Production Systems Engineering Research Articles

JCISE Editorial Board - Year 2023

Abstract The Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to scientific computing methods (e.g., modeling, simulation, representation, algorithm) and computational tools (e.g., high-performance computing, virtual and augmented reality) that aim to improve engineering products and systems for their complete lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, and recycling). The interest areas include computational geometry, computer-aided design and manufacturing, cyber-physical systems, human-machine interface, machine intelligence, machine learning, modeling and simulation, precision engineering, product lifecycle management, reverse engineering, and systems engineering.

Active learning based reverse design of hydrogen production from biomass fuel

Biomass hydrogen production holds substantial promise in addressing critical challenges within the realms of renewable energy and environmental sustainability. Nevertheless, the labor- and time-intensive nature of conducting combinatorial experiments and relying on traditional trial-and-error methods significantly constrains the exploration of biomass fuel solutions. This paper introduces a novel framework for the reverse engineering of hydrogen production systems, which seamlessly integrates Bayesian active learning with machine learning models. In Bayesian active learning, 307 biomass fuel samples were expanded to 1354 in order to ensure the accuracy of the model prediction between the seven characteristics of biomass fuel and hydrogen production. Four machine learning algorithms including RF, XGBoost, KNNR, AdaBoost were used to build the prediction model. A stochastic search reverse design approach based on the XGBoost model with the highest coefficient of determination (R2 = 0.91) was innovatively used to realize the “reverse design” from “target performance” to “composition and process”. Through reverse design, a two-order-of-magnitude increase in design space is achieved in hundreds of times less time than conventional design time, resulting in a significant improvement in hydrogen production efficiency. The practical application of this method for hydrogen production from biomass fuels may go far beyond linear extrapolation based on the current state of the art and can be applied to a wider range of material designs.

人の意思決定が埋め込まれた生産システムのモデル化と分析

Most production systems are operated in a human-in-the-loop fashion, and it is sometimes argued that the human decisions involved make it possible, or at least easier, for the systems to cope with various stationary and nonstationary variations. However, it has not been well-studied and understood how this positive contribution of human decisions work, what factors determine its effect, how the function should be supported or fostered, etc. This paper first briefly reviews conventional production systems simulation techniques and discusses why it is difficult for them alone to address aforementioned questions. This next points to some recent attempts, in production systems engineering and related areas, to study human decisions and their effects by complementally using gaming simulation and agent-based simulation and highlights the potential of combining such behavioral and computational scientific approaches. Then, the paper introduces a cognitive framework model composed of interface, interaction, and incentive dimensions. It can be used for formally characterizing the decisions made by an individual facing a problem situation in operating a production system, and functions as a bond connecting behavioral and computational analyses of the decision maker. The paper further presents some example ongoing research projects worked on by the author’s team in this direction and discusses some future perspective.

QualSec: An Automated Quality-Driven Approach for Security Risk Identification in Cyber-Physical Production Systems

As the threat landscape in the industrial domain continually advances, security-by-design is an ever-growing concern in the engineering of cyber-physical production systems (CPPSs). Often, quality aspects are not considered when securing CPPSs, which creates attack vectors that could lead to malicious activity affecting the products' quality. Since quality control systems generally provide inadequate protection against intentionally introduced defects, and can be susceptible to attacks, quality considerations must be integrated into security-aware CPPS engineering. For this purpose, we propose the QualSec method that automatically identifies security risks pertaining to CPPSs, building on the quality characteristics associated with manufacturing operations to determine cascading effects. QualSec is based on a semantic representation of engineering knowledge, allowing to efficiently reuse engineering models from AutomationML artifacts. Moreover, QualSec utilizes Petri nets to facilitate the analysis of security risks and cascading effects. In this way, QualSec informs users about possible attack paths for compromising quality characteristics, how attackers may disguise their malicious actions, and the possible consequences of attacks with respect to product quality. We demonstrate the benefits of QualSec in a case study and analyze its scalability through a rigorous performance evaluation.

Traceable Multi-view Model Integration: A Transformation Pipeline for Agile Production Systems Engineering

Purpose.Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several disciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies on concepts of PSE assets, such as plans and configurations. Thus, tracing multi-view changes to PSE assets is challenging.Method.Following the Design Science approach, we (i) elicit requirements for tracing multi-view changes to PSE assets from a domain analysis in automotive manufacturing; (ii) introduce and evaluate the Traceable Multi-view Model Transformation (TMvMT) process; and (iii) propose the TMvMT pipeline architecture to provide traceable model integration capabilities for agile PSE.Results.In a feasibility study on robot cell models, we evaluate the TMvMT process and architecture regarding the requirements for traceability compared to traditional approaches.Conclusion.The proposed TMvMT approach provides traceability of changes in multi-view modelling as a basis through the separation of modelling transformation steps and provision of clear input and output artefacts to achieve traceable configuration management and validation of system designs for production system assets in agile PSE.

Organizing reuse for production systems engineering with capabilities and skills

Abstract The flexibility of production systems is a key factor for Industry 4.0. Capabilities and skills (C&Ss) aim at improving engineering flexibility along the production system life-cycle by decoupling production processes and resources. However, traditional reuse approaches in production systems engineering, such as the VDI 3695, do not yet consider C&Ss. This paper proposes the Capability and Skill Reuse (CSR) framework to define how VDI 3695 activities require adaptation for C&S models. The paper analyzes how the framework can facilitate reuse along the production system life-cycle and identifies open issues for research.

Smart Counting Machines for Modular Industry 4.0 Packing Lines

The current volatility on the global market presents new challenges in production system engineering and control. On the one hand, packaging and packaging production systems must support increasing production volumes. On the other hand, market volatility requires more flexible production that is fragmented into smaller batches, which causes increased time, effort, and overall workload implied by changeovers between different production setups. Typically, it is not feasible to build a green-field generation of production sites that conforms to all design principles of Industry 4.0. Therefore, the addressed approach deals with a “smartification” of legacy hardware components, bridging multiple levels of the traditional automation pyramid, improving human-machine collaboration, and transforming human experience into a common shared knowledge base. In more detail, we are applying unsupervised learning methods to generalize individual machine settings into reusable and automatically applicable control setups and strategies. The core components of the addressed packing lines are counting machines that deliver the required portions of elements. The developed Smart Counting Machines retain current interfaces that are enhanced with new enhancements providing OPC UA communication and modular control layer. The realized solution has been deployed and tested in a real packing line of our industrial partner. The lessons learned in the presented mitigate barriers on the journey from the proof-of-concept phase to the full production deployment.

Lean Engineering – Identifying waste in engineering chains

Data and information in production systems engineering are the counterpart of material in production. To increase effectiveness and efficiency, waste in data and information flow must be defined in analogy to lean paradigm. For this purpose, the known eight types of waste are transferred to the data and information flow of production systems engineering, considering engineering activities and chain, to achieve overall process optimum. The validation of the defined types of waste using six case studies confirms their applicability and significance in terms of continuous engineering process improvement.

Towards a domain-specific information architecture enabling the investigation and optimization of flexible production systems by utilizing artificial intelligence

The industrial domain is undergoing a major transformation, pushed forward by emerging technologies originating from research or industry. The resulting trend, better known by the term Industry 4.0, advances automation within these manufacturing companies by providing ubiquitous interconnection. This enables the integration of technologies mainly used in the Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS) or Big Data with the goal to optimize production processes or facilitate intelligent decision-making. While those processes are progressively supported by methodologies coming from the area of artificial intelligence (AI) like machine learning algorithms, sustainable and consistent storing of production data becomes increasingly important. Concluding, production systems engineering and information engineering are correlating with each other, as the respective results could be used to the advantage of the respective other discipline. In order to address these issues while developing such flexible production systems, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been introduced. However, practical applications are lacking as this standard is mainly described in theory, which makes it difficult to actually apply this framework. Thus, the main goal of this paper is to specify a detailed architecture description of the Information Layer to ensure the practical application of RAMI 4.0, which allows stakeholders to utilize model-based Systems Engineering (MBSE) for developing data aspects of industrial systems on the one hand and enable Information Engineering on the other hand. Supported by the concept of the Zachman Framework, the resulting architecture is applied and validated with the help of a real-world case study.

Using two case studies to explore the applicability of VIATRA for the model-driven engineering of mechatronic production systems

The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: “SysML – AutomationML” and “SysML4Mechatronics – AutomationML.” Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems.

Strength of A Chair Renovated With Additive Production Systems and Reverse Engineering Approach

Furniture are items that provide convenience and comfort in daily works, and that are present in all areas of life. The furniture renovation process is of great importance for the protection of furniture and the maintenance of furniture’s function. Today, this process may include applications involving reverse engineering and additive production systems. The aim of this study is to provide guidance for amateur and professional workers in the field of furniture renovation about the use of reverse engineering approach and additive production systems in this field. In this study, missing details of a plywood chair were scanned with 3D scanners. Point clouds were obtained in STL format. These point clouds were converted into meaningful data and the missing details were printed out with the additive production system. Damaged areas of the chair were repaired with this method. The repaired chair sample and the original chair sample were subjected to static and dynamic strength tests, and the test results obtained from these samples were compared. As a result of these tests, the renovation work was found to be successful.

Product-Production-CoDesign: An Approach on Integrated Product and Production Engineering Across Generations and Life Cycles

Shorter product life cycles and high product variance nowadays require efficient engineering of products and production systems. Hereby a further challenge is that costs over the entire life cycle of the product and production system are defined early in the process. Existing approaches in literature and practice such as simultaneous engineering and design for manufacturing incorporate aspects of production into product engineering. However, these approaches leave potential for increasing efficiency unused because knowledge from past generations of products, production systems, and business models is not stored and reused in a formalized way and future generations are not considered in the respective current engineering process. This article proposes an approach for integrated product and production engineering across generations and life cycles of products and production systems. This includes the consideration of related business models to successfully establish the products on the market as well as the anticipation of future product and production system characteristics. The presented approach can reduce both development and manufacturing costs as well as time to market and opens the vast technological potential for product design to achieve additional customer benefits. Three case studies elaborate on aspects of the proposed approach and present its benefits.

BPMN++ to support managing organisational, multiteam and systems engineering aspects in cyber physical production systems design and operation

Abstract Interdisciplinary engineering of cyber physical production systems (CPPS) are often subject to delay, cost overrun and quality problems or may even fail due to the lack of efficient information exchange between multiple interdisciplinary teams working in complex networks within and across companies. We propose a direct integration of multiteam and organisational aspects into the graphical notation of the systems engineering workflow. BPMN++, with eight new notational elements and two subdiagrams, enables the modelling of the required cooperation aspects. BPMN++ provides an improved overview, uniform notation, more compact presentation and easier modifiability from an engineering point of view. We also included a first set of empirical studies and historical qualitative and quantitative data in addition to subjective expert-based ratings to increase validity. The use case introduced to explain the procedure and the notation is derived from surveys in plant manufacturing focussing on the start-up phase and decision support at site. This, in particular, is one of the most complex and critical phases with potentially high economic impact. For evaluation purposes, we compare two alternative solutions for a short-term management decision in the start-up phase of CPPS using the BPMN++ approach.

ІНЖЕНЕРНИЙ МЕНЕДЖМЕНТ ФУНКЦІЮВАННЯ АВТОМАТИЗОВАНОЇ СИСТЕМИ СТАБІЛІЗАЦІЇ ХОДУ СОШНИКА ПОСІВНОГО АГРЕГАТА СИСТЕМОТЕХНІКИ РОСЛИННИЦТВА

The purpose of the study is to justify the analytical provisions of the engineering management of the functioning of the automated system for stabilizing the course of the coulter of the sowing unit of the system engineering of crop production. The practical result will be the formation of the initial provisions of the Smart-technology of engineering management of changes in the states of independent elements of crop production system engineering.

Patterns for Reuse in Production Systems Engineering

In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for the representation of reuse knowledge in PSE, based on the results of a domain analysis in automotive manufacturing; (ii) refine the Industry 4.0 Asset Network (I4AN) meta-model that integrates multi-disciplinary dependencies between the assets; (iii) introduce the I4AN reference model that exposes recurring patterns; and (iv) present basic and applied patterns for reuse in PSE that aim at improving reuse efficiency and lowering risks. We evaluate the I4AN reference model and patterns with reuse scenarios in a feasibility study in automotive manufacturing. The study results indicate that the I4AN reference model and patterns satisfy the elicited requirements and enable PSE domain experts to identify patterns for reuse and sufficiently complete sets of reusable assets in their contexts.

Statistical Evaluation of Semi-Analytical, Analytical, and Numerical Models of the Serial Production Lines

Production lines are the backbone of the manufacturing industry. To gain the best profit out of a line it is necessary to design each line using the production system engineering. Therefore, three approaches can be used, the numerical, the analytical, and the semi-analytical approach. The aggregation method, finite state method, and the numerical approach are statistically compared concerning the analytical approach using the STATISTICA software. We analyzed the interaction between the input data and the output data for the finite state method in an illustrative example, using a full factorial design and the Design Expert software

Development of high-performance sports machine and elucidation of Two-seam ball gyroball

Sports engineering uses mathematics and physics to develop solutions to sporting problems. It involves the design and manufacture of sports equipment and facilities that can help maximise athletic performance. In order to develop fit-for-purpose products, it is necessary to consider the motions and movements of athletes. Professor Shinobu Sakai, Production Systems Engineering and Sciences, Komatsu University, Japan, is combining AI and neural networks (NN) to develop multifunctional and high-performance sports equipment and training machines that can benefit athletes and people across the globe who play sports. Believing that machines are subordinate to human beings, a key goal for Sakai is to augment the performance of sports players through the development of tools and machines. Some of the state-of-the-art tools and methodologies he uses in his work are high-resolution sensors, laser-speed sensors, MATLAB and general finite element method analysis software (ANSYS/LS-DYNA). He has developed a shuttlecock launching machine for badminton that can launch at high speed, a high-performance baseball pitching machine that is able to generate an efficient and objective gyro spin and has a high throwing performance and a pitching control method that uses AI.

Forecasting Structural Equation Modelling of Lean Manufacturing using High Order Statistical Functions

Typically, the primary goal of lean technology (LT) is to improve profits and create value by minimising waste. The consideration of demand variability in a multi-dimensional lean manufacturing environment is an innovation in production system engineering. However, manufacturing systems that fail to recognise demand variability generate high work-in-process (WIP) and low throughput in the lean manufacturing process. Moreover, many companies have found it difficult to successfully implement and sustain lean manufacturing. It is, therefore, very important for companies to identify and understand the critical success factors for successfully implementing lean manufacturing. To overwhelm the challenges in the implementation of lean manufacturing, this paper plan to introduce an advanced model and this model is analysed using structural equation modelling.

Transforming traditional nutrition paradigms with synthetic biology driven microbial production platforms

Abstract Malnutrition is a multidimensional issue, spanning at its heart both the quantity and quality of food. In recognition of the trade-off between yields and micronutrient quantity, biofortified and engineered crops have been developed. However, restrictive genetically modified organisms GMO legislation, shifting climate challenges, and public opposition have questioned their utility in the food chain. With the rise of synthetic biology, an alternate paradigm is developing whereby the production of traditional plant natural products and micronutrients are being explored using microorganisms, with the view of micronutrient supplementation and the development of functional food products. It is envisaged that successful implementation of metabolic engineering, data analytics, and synthetic biology of microbial micronutrient and food production systems will play a significant role in shaping the future of food production, food economics, farming, and human nutrition. Decreasing level of micronutrients in cereal crops is a serious issue, and this hidden hunger will be addressed by the developments in synthetic biology driven microbial production platforms in the future.

Integrating human factors in the model based development of cyber-physical production systems

In order to optimize production processes and to avoid errors, it is not only necessary to automate processes, but also to integrate workers wit their individual personality and skill profiles. For this purpose, human factors should be considered in the entire design process. The integrate view of mental human models, the cognitive demand of the working environment and the automation design is essential. Human-Syste Integration (HSI) constitutes a promising approach. Current model-based approaches offer possibilities to analyze and optimize tasks within a overall system, but they still lack integration. This leads to the research question: How can human factors be integrated into a system model of socio-technical, Cyber-Physical Production System? The paper at hand contributes an approach of human factor integration into the procedu of Model-Based Systems Engineering for Cyber-Physical Production Systems (CPPS). The approach combines a system model of a CPPS wi HSI concepts. In accordance to the benefits of MBSE, SysML is selected to integrate human factors in the development process of a CPPS. Th approach is divided into five steps, which include the extension of the SysML meta model. This allows the optimization of skill-based huma machine interaction. Defined HSI-Profiles enable system developers to integrate employee requirements at early stages within the developme process. The approach is demonstrated by the maintenance of a 3D-Printer as a case example. This research enables system developers to depi individual workers with the help of the developed concepts and systematically integrate them into the development process of a CPPS.