Next-generation Manufacturing Systems Research Articles

Fully integrated downstream process to enable next-generation manufacturing.

Next-generation manufacturing (NGM) has evolved over the past decade to a point where large biopharmaceutical organizations are making large investments in the technology and considering implementation in clinical and commercial processes. There are many well-considered reasons to implement NGM. For the most part, organizations will not fund NGM unless the implementation benefits the funding organization by providing reduced costs, reduced time, or additional needed capabilities. Productivity improvements gained from continuous purification are shown in this work, which used a new system that fully integrates and automates several downstream unit operations of a biopharmaceutical process to provide flexibility and easy implementation of NGM. The equipment and automation needed to support NGM can be complicated and expensive. Biopharmaceutical Process Development considered two options as follows: (1) design its own NGM system or (2) buy a prebuilt system. PAK BioSolutions offers a turn-key automated and integrated system that can operate up to four continuous purification stages simultaneously, while maintaining a small footprint in the manufacturing plant. The system provides significant cost benefits (~10× lower) compared with the alternative-integration of many different pieces of equipment through a Distributed Control System that would require significant engineering time for design, automation, and integration. Integrated and Continuous Biomanufacturing can lead to significant reductions in facility size, reduced manufacturing costs, and enhanced product quality when compared with the traditional batch mode of operation. The system uses new automation strategies that robustly link unit operations. We present the optimized process fit, sterility and bioburden control strategy, and automation features (such as pH feedback control and in-line detergent addition), which enabled continuous operation of a 14-day end-to-end monoclonal antibody purification process at the clinical manufacturing scale.

Sustainable Human–Machine Collaborations in Digital Transformation Technologies Adoption: A Comparative Case Study of Japan and Germany

The Digital Transformation (DX) megatrend is fundamentally disrupting and changing the nature of work, business, and industry at a rapid pace. Although the notion of DX has garnered much research interest from practitioners, scholarship on this topic is somehow lagging behind, possibly because of the lack of theoretical frameworks on DX. Recently, most Japanese firms have begun to use diverse digital technologies to sustain their competitive advantages. However, the return of investment on digital technologies has not been as high as expected for some firms. Furthermore, as the visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators, it is necessary to design resilient human–machine collaborations within factories. To this end, this paper presents a research model between DX technologies and scientific problem-solving in terms of deduction, induction, and abduction inference structures as an approach to resilient human–machine collaborations. The purpose of this research is to analyze the difference in the utilization pattern of the digital technology of American, German, and Japanese firms based on three types of decision-making methods. Next, we apply this framework in a comparative case study of two Japanese firms and one German firm, where we find that there is a difference in DX technologies utilization among the Japanese and German firms. We assert that the utilization of IoT technology in the United States and Germany is pursuing IoT with the aim of autonomous control, whereas Japanese firms prioritize robot–human collaboration. Finally, we discuss how our findings contribute to the burgeoning field of resilient human–machine collaborations by showing the distinct roles of deduction, induction, and abduction inference structures. Furthermore, our research contributes to international comparative studies to identify the difference in national IT utilization. Lessons and implications are discussed.

Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System

Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes.

One-of-a-kind production (OKP) planning and control: a comprehensive review and future research directions

PurposeThe aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.Design/methodology/approachThe following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.FindingsThe results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.Originality/valueThis paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge

Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, we analyze this design challenge, and we propose basing the design on the joint cognitive systems approach. The established joint cognitive systems approach can be complemented with approaches that support human centricity in the early phases of design, as well as in the development of continuously co-evolving human–machine teams. We propose approaches to observing and analyzing the collaboration in human–machine teams, developing the concept of operations with relevant stakeholders, and including ethical aspects in the design and development. We base our work on the joint cognitive systems approach and propose complementary approaches and methods, namely: actor–network theory, the concept of operations and ethically aware design. We identify their possibilities and challenges in designing and developing smooth human–machine teams for Industry 5.0 manufacturing systems.

A review of G code, STEP, STEP-NC, and open architecture control technologies based embedded CNC systems

Computer Numeric Control (CNC) is a significant pillar of manufacturing for decades. These systems are an essential part of future manufacturing. In the urge towards the development of next-generation manufacturing systems, various technologies have been introduced. Moreover, based on those technologies, numerous systems have been developed all around the world. In order to assist in future research, the related major technologies and developments are discussed in this content. This paper presents the review of basics CN), Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), Computer-Aided Manufacturing (CAM), G codes (ISO 6983), Standard for the Exchange of Product (STEP) model data (STEP), STEP-numeric control (STEP-NC), and open architecture control (OAC) technologies. The in-depth discussion on the development and implementation of the STEP and STEP-NC technologies is also addressed. The review also highlights the benefits and role of the open architecture control technology in the CNC system development. Apart from that, the paper also presents the last 17 years of embedded CNC systems development in tabular form, which shows the cocktail of various technologies and ISO data interface models. Finally, the paper concludes with future directions and recommendations.

Digital twin-based sustainable intelligent manufacturing: a review

As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

Toward a Real-Time Reconfiguration of Self-Adaptive Smart Assembly Systems

In the current industrial context, signed by the advent of Industry 4.0, traditional manufacturing systems are evolving towards the new class of Next Generation Manufacturing Systems (NGMSs), characterized by the main attributes of changeability, reconfigurability and self-adaptability. Such advanced systems are able to cope with the new industrial and market challenges as the dynamic market demand, short products life cycles and the need for flexibility. This paper presents the design and control of an innovative prototype of Self-adaptive Smart Assembly System (SASAS) able to real-time reconfigure its structure according to both product and operator features. An easy-to-use graphic user interface (GUI), developed in Matlab environment, supports the human operators in the real-time system reconfiguration and self-adaptability by storing information about the work cycles of the products manufactured by the case company and associating each operation of the product work cycle to a specific movement of the system. Finally, the proposed GUI allows the operators to quickly add new products work cycles in response to the customers’ request for new product variants, matching the Industry 4.0 principles.

Real-Time Monitoring System to Lean Manufacturing

Future of the manufacturing industry is the combination of lean and cloud manufacturing which is a promising paradigm for next-generation manufacturing system. Lean practice is one of the best methods used by the manufactures around the world to enhance their competitiveness. It is believed that lean practice will result in reducing waste and control the essential processes related to the production. Even though lean manufacturing system is very reliable and trustworthy concept in large scale industries but still there are some flaws and challenges faced by SMEs industries to make it run successfully. To overcome these challenges and problems, we will rely on the cloud manufacturing concept while applying lean manufacturing methodology. Cloud platform will have a potential to collect the real-time production monitoring and will have a very close look at the inventory stock so that next order for raw material could be placed whenever it is required. With the combination of lean and cloud manufacturing, the new approach will provide us with a very flexible approach towards the production and will keep monitoring the scheduling the dispatching of the finished product as per the market requirement. In this paper, discuss a case study based on the cloud manufacturing and lean manufacturing system together to see how it will benefit and can be used to enhance productively in SMEs industry. With this combination of cloud and lean manufacturing and with the help of electronics system in it a partial automation system was established.

A structural equation model and a statistical investigation of lean-based quality and productivity improvement

In response to rapid expansion rate of technological change and information technology, and increasing customer demands, Next Generation Manufacturing Systems (NGMS) and Fit Production Systems (FPS) are emerged. Lean Production (LP) covers a wide range of production concepts and philosophies that can be classified as NGMS and/or FPS. In this paper, LP is introduced, defined, and reviewed, important aspects when implementing such production strategy is addressed, and practices of LP are mapped into different impact areas of production, among these "Quality and Productivity Improvement (QPI)" impact area. QPI may involve eight lean practices these are: Variability Reduction (VR), Visual Control (VC), Poka Yoke (PY), Quality at the Source (QS), Kaizen (K), Five S (5S), Root Cause Analysis (RCA), and Total Quality Management (TQM). The purpose of this article is to provide bottomless insights into the adoption of these eight constructs and their effect on lean based QPI model. A Structural Equation Model (SME) is constructed. Hypotheses are proposed, Five-Points-Likert scale questionnaire is distributed by hand to 450 different firms. Feedback from 300 firms is collected then statistically analyzed using SPSS 15.0, AMOS 19.0.0, and LISREL 8.8 software. Implementation level (I %) of the eight lean practices in the selected sample is evaluated, multicollinearity, reliability and internal correlations matrix to insure content validity, consistency, and construct validity respectively are applied, fit indices are derived, one way ANOVA analysis is performed, and finally hypothesized causal relationships between model constructs are concluded, the results provide strong evidence that all the considered lean practices has a positive effect on lean-based QPI model, these findings can be fruitfully utilized to cope over the challenges facing the implementations of LP production strategy in industry.

Analysis of interactions of criteria for mitigating negative social impacts of next generation manufacturing systems

Adoption of next generation manufacturing systems (NGMS) is the utmost requirement to stay and remain competitive in business. However, there are many social impacts associated with these manufacturing systems. The emphasis of this paper has been to identify critical social impact variables (SIVs) of the NGMS, understand the interactions among them and then develop a framework to mitigate their negative impacts. Interpretive structural modelling (ISM) approach has been used to establish the relationship and order among different variables. Also, this research classifies the relationship among various SIVs in NGMS implementation according to their driving power and dependence. The dependent, independent, autonomous and linkage variables have been categorised using MIMAC analysis. This proposed ISM model and MIMAC analysis provide a platform for manufacturing firms to develop suitable strategies to mitigate their negative impacts.

A Web-Based Integration Procedure for the Development of Reconfigurable Robotic Work-Cells

Concepts related to the development of reconfigurable manufacturing systems (RMS) and methodologies to provide the best practices in the processing industry and factory automation, such as system integration and web-based technology, are major issues in designing next-generation manufacturing systems (NGMS). Adaptable and integrable devices are crucial for the success of NGMS. In robotic cells the integration of manufacturing components is essential to accelerate system adaptability. Sensors, control architectures and communication technologies have contributed to achieving further agility in reconfigurable factories. In this work a web-based robotic cell integration procedure is proposed to aid the identification of reconfigurable issues and requirements. This methodology is applied to an industrial robot manipulator to enhance system flexibility towards the development of a reconfigurable robotic platform.

Discovering autonomous structures within complex networks of work systems

Modern theories propose autonomous structures as building blocks of next-generation manufacturing systems. However, their size and scope are not agreed upon and remain a subject of research. The paper presents a method for discovering autonomous structures within existing manufacturing systems. Firstly, it is shown how a complex network model of a manufacturing system can be obtained. Then, a method for discovering structure in complex networks is applied in order to find cohesive subnetworks – candidates for the formation of autonomous work systems. The approach is illustrated in a case study of engineer-to-order production.

Revisiting System Paradigms from the Viewpoint of Manufacturing Sustainability

A system paradigm is an abstract representation of system; it includes system architecture used to determine the types and numbers of components and their relations in the system. The design of system paradigm relies on customers’ requirements and the characteristics of the manufacturing environment. Many system paradigms and design guidelines have been proposed for a variety of customers’ needs including functions, cost, quality, personalization, and lead time of products. However, the consideration of sustainability becomes essential to today’s manufacturing systems; a new challenge is how to evolve existing paradigms to accommodate the requirements of sustainability. In contrast to ample research activities on system paradigms in past decades, recent studies on system paradigms have been restricted, partially due to unclear research directions. Limited works can be found on conceiving new manufacturing system paradigms from the perspective of sustainability; most of the related literature concerns the new requirements of sustainability. The objectives of this work are (i) to examine the requirements of manufacturing systems in a wider scope; (ii) to revisit existing paradigms to clarify their limitations and bottlenecks; and eventually (iii) to identify some research directions, which will lead to a solution of sustainable manufacturing. To achieve these objectives, firstly, a brief description of today’s manufacturing environment is provided. Secondly, the requirements of sustainability are discussed, and the relevant researches on system sustainability are surveyed. Thirdly, the reconfigurable system paradigm is focused, and the gaps between a reconfigurable manufacturing system and a sustainable manufacturing system are discussed. Finally, the future endeavors towards to the next-generation manufacturing system paradigms are discussed.

Real-time work-in-progress management for smart object-enabled ubiquitous shop-floor environment

Recent developments in wireless technologies have created opportunities for developing next-generation manufacturing systems with real-time traceability, visibility and interoperability in shop-floor planning, execution and control. This article proposes a work-in-progress management framework based on smart objects such as radio frequency identification/Auto-ID devices and web service technologies in a ubiquitous manufacturing (UM) environment. Under this framework, two types of services (data source service and gateway data service) and WIPA (work-in-progress agent) are designed and developed to manage and control the real-time materials and information flows to improve the optimal planning and control of the entire shop floor. During production execution, real-time visibility explorers are provided for operators and supervisors to reflect the real-time situation of current manufacturing environment. It follows a simple but effective principle: what you see is what you do and what you do is what you see. Production disturbances could thus be detected and fed back to decision makers for implementing closed-loop shop-floor control. In addition, some important standards such as ISA 95 and business-to-manufacturing markup language (B2MML) are adopted to establish the information model and schemas of WIP called wipML (work-in-progress markup language). Based on B2MML and wipML, the real-time manufacturing information can be effectively encapsulated, shared and exchanged between gateways, WIPA and heterogeneous enterprise information systems. The presented framework is studied and demonstrated using a near real-life simplified shop floor that consists of typical manufacturing objects.

Next Generation Manufacturing Systems and the Virtual Enterprise

Next Generation Manufacturing Systems (NGMS) will support the Next Generation Manufacturing Enterprises (NGMEs). Adaptive distributed manufacturing systems seem to be a promising solution for NGMEs to provide increased levels of flexibility, reconfigurability and intelligence to respond to the highly dynamic market demands. Virtual Enterprises (VEs) are expected to be the dominant manufacturing enterprise model in the 21 st Century. VEs promise new possibilities where a manufacturing system is not know in advance and has to be structured, optimized and implement from scratch to produce e.g. one-of-a-kind products. VEs can approach each industrial project with tailored manufacturing systems represented by a temporary integration of processes capabilities and resources capacities from multiple enterprises in order to meet or exceed the quality-, time- and cost- frame requirements of the customer. VE paradigm represents the future of the NGMEs, and can be also used to provide industrial services in a collaborative way to evolve from physical products to the notion of extended products.

Rapid manufacturing facilitated customization

The current paper presents a novel method for the production of body-fitting customized seat profiles utilizing the following digital methods: three-dimensional laser scanning, reverse engineering and rapid manufacturing (RM). Seat profiles were manufactured in order to influence the comfort characteristics of an existing ejector seat manufactured by Martin Baker Aircraft Ltd. The seat, known as the Navy Aircrew Common Ejection Seat (NACES), was originally designed with a generic profile. This paper shows the replacement of this profile with shapes captured from fast jet pilots. Pressure mapping of occupied seats, has shown that the pressure distribution under the buttocks can be influenced using body-fitting design and thus comfort is directly affected. The paper discusses the relevance of RM with respect to mass customization and personalization and, in addition, recognizes RM as a next generation manufacturing system (NGMS) capable of satisfying increasingly diverse products and lower volume production. A generic customization process is reviewed to identify areas of technical difficulty and key issues in the cost-effective customization of products.

SURFACE ROUGHNESS AND CUTTING TOOL-WEAR DIAGNOSIS BASED ON BAYESIAN NETWORKS

Next-generation manufacturing systems demand more intelligent features such as diagnosis, adaptation, etc. One of the main goals in machining systems is to find an appropriate trade-off among cutting tool condition, surface roughness and productivity. However, both surface roughness and cutting tool wear are impractical to measure online. This work proposes a multi-sensor system for indirect monitoring based on Bayesian Networks (BN) framework. Unlike classical modelling techniques, a important advantage of BN models are their ability to deal with the stochastic nature of the machining process. We discuss BN models based on their precision and reliability for different discretization sizes of their continuous variables. Early results show that models with high discretization exhibit a reliability of 89.5% for the surface roughness prediction and 97.3% for the cutting tool wear diagnosis. However, lower discretization presents better reliability but worse diagnosis.

Next-generation manufacturing systems: key research issues in developing and integrating reconfigurable and intelligent machines

Throughout history it has been proved that tendencies in the manufacturing floor are a reflection of the changes in the customer's demands. Today, markets increasingly require more customized products, with shorter life cycles. In response, manufacturing systems have evolved from mass production techniques, through flexible automation, and to mass customization to produce ‘one-of-a-kind production’ at mass production costs. This paper argues that manufacturing systems of the next generation will have to incorporate more flexibility and intelligence, evolving towards reconfigurable manufacturing systems. In particular, the concept of ‘intelligence’ becomes more relevant because of the need to maintain effective and efficient manufacturing operations with minimum downtime under conditions of uncertainty. This paper presents a comprehensive literature review of the field of reconfigurable and intelligent machines. The relevant research issues for the development and introduction of reconfigurable machines tools are presented.

Emergence of intelligence in next-generation manufacturing systems

In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organization, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analyzed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment.