The Key Factors of the Industrial Revolution 4.0 in the Malaysian Smart Manufacturing Context

IR 4.0 is a new phase for the current trend of automation and data exchange in manufacturing industry that focuses on cloud computing, interconnectivity, the Internet of Things, machine learning, cyber physical learning and creating smart factory. The purpose of this article was to unveil the key factors of the IR 4.0 in Malaysian smart manufacturing context. Two key data collection methods were used: (1) primary data from the face-to-face interview (2) secondary data from the previous study. Significantly, five key factors of IR 4.0 consider for this study. Autonomous production lines, smart manufacturing practices, data challenge, process flexibility, and security. As a result, IR 4.0 for quality management practices might get high impact for the best performance assessment, which addressed in various ways; there are few studies in this area have been conducted in Malaysian manufacturing sector, and to recommend the best practices implemented from the managers’ perspectives. For scholars, this enhances their understanding and highlight opportunities for further research.

Service Computing for Industry 4.0: State of the Art, Challenges, and Research Opportunities

Recent advances in the large-scale adoption of information and communication technologies in manufacturing processes, known as Industry 4.0 or Smart Manufacturing, provide us a window into how the manufacturing sector will evolve in the coming decades. As a result of these initiatives, manufacturing firms have started to integrate a series of emerging technologies into their processes that will change the way products are designed, manufactured, and consumed. This article provides a comprehensive review of how service-oriented computing is being employed to develop the required software infrastructure for Industry 4.0 and identifies the major challenges and research opportunities that ensue. Particular attention is paid to the microservices architecture, which is increasingly recognized as offering a promising approach for developing innovative industrial applications. This literature review is based on the current state of the art on service computing for Industry 4.0 as described in a large corpus of recently published research papers, which helped us to identify and explore a series of challenges and opportunities for the development of this emerging technology frontier, with the goal of facilitating its widespread adoption.

Are smart manufacturing systems beneficial for all SMEs? Evidence from Korea

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

The intelligent monitoring and diagnosis of steel defects plays an important role in improving steel quality, production efficiency, and associated smart manufacturing. The application of the bio-inspired algorithms to mechanical engineering problems is of great significance. The split attention network is an improvement of the residual network, and it is an improvement of the visual attention mechanism in the bionic algorithm. In this paper, based on the feature pyramid network and split attention network, the network is improved and optimised in terms of data enhancement, multi-scale feature fusion and network structure optimisation. The DF-ResNeSt50 network model is proposed, which introduces a simple modularized split attention block, which can improve the attention mechanism of cross-feature graph groups. Finally, experimental validation proves that the proposed network model has good performance and application prospects in the intelligent detection of steel defects.

Sustainable Manufacturing of Ultra-Fine Aluminium Alloy 6101 Wires Using Controlled High Levels of Mechanical Strain And Finite Element Modeling

The evolution of grain size and component mechanical behaviour are fundamental aspects to analyse and control when manufacturing processes are considered. In this context, severe plastic deformation (SPD) processes, in which a high shear strain is imposed on the material, are recognized as the main techniques to achieve microstructural changes and material strengthening by the recrystallization, attracting both academic and industrial investigation activities. At the same time, nowadays, sustainable manufacturing design is one of the main responsibilities of the researchers looking at UN2030 agenda and the modern industrial paradigms. In this paper a new severe SPD process is proposed with the aim to steer manufacturing to fourth industrial revolution using some of Industry 4.0 pillars. In particular, additive manufacturing (AM) and numerical simulations were setup as controlling and monitoring techniques in manufacturing process of wires.Strengthening effect (yield and ultimate tensile strength, plasticity and hardness) and microstructural evolution (continuous dynamic recrystallization -CDRX-) due to severe plastic deformation were experimentally analysed and numerically investigated by an innovative finite element (FE) model able to validate the effectiveness of a properly modified process for ultra-fine aluminium alloy AA6101 wires production designed with the aim to avoid any post manufacturing costly thermal treatment.The study provides an accurate experimental study and numerical prediction of the thermo-mechanical and microstructural phenomena that occur during an advanced large plastic deformation process; it shows how the combination of smart manufacturing and simulations control represents the key to renew the traditional manufacturing methods in the perspective of the Industry 4.0, connecting and integrating the manufacturing process for the industrial evolution in production.

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain.

A Methodology for Flexible Implementation of Collaborative Robots in Smart Manufacturing Systems

Small-scale production is relying more and more on personalization and flexibility as an innovation key for success in response to market needs such as diversification of consumer preferences and/or greater regulatory pressure. This can be possible thanks to assembly lines dynamically adaptable to new production requirements, easily reconfigurable and reprogrammable to any change in the production line. In such new automated production lines, where traditional automation is not applicable, human and robot collaboration can be established, giving birth to a kind of industrial craftsmanship. The idea at the base of this work is to take advantage of collaborative robotics by using the robots as other generic industrial tools. To overcome the need of complex programming, identified in the literature as one of the main issues preventing cobot diffusion into industrial environments, the paper proposes an approach for simplifying the programming process while still maintaining high flexibility through a pyramidal parametrized approach exploiting cobot collaborative features. An Interactive Refinement Programming procedure is described and validated through a real test case performed as a pilot in the Building Automation department of ABB in Vittuone (Milan, Italy). The key novel ingredients in this approach are a first translation phase, carried out by engineers of production processes who convert the sequence of assembly operations into a preliminary code built as a sequence of robot operations, followed by an on-line correction carried out by non-expert users who can interact with the machine to define the input parameters to make the robotic code runnable. The users in this second step do not need any competence in programming robotic code. Moreover, from an economic point of view, a standardized way of assessing the convenience of the robotic investment is proposed. Both economic and technical results highlight improvements in comparison to the traditional automation approach, demonstrating the possibility to open new further opportunities for collaborative robots when small/medium batch sizes are involved.