introduction
Economic globalization and increased consumption in society have caused companies to need to
optimize and improve production processes. This has resulted in increased competitiveness in the
global market and the need for stricter requirements in terms of product quality and manufacturing
lines [1]. To meet these stringent requirements, production line inefficiency and hidden loss must be
detected and eliminated [2]. Currently, those actions are carried out through technological advances,
such as sensors, machine data collection systems, data processing, application of analytical solutions
and control systems. These methods and technologies have provided plant managers with the
necessary tools to improve and optimize manufacturing lines by monitoring and controlling the state
of machines. Despite market needs and technological advances, researchers and manufacturing
professionals have struggled to detect the main cause of production lines’ inefficiencies and to
determine the necessary improvements to increase productivity.
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: enieto@ikerlan.es (E. Nieto); ana.gomez@ikerlan.es (A. Gomez); nmyrsini@iti.gr (M. Ntemi); nowak@tu-berlin.de (A.M.
Nowak-Meitinger); j.mayer@tu-berlin.de (J. Mayer); robert.trevino@tu-berlin.de (R. Trevino); mmateo@cigip.upv.es (M.A. Mateo-Casalí);
gapostolou@iti.gr (G. Apostolou); rpoler@cigip.upv.es (R. Poler); heliasgj@iti.gr (I. Gialampoukidis); stefanos@iti.gr (S. Vrochidis)
ORCID: 0000-0002-5024-1051 (E. Nieto); 0000-0001-5720-0183 (A. Gomez); 0000-0002-5081-6863 (M. Ntemi); 0000-0002-3564-0513
(A.M. Nowak-Meitinger); 0000-0001-5086-9378 (M.A. Mateo-Casalí); 0000-0003-1664-0224 (G. Apostolou); 0000-0003-4475-6371 (R.
Poler); 0000-0002-5234-9795 (I. Gialampoukidis); 0000-0002-2505-9178 (S. Vrochidis)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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As noted by [3], any robust metric of a plant should be characterized by four features:
• Inclusiveness: the capacity to account for all pertinent aspects.
• Universality: the capacity of being compared under various operating conditions.
• Measurability: the capacity to measure data analytically.
• Consistency: the capacity to be inherent to all organizational goals.
However, both throughput and utilization lack inclusiveness as they only account for a portion of
the manufacturing system performance. Additionally, the effectiveness of a plant depends on the way
it uses equipment, material, men/women and methods, which cannot be easily measured [2].
Machines lack consistency, as they are programmed with the main goal in mind but neglect other
secondary goals (e.g. electric consumption optimization). And machines’ sensors lack universality, as
the gathered data is usually tied to a specific operating condition.
2. Rapid manufacturing line qualification and reconfiguration
In the i4Q European project [4], the Rapid Manufacturing Line Qualification and Reconfiguration
set of solutions aim to develop new and improved strategies and methods for process qualification as
well as process reconfiguration and optimization through currently used manufacturing data and smart
algorithms.
This set of solutions considers the particularities of the final process, adapting the available
algorithms when needed. Furthermore, it considers the format in which the data may be obtained from
the system, such as newly added sensors, readily available legacy data, or virtual sensors. A
breakthrough of this work package lies in the use of the Digital Twin (DT) to:
i) Analyze the effect of process parameters on the final product quality.
ii) Obtain virtual sensors that extend available process data.
iii) Explore potential upgrade actions on the system.
These solutions (Figure 1) address the following topics:
i) Continuous process qualification to determine that outputs are within limits (PQ).
ii) Diagnosis strategies to detect cause of defect and recommend rapid corrective actions (QD).
iii) Simulation to guide preventive actions (PA).
iv) Optimization to reconfigure the production line (LRT).
v) Process and data certification based on reliable collected data (LCP).
Figure 1: Rapid Manufacturing Line Qualification and Reconfiguration set of solution’s diagram
2.1 Manufacturing line continuous process qualification
Continuously evaluating manufacturing processes is a common procedure to describe the
capabilities of machinery in manufacturing industries. Therefore, multiple descriptive indices like
process potential (Cp) and process performance (Cpk) [5] are calculated from samples to assess
manufactured products in relation to a distinct quality feature. Maintaining this evaluation over time
allows interpretations about process quality. To achieve this information, it is sufficient for the
evaluation to assess a measurable quality feature concerning its numerical characteristics.
Furthermore, upper and lower tolerance levels are introduced by the process owners to define hard
limits once the process is not in the desired quality range. This procedure is described in literature as
statistical process control (SPC) [6]. In spite of the broad industrial application of the SPC, describing
samples does not allow a real-time evaluation of desired process capabilities. In addition, non-normal
distributions of the chosen samples or multi-stage manufacturing processes decrease the interpretation
of the process and its performance [7]. To overcome this problem, the continuous line qualification
(i4QPQ) is developed.
i4QPQ as software solution is providing three main services for process owners as microservice. It
uses sensor data from manufacturing machines for its calculations:
1. Continuously evaluating process parameter Cpk: reading real-time data streams and presenting
them over specified time range or product quantity transformed to non-normality performance
index. Furthermore, single parameters can be adjusted by inserting them in a sidebar to create
personalized analysis.
2. Indication of distribution characteristics: to inform the process owner about the distribution
over the specified time range or product quantity, a distribution plot and the highlighted
confidence interval of the last number of chosen products is displayed.
3. Capacity forecast and forecast accuracy: as information about the chosen time range into the
future, a forecast about the process capacity is made concerning the current conditions of the
machine. Additionally, the evaluation criteria of the forecast algorithm are provided to leave
the decision about further actions in the process owners’ responsibility.
Conclusively, the i4QPQ application is a customer centric and user-friendly software tool, which
allows the interpretation of current and future process capability. Additionally, information about the
current process status and its distribution are facilitating machine ramp-up and condition monitoring.
2.2 Manufacturing line quality diagnosis and smart alerting
The rapid quality diagnosis solution (i4QQD) is a microservice aiming to provide an efficient
rapid diagnosis on possible causes of failures, on manufactured products quality, and on
manufacturing process conditions. Specifically, i4QQD incorporates intelligent techniques to improve
the final product quality by applying state-of-the-art Machine Learning (ML) techniques on industrial
sensor signals [8]. Moreover, it conducts advanced causality analysis on machine parameters and on
manufacturing conditions to infer the most influencing parameters. This mechanism is crucial since
usually complex relationships and erratic dynamics govern industrial data, while exact patterns of
signals evolution through time are not profound and latent factors affecting the overall manufacturing
process cannot be directly detected by a human operator.
i4QQD key advantage is that instead of simply applying traditional ML algorithms, it reveals the
laws that industrial data may obey, by explicitly modeling their trends, discontinuities,
interdependencies, and interactions with respect to causation. The analysis results are provided
i4QLRT (described below) to optimally reconfigure system parameters, and to take corrective actions
if a problem is detected. Consequently, this process reaches three major goals towards zero-defect
manufacturing: i) it reduces waste and cost, ii) it eliminates defects, and iii) it optimizes the overall
production quality. Classification and regression are the fundamental ML approaches applied by
i4QQD. The first one is applied in cases where the objective is to predict the class of given industrial
data (e.g., chatter detection in computer numerical control (CNC) machining industries), while the
second one is applied in cases where the objective is to anticipate estimations for future values of a
certain variable (e.g., surface roughness prediction in CNC machining industries), For classification
tasks, tree-based learning algorithms are deployed (e.g., gradient boosting frameworks). For
regression tasks, recurrent neural networks are utilized (e.g., long-short-term memory neural
networks, bi-directional long-short-term memory neural networks, gated recurrent units) [9]. Granger
causality analysis is applied to detect the most influencing parameters affecting a manufacturing
process and Kalman filtering and Markov Chain Monte Carlo techniques model the dynamics
governing the sensor signals.
2.3 Simulation and optimization for smart manufacturing line
reconfiguration prescription
Even the simplest models contain a significant number of internal parameters (for instance, the
mass, stiffness, thermal conduction, and so on). These parameters are all correlated, hence changing
the value of those parameters have a great impact on the model’s behaviour. This complicates the
analytical design of an optimum and robust system, and the analytical prediction of the effect of
parameters’ variation due to materials wear, vibration or other disturbances on the model’s behaviour.
The prescriptive analysis tool (i4QPA) was created to deal with these issues. The i4QPA is a
simulation, optimization and prescription tool. The main objective of this solution is to prescribe the
optimum system configuration according to an evaluation function, being a configuration of a specific
model’s parameters’ values data frame, and the system any DT model. To obtain the optimum
configuration of a system, the i4QPA creates a model configurations data frame where every line
represents a possible configuration of the model. Then the i4QPA conducts multiple simulations (one
simulation per configuration), evaluates those simulations results and ranks them according to the
selected evaluation function. Finally, the i4QPA prescribes the winning simulation’s configuration.
The i4QPA solution is a user-friendly application in which the user can select the model, the
parameters to vary, the evaluation function and the model’s input data. Therefore, the solution
requires a model and input time-series database (DB). Following the models/DTs standards, the
i4QPA expects an FMU type model as model input, which is read and processed with a variation of
the “FMPy” library.
Regarding other solutions, the i4QPA may give input to the i4QLRT (described below) and can be
supported by the i4Q Analytical Dashboard, a data visualization solution. The communication
between solutions is done through REST APIs.
2.4 Manufacturing line reconfiguration
The manufacturing line reconfiguration solution (i4QLRT) proposes changes to manufacturing
systems’ configuration parameters to achieve improved quality targets. This is done through a
collection of simulation-based optimisation microservices that evaluate different possible scenarios,
thereby increasing productivity and reducing manufacturing line reconfiguration efforts through
Artificial Intelligence (e.g., optimisation algorithms, ML models). The solution can be used in the
cloud or deployed on-premises, with sensor data from the manufacturing process as input and
operational data as output (e.g., configuration parameters and actuation commands); it will optimise
the manufacturing line with the best available set of parameters.
This solution is therefore based on optimisation algorithms developed in Python and deployed
through a container (e.g., Docker) that exposes its main functions (metadata description and
input/output configuration) as REST interfaces and provides access to data services (messaging,
storage). It offers ready-to-use optimisation functions for business processes, data pipelines or
applications. In this sense, the main architecture building blocks (ABBs) identified are the set of
algorithms implemented in Python and the REST wrapper that facilitates deployment and integration
into data pipelines and workflows. The wrapper must implement the client libraries to connect to the
i4Q data services and DT APIs and must be deployable at the edge and manageable as an AI
workload.
As microservices are deployed in Docker containers, they do not have strict hardware
requirements, although they can benefit from GPU acceleration. Security, governance and
management capabilities are delegated to external services through integrations using standard
technologies (e.g., OAuth, certificates, RBAC). The i4QLRT software has interfaces to integrate into
data pipelines (e.g., messaging, REST APIs, gRPC, OpenAPI). i4QLRT has no strict dependencies,
and it is interoperable with other i4Q solutions such us, i4Q Digital Twin, i4Q Services for Data
Analytics, i4Q Big Data Analytics Suite, i4Q Edge Workloads Placement and Deployment.
2.5 Certification and audit procedure
The i4Q Line Data Certification Procedure (i4QLCP) is a digitized certification and audit
procedure to ensure data quality for reliable use of all related i4Q solutions. The solution provides a
digital workflow for an audit procedure which can be conducted on manufacturing resources, e.g.,
machine, cell or manufacturing line. The workflow contains all essential steps in a logical sequence of
activities which need to be performed to ensure high level data quality throughout the complete data
generation process. These activities include auditing manufacturing resources for data generation
(e.g., sensors, controls, software), using calibration devices, and performing tests. Furthermore, the
procedure provides additional information to users, like shopfloor workers, process owners, and
auditors, in the form of definitions and vocabulary, frame and application areas, prerequisites,
planning, implementation, controlling, improvement and documentation of data-driven qualification,
reconfiguration, applicable standards, and quality control.
The audit procedure works as a guideline and introduces gates that represent predefined
milestones. For each milestone, the fulfilment of all requirements, necessary criteria, or activities is
needed to allow the user to proceed to the next phase of the procedure. This is achieved, e.g., by
uploading a digital signed document or report by the responsible user to a DB. To facilitate
completion of the individual milestones, the procedure also contains relevant information in the form
of instructions, references to relevant standards and internal company documents. After each gate is
passed successfully, the software will provide a digital certificate. The complete documentation is
stored on a secured and access-restricted DB with user identification. By accessing the secure DB,
auditors are able to find all necessary documentation for each performed activity. Recertification
procedures are simplified and less time consuming, as are all activities for audit preparation.
This procedure will complement already existing quality certifications by introducing
manufacturing process data quality as a factor that needs to be considered in future audits.
3. Conclusions
The Rapid Manufacturing Line Qualification and Reconfiguration set of solutions aims to develop
new and improved strategies and methods for process qualification, process reconfiguration, and
optimization using existing manufacturing data and smart algorithms. The solutions are the following:
• The i4QPQ continuously evaluates manufacturing processes checking if outputs over time are
within limits and forecasting process capacity.
• The i4QQD is a microservice aiming to provide efficient rapid diagnosis on possible causes of
failures, on manufactured products quality, and on manufacturing process conditions.
• The i4QPA is a DT/model simulation, optimization and prescription tool.
• The i4QLRT proposes changes to manufacturing systems’ configuration parameters to
achieve quality targets.
• The i4QLCP is a digitized certification and audit procedure to ensure data quality for reliable
use of all related i4Q solutions.
All the solutions as a whole give the user the tools to analyze and correct all manufacturing line
aspects, from sensors, to processes, machines and manufacturing lines, fulfilling to a large extent
Beamon’s [3] inclusiveness metric. Moreover, the solutions carry out a wide range of actions, from
predictions, to diagnosis, simulations, prescriptions, optimizations, qualifications, and so on. Actions
that add the Beamon’s metric of consistency to any manufacturing line, as the more information a
company has, the better decisions the company carries out, and so a wider range of organizational
goals can be fulfilled.
The PA and LRT carry out simulations under various conditions in order to obtain the optimum
model or manufacturing line configuration, which would check the Beamon’s universality metric.
And last, but not least, the measurability metric if fulfilled by the PQ, QD and LCP, as they analyze
and check multiples aspects of sensors’ or models outputs’ signals.
These solutions altogether, provide manufacturing line managers with the tools to obtain all the
robust metrics defined by Beamon [3]. Therefore, managers have all the required information to make
the best decisions and changes to the manufacturing line, from the smallest internal process of the
manufacturing line to the whole configuration of the manufacturing line, which would improve the
production, the quality of the manufactured products and the machines’ life cycle.
As for future research lines, every solution or software have its own future research line. The
i4QPQ will be improved by implementing continuous evaluation criteria assessment for machine
learning models and comparing the evaluation criteria of multiple forecasting algorithms and their
outputs. The i4QQD’s future work will be focused in adopting Transfer Learning techniques to
combine the abilities of various models and enhance the performance of the i4QQD, in exploiting
clustering techniques to address the frequent issue of unlabeled data, and in adapting Deep Neural
Networks to classification-related tasks. One of the future research lines of the i4QPA would be the
optimization of simulations to decrease the consumed time and computational power. The other future
research line of the i4QPA is shared with the i4QLRT, that is, to communicate the i4QPA or/and
i4QLRT to a system so that the results of the solutions affect the system and changes its
configuration. The i4QLRT is focused on the industry, therefore, in the future it could be applied to
different domains where control algorithms, operations or different learning models could be applied.
And last but not least, the i4QLCP, in the future, should provide a basis for standardization, e.g. a new
data quality auditing standard or the addition of data quality requirements to existing series of
standards such as ISO 9000f.
4. Acknowledgements
The research leading to these results is part of the i4Q project that has received funding from the
European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement No
958205.
5. References
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[4] i4Q Project Website, 2021. URL: https://www.i4q-project.eu.
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