Review of intelligence for additive and subtractive manufacturing: current status and future prospects

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes

Analyzing the factors influencing the wind energy adoption in Bangladesh: A pathway to sustainability for emerging economies

The future of energy security has become a prominent concern for emerging economies due to the inevitable depletion of fossil fuels and the ongoing disruptions in their supply. The crippling effect of complete dependence on expensive fossil fuel imports is magnified by the ineffective policy response to the enduring energy crisis, impeding progress across various sectors and thwarting efforts to meet the demands of population growth and industrialization amid acute electricity shortages. Amidst the economic growth of a prominent emerging economy, Bangladesh, wind energy emerges as a transformative solution to effectively tackle the mounting challenges of electricity demand, environmental pollution, greenhouse gas emissions, and the depleting reserves of fossil fuels. Therefore, this study utilizes an integrated multi-criteria decision-making (MCDM) approach combining the inter-valued type 2 intuitionistic fuzzy (IVT2IF) theory with the decision-making trial and evaluation laboratory (DEMATEL) method aiming to identify, prioritize, and investigate the relationships among the factors that impact the sustainable adoption and growth of wind energy in an emerging economy like Bangladesh. Initially, the factors were derived from reviewing existing literature. After subsequent expert validation, sixteen factors were selected for analysis using the IVT2IF DEMATEL method. The findings of the study indicate that ''Fossil fuel supply disruption,'' ''Stable financial investment and resource mobilization,'' and ''Geographical region'' are the most significant factors influencing the adoption of wind energy for national grid support with prominence value 4.415, 4.406 and 4.339 respectively. Moreover, ''Fossil fuel supply disruption'' is also the most significant causal factor with a causal weight of 1.274, which is followed by ''Stable financial investment and resource mobilization'' and ''Geographical region'' with a causal weight of 1.029 and 0.794. The study's findings have the potential to aid decision-makers and policymakers in formulating long-term strategies and investment decisions to improve the sustainability of the national grid and achieve carbon neutrality

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies.

The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study's findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry