Visual Inspection Improvement of Engine Components Using Deep Learning with Pre-processed Dataset Augmentation: Case Study

Purpose Although research on sustainable manufacturing practices (SMPs) is expanding, few studies adopt a lifecycle-wide lens that integrates product design, manufacturing processes, supply chain management and end-of-life practices. This study aims to address that gap by examining how technological, organisational and environmental (TOE) factors influence SMP adoption and how distinct SMP dimensions affect environmental and economic performance within Ghana’s food manufacturing small and medium-sized enterprise (SME). Design/methodology/approach Survey data from 177 managers in Ghana’s SME-dominated food manufacturing sector were analysed using partial least squares structural equation modelling. Findings Organisational and environmental factors significantly drive SMP adoption, while technological factors show limited influence. Among the SMP dimensions, sustainable product design (SPDesign) and end-of-life management (SPEOL) strongly enhance performance, whereas sustainable manufacturing (SPManuf) and sustainable supply chain management (SSCM) have weaker effects. The findings suggest that internal capabilities and external pressures shape SME sustainability agendas, influencing practices across supply chain tiers. Originality/value This study integrates the TOE framework with the natural resource-based view to offer a comprehensive understanding of SMP adoption in resource-constrained settings. It extends the sustainable manufacturing literature by contextualising SMPs within developing economies and showing how lifecycle-wide sustainability outcomes are shaped by firm-level capabilities, upstream suppliers and downstream stakeholders. While the analysis is firm-centric, the findings reveal multi-tier dynamics and networked sustainability strategies.

The improvement of the possibility of sustainability demonstrates an emotional development in worldwide reasoning that powers organizations to reevaluate the manner in which they work. To upgrade financial turn of events, organizations need to reinitiate and receive more coordinated manageable practices to reestablish their organization technique. Sustainable manufacturing practices (SMP), as considered as intra-and Inter-authoritative performs that coordinate natural, monetary and communal components in functional and commercial activities, will work on corporate execution, earlier investigations accept. This article is pointed toward dissecting that how impact SMP affects economic sustainability (EST) and interceded that how SMP affects EST over innovation performance (IPR). This article investigations exactly the connection between SMP, IPR (item, measure, sorting out and promoting) and EST utilizing review information acquired from 140 fabricators from Pakistan. The investigation uncovered that the inward SMP well affects EST and interaction advancement intercedes incompletely this interior SMP-EST relationship. The exploration embraced the PLS-SEM approach. Shockingly, despite the fact that here is not any substantial association among external SMP and EST, the significance of the relationship has modified with the consolidation into this connection of item and interaction advancements. The investigations have commonly exhibited the significance of SMP and IPR in affecting financial execution tentatively. It is subsequently prompted that organizations ought to expressly recognize the financial potential accomplished from being socially mindful or naturally agreeable, instead of depending on benevolent feelings or responding to outside pressures.

PurposeAs sustainability becomes more important in manufacturing, researchers recommend using the four-stage Hayes and Wheelwright (H-W) model of strategic manufacturing effectiveness (SME) to integrate sustainable manufacturing practices (SMPs) at a strategic level. However, there is limited research on this topic. This paper investigates SMPs encompassing four sustainable manufacturing capabilities (SMCs): pollution control, pollution prevention, product stewardship, and clean technology. It relates these SMCs to the four SME stages of the H-W model, both of which form a continuum of stages.Design/methodology/approachA theoretical model on the congruence between SMCs and SME stages is first established using organizational theories to identify the dominant combinations. This model is then tested by examining 178 SMPs of four large manufacturing firms.FindingsThe study reveals that the SMPs of the case firms clearly show SMC and SME stage characteristics. Few deviations from the relationships established in the theoretical model are observed, leading to a revision of the model. A major finding is that SMPs within an SMC category can span multiple SME stages.Research limitations/implicationsThe study proposes a revised model based on a small sample of case firms, which may limit its broader applicability.Practical implicationsManufacturing practitioners can use the findings of this study to plan SMPs that align with their SME goals.Originality/valueTowards incorporating sustainability in the H-W model, this is the first major exploratory study that establishes congruent relationship between SMCs and SME stages of the H-W model.

With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services.

Today’s society is becoming aware that a new economic model of production and consumption must take into account its environmental and social impact. Industries are under increasing pressure from stakeholders to be transparent in reporting the environmental and social impacts of their operations. In this context, sustainable manufacturing must minimize negative environmental impacts and consumption of energy and natural resources, while also being socially responsible and economically viable. That is why the sustainable manufacturing concept is gaining increasing attention both in the research community and in organizations, especially in the industrial sector. However, even today, there is a great diversity of interpretations and ideas associated with this term. Accordingly, this article first presents an overview of the main concepts related to sustainable manufacturing, and metrics to evaluate organizations’ sustainability performance, and then an outlook of current trends. Our work highlights the consistencies and inconsistencies in the research community related to the interpretations of sustainable manufacturing and Industry 4.0, as well as the lack of consensus about the true social impact of Industry 4.0. However, the positive ecological and economic impacts of sustainable manufacturing seem fairly widespread. In this way, sustainable manufacturing practices seem to be reinforced by initiatives within the fourth stage of industrialization – the so-called Industry 4.0 – which offers great opportunities for sustainable manufacturing, thanks to digital transformation.

With the increasing energy consumption in the mining industry, the effective prediction and optimization of energy consumption in mining equipment have become pressing challenges. Traditional energy consumption prediction methods suffer from data processing delays and the fixed nature of monitoring devices, making them inadequate for meeting the real-time and flexible demands of modern mining operations. The advent of mobile computing platforms has introduced new possibilities for the dynamic prediction and optimization of energy consumption in mining equipment. In recent years, energy consumption prediction techniques based on mobile computing platforms have gained significant attention, enabling realtime data acquisition and analysis for a more precise understanding of energy consumption patterns and the implementation of efficient optimization strategies. However, existing studies predominantly focus on conventional models and methodologies, lacking effective mechanisms to capture spatiotemporal dynamics and optimize energy consumption accordingly. In this study, a spatiotemporal gated graph convolutional prediction model was proposed for the dynamic prediction of energy consumption in mining equipment based on a mobile computing platform. Additionally, an energy consumption optimization strategy was explored using the prediction results. This study provides a novel approach to energy consumption optimization in mining equipment, offering both theoretical significance and practical value.

BackgroundThis study evaluates the conformity of using a computer vision-based posture analysis system as a screening assessment for postural deformity detection in the spine that is easily applicable to clinical practice.MethodsOne hundred forty participants were enrolled for screening of the postural deformation. Factors that determine the presence or absence of spinal deformation, such as shoulder height difference (SHD), pelvic height difference (PHD), and leg length mismatch (LLD), were used as parameters for the clinical decision support system (CDSS) using a commercial computer vision-based posture analysis system. For conformity analysis, the probability of postural deformation provided by CDSS, the Cobb angle, the PHD, and the SHD was compared and analyzed between the system and radiographic parameters. A principal component analysis (PCA) of the CDSS and correlation analysis were conducted.ResultsThe Cobb angles of the 140 participants ranged from 0° to 61°, with an average of 6.16° ± 8.50°. The postural deformation of CDSS showed 94% conformity correlated with radiographic assessment. The conformity assessment results were more accurate in the participants of postural deformation with normal (0–9°) and mild (10–25°) ranges of scoliosis. The referenced SHD and the SHD of the CDSS showed statistical significance (p < 0.001) on a paired t-test. SHD and PHD for PCA were the predominant factors (PC1 SHD for 79.97%, PC2 PHD for 19.86%).ConclusionThe CDSS showed 94% conformity for the screening of postural spinal deformity. The main factors determining diagnostic suitability were two main variables: SHD and PHD. In conclusion, a computer vision-based posture analysis system can be utilized as a safe, efficient, and convenient CDSS for early diagnosis of spinal posture deformation, including scoliosis.

Nowadays, business practices are moving towards sustainabilitySustainability, which is necessary for organisational growth and environmental well-being. However, the world is becoming more VUCA making the business environment more volatile and uncertain. Moreover, complex decision-making and the human illusion of understanding of sustainability lead to ambiguous decision-making. Therefore, the present chapter explores the role of Industry 4.0Fourth industrial revolution (IE4.0) and Big DataBig Data (BD) in Sustainable Manufacturing (SM) to understand the need for process visibility and enhance the decision-making for SM practices. To achieve this, the present chapter reviews scholarly work related to IE4.0, BD, and SM. From, the extant review unique constructs namely SM in the VUCA world, Impact of Industry 4.0 in SM, and Big Data Architecture were identified. The review will serve as reference material for practitioners and academicians in understanding the SM practices in the VUCA world.

This paper seeks to establish the impact of university experiential learning programs on small- to medium-sized enterprises while emphasizing the benefit to rural workforce development and sustainable manufacturing practices. Data were collected from diverse partners of Louisiana State University’s experiential learning program over the last 7 years to illustrate the types of recommendations and implementation statistics for sustainable manufacturing practices. The study found that rural enterprises favored the adoption of short-term, high-saving solutions to mitigate the impact of utility costs resulting from geographical isolation, while there was low implementation of long-term, large investment projects. This highlighted the practical feasibility of a project over a focus on long-term sustainability plans, which require significant capital investment, management planning, and employee training. This study outlines a university-led experiential learning program’s engagement through academic–industrial partnerships that serve student development and the economic advancement of small- to medium-sized enterprises. The data can direct future incentive opportunities for sustainability projects that have more immediate payback, to increase the adoption rate in rural facilities. The larger implication provides a framework and validation that can support the development of similar programs for extension and enterprise engagement to impact sustainable manufacturing practices.

The level of innovative activities in Malaysia is still low compared to that of other nations, which is attributed to the scarcity of green innovation capability (GIC) and the minimal acquisition of new technological knowledge. Studies revealed that Malaysian manufacturing small and medium enterprises (SMEs) encounter the challenges of operation and supply chain disruption and tight cash flows. These firms would not want to venture into green equipment and eco technologies if there is no certainty of promising returns. Overall, studies on sustainable management systems in the Malaysian manufacturing SMEs have revealed that firms are still in the early stages of implementation, such that sustainable manufacturing practices (SMPs) are predominantly production- and process-oriented, neglecting the importance of the product life cycle and sustainable end-of-life management. The aim of this paper is: (1) To examine the effect of GIC on firm sustainability performance (SP) and (2) To investigate the mediating effect of SMPs on relationship between GIC and firm SP. Smart PLS 3.0 was applied to analyze the data collected among 150 ISO 14001 certified local SMEs manufacturers. The study demonstrated the non-significant result between GIC and SP; while SMPs mediated the relationship between GIC and SP. This study enriches the Dynamic Capability View (DCV) by extending the sustainability model to better explain the sustainable development process in the context of Malaysian manufacturing SMEs. This article does not just inform firms on the importance of SMPs for performance but also guides firm managers towards understanding and improving their current SMPs. These results also help Malaysian policy makers in providing appropriate assistance to improve current firm operations, and subsequently, sustainability-related outcomes. Following this, various kinds of assistance (e.g., strategic planning, governance, facility, financing, and technology) can be provided to support SMEs manufacturers.

This paper proposes a framework to enable a proactive approach to energy consumption prediction and optimisation that can be utilised from the design phase thus offering significant advantages over existing reactive approaches. The framework integrates energy modelling and management techniques at component and system levels using the modelling capabilities of virtual engineering tools. For proof of concept demonstration, a case study of a pick-and-place automatic workstation is presented. An energy consumption optimisation is achieved by optimising components motion control and reconfiguring the workstation. In the case study, a number of experiments were conducted to compare alternative designs against the original one. Results showed significant energy saving (up to 57% of the original design) by redefining the motion profiles and cycle time in a more optimised form.

In the present conditions, Industry 4.0-enabled sustainable manufacturing practices can affect whole business system by methods for changing the techniques the things are organized, made, passed on and discarded. Industry 4.0 is reasonably novel to making nations, especially in India and necessities an obvious definition for fitting understanding and practice in business. This paper intends to analyse the key challenges to Industry 4.0-enabled sustainable manufacturing. Decision-making trail & evaluation laboratory (DEMATEL) Process is used to analyse challenges of Industry 4.0-enabled sustainable manufacturing. This work is exceptionally valuable for experts, approach producers, administrative bodies and directors to build up an inside and out comprehension of Industry 4.0 activities and destroy the potential difficulties in receiving Industry 4.0 activities for sustainability.

Main challenges involved in the adoption of sustainable manufacturing in Uruguayan small and medium sized companies

Analysis and optimization of process energy consumption and environmental impact in electrical discharge machining of titanium superalloys

Nowadays, sustainability practices in manufacturing are required for the business environment, especially for SMEs. In particular, technology capabilities are essential to enhance firms’ capacity. This paper explores the relationship between technology infrastructure, manufacturing technology and sustainable manufacturing practice (SMP) in the context of SMEs. The data were collected through questionnaires using purposive sampling in Thailand, with completed 237 surveys being returned. We used structural equation modeling to test the research framework. The results reveal that the implementation of SMP in SMEs is still at a medium level, thus demonstrating that there is still substantial room for enhancement. Moreover, the findings show that technology infrastructure has an impact on SMP, while manufacturing technology also significantly mediates the relationship between technology infrastructure and SMP. These findings imply that SMEs in the manufacturing industry can develop technology capabilities as essential resources to gain higher SMP for sustainability in the competitive business environment and to accomplish the Sustainable Development Goals (SDGs).