Modern Manufacturing Systems Research Articles

Sustainable Manufacturing Supply Chain Performance Enhancement through Technology Utilization and Process Innovation in Industry 4.0: A SEM-PLS Approach

The fourth industrial revolution brought a paradigm shift in the present manufacturing system and its supply chain management (SCM). The evolution of Industry 4.0 (I4.0) brought several disruptive technologies like cloud computing (CC), blockchain, the Internet of Things (IoT), cyber-physical systems (CPS), etc. These disruptive technologies have changed the face of the modern manufacturing system and its manufacturing supply chain (SC). Several changes in manufacturing in terms of lead time, cost reduction, agility, flexibility, and response to market sensitivity are seen in almost all types of manufacturing. I4.0’s disruptive technologies influence lean SC, agile SC, leagile SC, and green SC. The current study examines how I4.0 technologies affect society on such supply chains (SCs), which leads to enhanced performance of the manufacturing SC. The effect of process innovation (PI) resulting from I4.0 innovations is also investigated. SEM-PLS-based modeling is constructed based on 195 responses received from manufacturing enterprises implementing various SC practices in managing their manufacturing SCs. The findings demonstrate a favorable correlation between I4.0 technology and the enhancement of various SCs. The result also revealed that there is a positive impact of I4.0 technologies on PI, which leads to manufacturing SC performance improvements.

Small local firms struggles: insertion process and entry barriers as lowest-tier automotive suppliers in Indonesia

This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0/ This paper examines how small local firms integrate into the automotive value chains and what entry barriers they face during this process. Using primary data from surveys on domestic suppliers and interviews with subsidiaries of Japanese automotive producers operating in Indonesia, the study found that apart from the initial condition of local firms that were mostly less capable and inexperienced in the modern manufacturing system, the subsidiaries also purposively created various entry barriers and provided limited assistance. Consequently, the linkages opportunities available for local firms were confined to lowest-tier suppliers and the integration process as new suppliers was arduous, costly and time consuming. Given such a situation, the paper proposes the insertion process as a new industrial upgrading trajectory and labels it as entry upgrading. Moreover, the research also suggests that workers’ mobility and demonstration are pivotal channels for local firms to acquire advanced knowledge necessary for their entry upgrading.

A deep reinforcement learning based hyper-heuristic for modular production control

In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.

Adaptive fusion based on physics-constrained dictionary learning for fault diagnosis of rotating machinery

Modern manufacturing systems rely on multiple types of sensors to monitor the manufacturing processes and machine faults to ensure product quality. The costs associated with sensor installation, maintenance, data transmission, and data storage are high. In this paper, a new data fusion method based on physics-constrained dictionary learning is proposed to improve the efficiency of data collection and the accuracy of fault diagnosis. In the proposed dictionary learning method, the measurement, basis, and classification matrices are optimized for the effective use of compressed sensing technique in process monitoring. With the optimized matrices, full-scale high-resolution signals can be reconstructed from a small number of sensor measurements. At the same time, machine states can be identified based on the sparse measurements. An adaptive weight scheme is introduced to combine low-cost sensor data so that the accuracy of fault diagnosis can be improved. The proposed method is tested with an experimental dataset of gearbox vibrations caused by gear cracks and different levels of crack severities are classified. The results show that up to 70% of data collection can be saved with the new approach while more than 95% of diagnosis accuracy is achieved. The sensitivities of the performance with respect to the number of measurements and number of sensors are also studied.

Preface

The 7th International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2023) was held virtually from February 24-26, 2023. On behalf of the Organizing Committee of this Conference, it is my great pleasure and honor to introduce the proceedings of ICMAA 2023.The proceedings include a variety of advanced research topics which were presented in the conference. Full papers submissions were blind peer-reviewed by at least two experts from the International Program Committee in order to ensure high quality standard of research. The papers are evaluated on the basis of their originality, innovation, technical value, and applicability. The selection process followed strict criteria and consequently only 28 full papers were selected and accepted for publishing.ICMAA2023 Conference Committee has chosen Zoom as the virtual platform and invited authors to give oral presentations based on their research works, followed by a short Q&A part. Before the Conference, all the authors are to prepare and submit videos as backups in case of unexpected technical issues. In the Conference, oral sessions were well organized with participation of scientists from all over the world. They actively discussed about Modern manufacturing system and technology, Mechanical structure design and strength analysis, Friction and material life analysis, Power machinery and system simulation and etc.The success of the Conference is in a large way attributed to the expertise and experience of the thematic coordinators in promoting and coordinating the overall organization and smooth running of the Conference. Project leader(s) in each branch has put much time and effort into soliciting papers from relevant researchers for presentation at the conference. The chairperson of each session has helped ensure a lively discussion during the Q & A and kept to the time allocation. On behalf of the Conferences Committee, I would like to express our sincere thanks to them for their fervent participation. We would also like to express our sincere thanks to all the reviewers.We hope to meet most if not all of you at the next ICMAA to have an exciting academic and practical discussions/exchanges. At the meantime, do keep safe until then.List of Organizing Committees are available in this Pdf.

A Parallel distributed genetic algorithm using Apache Spark for flexible scheduling of multitasks in a cloud manufacturing environment

ABSTRACT Cloud manufacturing is one of the modern manufacturing systems that aim to control and manage production processes through a cloud platform to optimize the usage of resources and achieve customer objectives. Scheduling large-scale problems with diversity in the required tasks and the available services is a significant challenge for any researcher, especially with the possibility of recurring dynamic events, such as the arrival of new tasks. Thus, our first objective is to present a mixed-integer mathematical model that demonstrates the complexity of scheduling problems in a dynamic CMfg environment. We developed a parallel distributed genetic algorithm (PDGA) for optimizing large-scale scheduling problems on Apache Spark. The PDGA adopts a resilient distributed dataset (RDD) to improve the decomposition of the population and also enhance both the performance and the speed of the evolution process. The PDGA then updates the schedule by using a greedy strategy to improve the overall performance. To verify the effectiveness of the PDGA, we evaluated the algorithm on a benchmark and generated 8 large-scale problems based on this benchmark. The experiments show that the PDGA provides better performance and lower computational time compared to the traditional genetic algorithm and particle swarm optimization algorithm.

Directional fault classification for correlated High-Dimensional data streams using hidden Markov models

Modern manufacturing systems are often installed with sensor networks which generate high-dimensional data at high velocity. These data streams offer valuable information about the industrial system’s real-time performance. If a shift occurs in the manufacturing process, fault diagnosis based on the data streams becomes a fundamental task as it identifies the affected data streams and provides insights into the root cause. Existing fault diagnostic methods either ignore the correlation between different streams or fail to determine the shift directions. In this paper, we propose a directional fault classification procedure that incorporates the between-stream correlations. We suggest a three-state hidden Markov model that captures the correlation structure and enables inference about the shift direction. We show that our procedure is optimal in the sense that it minimizes the expected number of false discoveries while controlling the proportion of missed signals at a desired level. We also propose a deconvolution-expectation-maximization (DEM) algorithm for estimating the model parameters and establish the asymptotic optimality for the data-driven version of our procedure. Numerical comparisons with an existing approach and an application to a semiconductor production study show that the proposed procedure works well in practice.

Evolution of the Human Role in Manufacturing Systems: On the Route from Digitalization and Cybernation to Cognitization

Modern society is living at a time of revolutionary changes in all areas of human life. For example, the field of industrial manufacturing has greatly influenced the role of human beings during the past 30 years. Modern manufacturing systems are in a phase of transition, in accordance with the concept of the fourth industrial revolution (Industry 4.0). A new manufacturing paradigm based on the principles of Industry 4.0 is presented by Smart Manufacturing Systems (SMS). A basic building block of SMS is cyber-physical production systems (CPPS), which together with innovative-management principles of emergence, self-organization, learning, open innovation, collaboration and the networking of people and organizations are the key principles of Industry 4.0. The three key enablers of Industry 4.0, i.e., the connectivity, the digitization and the cybernation of work processes in manufacturing systems, have paved the way for a new industrial revolution, i.e., Industry 5.0 concept that is bringing about a new paradigm in the field of manufacturing systems, the so-called Adaptive Cognitive Manufacturing Systems (ACMS). A fundamental building block of ACMS is the new generation of manufacturing systems called Cognitive Cyber-Physical Production Systems (C-CPPS), which are based on CPPS concepts and incorporate cognitive technologies and artificial intelligence. This paper presents the revolutionary development of manufacturing and manufacturing systems through the industrial revolutions and the evolution of the role of humans in manufacturing systems towards Industry 5.0.

A review on learning to solve combinatorial optimisation problems in manufacturing

AbstractAn efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state‐of‐the‐art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges.

A Distributed Blocking Flowshop Scheduling with Setup Times Using Multi-Factory Collaboration Iterated Greedy Algorithm

As multi-factory production models are more widespread in modern manufacturing systems, a distributed blocking flowshop scheduling problem (DBFSP) is studied in which no buffer between adjacent machines and setup time constraints are considered. To address the above problem, a mixed integer linear programming (MILP) model is first constructed, and its correctness is verified. Then, an iterated greedy-algorithm-blending multi-factory collaboration mechanism (mIG) is presented to optimize the makespan criterion. In the mIG algorithm, a rapid evaluation method is designed to reduce the time complexity, and two different iterative processes are selected by a certain probability. In addition, collaborative interactions between cross-factory and inner-factory are considered to further improve the exploitation and exploration of mIG. Finally, the 270 tests showed that the average makespan and RPI values of mIG are 1.93% and 78.35% better than the five comparison algorithms on average, respectively. Therefore, mIG is more suitable to solve the studied DBFSP_SDST.

Simultaneous predictive maintenance and inventory policy in a continuously monitoring system using simulation optimization

Predictive maintenance (PdM) is a strategy which can help determine the appropriate timing for maintenance depending on the actual operational conditions of a manufacturing system. Many relevant studies have ignored the effects of resource dependency and only considered perfect maintenance to reduce the complexity and uncertainty of maintenance problems. In this study, we develop a novel and highly practical maintenance model to fill the gap. The proposed model simultaneously considers the predictive maintenance and inventory policies of spare parts. In light of the high degree of complexity in modern manufacturing systems and the profound stochasticity in component degradation, a metamodeling-based simulation optimization method is proposed to find the optimal component inventory policy and degradation level thresholds of each component. A numerical study is conducted to verify that the proposed model and solution method can find the optimal or nearly optimal maintenance cost effectively and efficiently. Furthermore, the impact of critical factors in the maintenance model on the optimal policy is analyzed and useful managerial insights are derived.

Realisation of Design Methodologies and Tools in Modern Manufacturing Systems

Engineering design is the basis for technological advancement and the development of new products. Many changes have emerged over the last few decades due to scientific advancements, evolution in novel engineering materials, advances in computational technology, the opening up of the global village, economic constraints, short life spans of products, and the need for a responsive life cycle design paradigm in engineering products. To meet customer or operational requirements, a certain design methodology is used to design and develop products. This has resulted in notable growth in product design methodologies across disciplines, particularly architecture, engineering, and industrial designs. The engineering design methodology has addressed design procedures, epistemological issues, and working procedures in the design disciplines for several decades. In this article, the research seeks to delve into novel design methodologies currently in use and interrogate their limitations and advantages to create synergies among the best practices in engineering design approaches, with particular emphasis on responsiveness to continuously changing customer requirements. To achieve this, special emphasis will be placed on a design methodology for life cycle, end-of-life, concurrent engineering, eco-design, and reconfigurable design. Based on a strategic focus, the drive is for innovative engineering design that requires design approaches that respond to continuous product changes. The results from this study demonstrate the relationships between engineering design methodologies and solution models that respond to industry and customer requirements.

Training a Machine Learning Model for representing Manufacturing Systems towards optimizing Resilience

Studies affecting manufacturing system resilience have been of rising interest in recent years due to increased rates of naturally caused, and socio-economic disturbances. The non-linearity in modern manufacturing systems, attributed to a host of internal and external complexities in structure and operations cannot be properly modeled using conventional white-box techniques. This paper focuses on building a black-box description of a manufacturing system as a solution, addressing the inherent non-linearity, and further supporting fast investigations on a larger solution space with the example of an application scenario. This work acts as a precursor towards the implementation of a conceptual workflow describing a cross-domain application of machine learning (ML) for optimizing manufacturing system resilience.

Performance Evaluation of ML-Based Algorithm and Taguchi Algorithm of the Hardness Value of the Friction Stir Welded AA6262 Joints at a Nugget Joint

Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min), and the plane depth (mm) are used as controllable parameters and optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools. Analysis of variance was also conducted at a 95% confidence interval for identifying the significant parameters. The result indicated that the coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained while Random Forest and XG Boost algorithm imparted 0.62 and 0.65 respectively. Keywords: Friction Stir Welding; Taguchi; Machine Learning; Hardness; Nugget Zone and Random Forest.

Evolutionary Algorithm-based Optimal Parametrization of Multi-objective Mixed-integer Linear Programming Scheduling Models

Modern manufacturing systems strive to optimize many different key performance indicators. Additionally, mathematical programming-based scheduling models enable the explicit considerations of constraints. However, due to the ever-increasing demand for customized products it is not guaranteed that all constraints can be met at the same time. An approach to circumvent this problem of infeasibility is to replace the constraints by soft-constraints, i.e., to penalize their violation in the objective function. The presence of multiple competing objectives and soft-constraints gives rise to multi-objective optimization problems, where a decision maker has to weigh and balance the different objectives and soft-constraints according to his/her preferences and priorities. Ideally the values of all objectives and soft-constraints should lie within the same order of magnitude, making it easy to weigh them against each other. However, for heterogeneous objectives and soft-constraints with different scales and units this might be challenging. This paper presents an evolutionary algorithm for the optimal parametrization of multi-objective mixed-integer linear programming-based scheduling models. The goal of the evolutionary algorithm is to compute weights for the different terms of the objective which lead to a balanced influence on the overall objective at the optimum. Furthermore, the algorithm is extended by introducing an a priori weighing of the objectives in the fitness function of the evolutionary algorithm. The method is demonstrated on a scheduling problem in which a given set of orders has to be allocated to machines within a manufacturing environment.

AUTOMATED MANUFACTURING SYSTEMS, MODELLED BY MEANS OF COLOURED PETRI NETS WITH COMPLEX COLOURS AND FUNCTIONS

The automated manufacturing systems are frequent within the modern manufacturing systems. This paper shows how the coloured Petri nets are used to assess a robotic manufacturing system, considering several versions of it. The system is made of three machines, and the workpieces are moved through the system by means of a conveyer-type transferring device. Three typed of workpieces are manufactured in this system. the modelling and simulation are made by means of coloured Petri nets. Within the first version, each workpiece type is manufactured on one machine. The loading and evacuation of the workpieces is made by means of an industrial robot for each machine. Within another version, a gantry-type robot serves all the three machines. Also, this paper analyses the case when the manufacturing of one workpiece is made on two machines. These versions were modelled by means of complex coloured Petri nets. The simulations were meant to point out any bottlenecks that may occur within the operation of the system. Using the time factor allowed to assess the system versions also for efficiency. Finally, the paper confirms that modelling and simulation with coloured Petri nets are very useful to assess the performances of the robot manufacturing systems.

The evolution of operators’ role in production: how Lean Manufacturing and Industry 4.0 affect Job Enlargement and Job Enrichment

ABSTRACT In modern manufacturing systems, operators’ role is evolving continuously in relation to the main production paradigms affecting how companies approach their manufacturing and logistics operations. If the introduction of Lean Manufacturing principles has affected workers’ well-being directly, the adoption of digital technologies is modifying how factory work is organised and performed. As a result, digitalised and Lean Manufacturing contexts require operators to enhance their skills increasingly to perform several tasks and functions. This paper examines the evolution of operators’ role in production by recalling two main work design paradigms that originated in the 1960s: Job Enlargement and Job Enrichment. Indeed, through a literature review and development of a causal loop diagram (CLD), this research points out how the main features of Industry 4.0 and Lean Manufacturing jointly affect the Job Enlargement and Job Enrichment concepts. The study significantly helps reshape work design in next-generation production systems, shedding light on the implications and relationships of the most widespread manufacturing paradigm, i.e. Lean Manufacturing and Industry 4.0, using Job Enrichment and Job Enlargement strategies. A CLD also can be a helpful tool for practitioners to understand what factors to leverage to increase efficiency and worker satisfaction.

Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

<span>Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot's trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot's trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot's movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.</span>

DETERMINATION OF COOLANT OPTIMUM OPERATING TEMPERATURE IN INJECTION PRINTING PROCESS BASED ON PRODUCT PRECISION LEVEL

Currently, plastic is more dominantly used because it is cheap, elastic and easy to process. To maintain the quality of plastic products, especially the type of Polyethylene Terephthalate (PET), the researchers implemented a modern plastic manufacturing system, namely using an injection molding system. In order to be able to meet the demands in solving problems that may arise in the process of making plastic products such as: quality, manufacturing time, and safety systems. The injection molding method replaces the old method, namely the hand press machine. This selection is based on advantages such as the consistency of the product produced because it is programmed, equipped with a control system, shorter product time, adequate security system, and other advantages. In this research, an experiment will be conducted to determine the optimum cooling temperature in the form of Pet Gallon. The problems that often occur are product defects due to shrinkage. In connection with this, the test results are obtained as follows: there is a difference in the shrinkage of product dimensions in the experiment using Cooled Chiller and Tower Chiller cooling, cooling has the greatest influence on the processing time of product manufacture, the optimum cooling temperature occurs at a cooling temperature of 200C.

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.