Design Of Manufacturing Systems Research Articles

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

DIGITAL RESTORATIVE PROCEDURES IN DENTISTRY

ABSTRACT The integration of digital technologies in restorative dentistry has significantly transformed clinical workflows, enhancing precision, efficiency, and patient outcomes. This article explores the pivotal role of various digital procedures in modern dental practices. Digital imaging techniques have revolutionized diagnostic capabilities, providing high-resolution, detailed visualizations essential for accurate treatment planning. Digital cone beam computed tomography (CBCT) has further refined diagnostic accuracy, enabling three-dimensional assessment of dental structures, which is crucial for implantology and complex restorative cases. Digital caries detection methods offer enhanced early detection of carious lesions, improving preventative care and treatment outcomes. The advent of digital impression systems has streamlined the process of capturing accurate dental impressions, reducing patient discomfort and enhancing the precision of prosthetic restorations. Additionally, digital design-manufacturing (CAD/CAM) systems have facilitated the rapid production of high-quality dental restorations, allowing for same-day procedures and greater customization. Collectively, these digital advancements are reshaping the landscape of restorative dentistry, offering unprecedented opportunities for improving the accuracy, efficiency, and overall success of dental restorations. This review aims to provide an in-depth understanding of the current state of digital restorative procedures in dentistry, with a focus on the latest technological advancements and their clinical implications. Keywords: Dentistry, digital technology, CAD/CAM, CBCT.

RESILIENT CIRCULARITY IN MANUFACTURING: SYNERGIES BETWEEN CIRCULAR ECONOMY AND RECONFIGURABLE MANUFACTURING

Abstract Reconfigurability in manufacturing signifies a system's capacity to promptly adapt to evolving needs. This adaptability is critical for markets to maintain operations during unexpected disruptions, including weather anomalies, cyber-attacks, and physical obstructions. Concurrently, the concept of a circular economy is gaining popularity in manufacturing to mitigate waste and optimize resource utilization. Circular economy principles aim to reduce environmental impacts while maximizing economic benefits by emphasizing the reuse of goods and resource byproducts. The nexus between reconfigurability and the circular economy stems from their shared pursuit of sustainability and resilience. Interestingly, biological ecosystems also exhibit these traits, showcasing exceptional adaptability to disturbances alongside the ability to effectively utilize available resources during normal operations. This study explores various manufacturing system configurations to assess both their adaptability and connection to circular economy principles. 44 configurations are categorized based on layout (e.g., job shop, flow line, cellular) and analyzed using convertibility, cyclicity, and Degree of System Order metrics. A significant positive correlation (R2 = 0.655) is found between high convertibility and ecologically similar levels of structural cycling, suggesting that effective resource utilization supports adaptability in manufacturing systems. Furthermore, this paper proposes the existence of a possible “window of vitality” for cyclicity, as it demonstrates a significant correlation (R2 = 0.855) between the Degree of System Order and cyclicity. Identifying systems that strike a balance between redundancy, efficiency, convertibility, and cyclicity can aid manufacturing system designers and decision-makers in making choices that address increasing requirements for both sustainability and resilience.

A mixed-integer programming formulation for optimizing the double row layout problem

The Double Row Layout Problem (DRLP) asks for an arrangement of machines on both sides of a straight line corridor so as to minimize the total cost for transferring materials among machines. The DRLP is NP-Hard and has practical relevance, specially in manufacturing systems design. In this paper, we drastically reduce the time required to solve the problem by constructing a new and effective mixed-integer linear programming (MILP) model of the DRLP. The new model was obtained by reformulating an existing MILP model. This includes tightening some constraints, introducing new variables, implementing constraints to link the new and original variables; and adding valid inequalities and a valid system of equations. To reduce the size of the reformulated model, we eliminate several of the new introduced variables by a substitution using the system of equations. The computational results demonstrate that the proposed model requires considerably smaller computational times compared to the ones in the literature. As a consequence, optimal solutions can now be efficiently found for larger instances of the problem. Previous studies have been able to optimally solve, within reasonable time, instances with size up to 16 machines, while with the new model four instances with 20 machines could be optimally solved.

Utilization of Immersive Virtual Reality as an Interactive Method of Assignment Presentation

Virtual reality is a technology with many possible uses and ways to improve various processes, including the presentation of results. This paper deals with the utilization of virtual reality as a tool for assignment presentation. During the classes of manufacturing and assembly systems design, the conventional form of presentation was replaced with immersive virtual reality, where the students would present their work while wearing the virtual reality headset and walking around the 3D model of their design. The main goal was to test whether this approach had a positive impact on the students’ motivation and engagement in the presentation creation and presenting itself. To test this approach, a small case study took place at the Department of Industrial Engineering, Faculty of Mechanical Engineering at the University of Žilina. In conclusion, the overall responses to this experiment were positive; the majority of the students felt more comfortable while presenting and more motivated to put more effort into their preparation. Wearing a virtual reality headset caused the students not to have to directly face the audience, giving them more confidence while presenting. Additionally, the novelty of the virtual reality technology made the students more engaged in showing their work. There is a plan to integrate the virtual reality presentation as the stable part of this assignment.

Development of ZTA (80% Al2O3/20% ZrO2) pre-sintered blocks for milling in CAD/CAM systems

The present work aims to develop a production method of pre-sintered zirconia-toughened-alumina (ZTA) composite blocks for machining in a computer-aided design and computer-aided manufacturing (CAD-CAM) system. The ZTA composite comprised of 80% Al2O3 and 20% ZrO2 was synthesized, uniaxially and isostatically pressed to generate machinable CAD-CAM blocks. Fourteen green-body blocks were prepared and pre-sintered at 1000 °C. After cooling and holder gluing, a stereolithography (STL) file was designed and uploaded to manufacture disk-shaped specimens projected to comply with ISO 6872:2015. Seventy specimens were produced through machining of the blocks, samples were sintered at 1600 °C and two-sided polished. Half of the samples were subjected to accelerated autoclave hydrothermal aging (20h at 134 °C and 2.2 bar). Immediate and aged samples were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). Optical and mechanical properties were assessed by reflectance tests and by biaxial flexural strength test, Vickers indentation and fracture toughness, respectively. Samples produced by machining presented high density and smooth surfaces at SEM evaluation with few microstructural defects. XRD evaluation depicted characteristic peaks of alpha alumina and tetragonal zirconia and autoclave aging had no effect on the crystalline spectra of the composite. Optical and mechanical evaluations demonstrated a high masking ability for the composite and a characteristic strength of 464 MPa and Weibull modulus of 17, with no significant alterations after aging. The milled composite exhibited a hardness of 17.61 GPa and fracture toughness of 5.63 MPa m1/2, which remained unaltered after aging. The synthesis of ZTA blocks for CAD-CAM was successful and allowed for the milling of disk-shaped specimens using the grinding method of the CAD-CAM system. ZTA composite properties were unaffected by hydrothermal autoclave aging and present a promising alternative for the manufacture of infrastructures of fixed dental prostheses.

Development of Cyber Physical System Based Manufacturing System Design

Cyber Physical System (CPS) is Informatics and computer science. Cyber physical manufacturing system is a new research field. In the fields of computer science and manufacturing science and technology Promote the Fourth Industrial Revolution known as Industry 4.0. CPS is generally focused. About the integration of the physical world and cyberspace. It`s the integration of communication, Computational, control, and physical elements. Currently, CPS is Science, government and industry. Systematic literature review on cyber. The physical system of the manufacturing system is not available. The purpose of the chapters in this bookis to gain insight into manufacturing systems and develop cyber-physical systems. Physical systems for intelligent manufacturing. CPS is written using the concept of CPS. A 5-tier architecture system for manufacturing systems. Continuous Key Release. It also describes the technology of cyber physical manufacturing systems. Combined with manufacturing, CPS enables intelligent manufacturing and provides technical support for manufacturing upgrades and conversions. This can be summarized in three aspects: internet-based manufacturing, process intelligence, and product intelligence. Future manufacturing is said to be personalized manufacturing based on cyber physical systems, intelligent manufacturing, digital manufacturing, and network-based manufacturing. CPS continues to transform the manufacturing trends of the industry, creating even more amazing value for future global manufacturing scenarios

Verifying the seating of a 3D-printed removable die using elastomeric matrices: A dental technique.

Computer-aided design and computer-aided manufacturing systems enable digital designing and 3-dimensional (3D) printing of definitive casts with removable dies. However, the fit of the removable dies should be without interferences for their accurate positioning in the cast. Given that the accuracy of additive manufacturing depends on design- and manufacturing-related factors, verifying the accuracy of the position of 3D-printed removable dies in their cast is essential to fabricate positionally accurate definitive prostheses, which would enable minimal or no laboratory and clinical adjustments. This dental technique article presents a straightforward approach to verify the seating of a 3D-printed removable die by using verification matrices made of a polyvinylsiloxane interocclusal registration material.

Facility layout planning through the ALDEP Method in the wooden cable reels industry

Facility layout planning plays a pivotal role in manufacturing system design, impacting vital metrics such as lead time, handling costs, and space optimization. While a significant portion of research has been invested in refining existing facility layouts, there is a noticeable research gap in devising optimized layouts for new establishments, especially in the value-added wood products domain. Addressing this lacuna, this research focused on designing an efficient department-level layout for a wooden cable drums manufacturing facility in an area of 4150 m2. This facility included both office and production areas. The investigative process was segmented into four distinct phases: Deciding the strategic positioning of the facility on the available plot, defining the functional and spatial requirements for each department, establishing the intricate relationship dynamics between these individual units, and rigorously documenting the most optimal department-level facility layout. For precision in layout creation, the ALDEP algorithm was employed, which was further visualized to offer a comprehensive three-dimensional representation. The final layout seamlessly organized seven departments within the 1st Floor Office Area, eight in the 2nd Floor Office Area, and thirteen within the Production Floor. Efficiency evaluation of these areas yielded scores of -811, 184, and -318, respectively. Conclusively, this research furnished actionable insights for manufacturers within the wood products sector and was expected to be an invaluable reference for academics delving into facility planning and value-added wood products manufacturing.

A Framework for Sustainable Manufacturing: Integrating Industry 4.0 Technologies with Industry 5.0 Values

The limitations imposed by resource scarcity and the imperative to mitigate adverse environmental and societal impacts have intensified the urgency of developing more sustainable manufacturing systems. Simultaneously, the rapid development and implementation of new technologies is exacerbating the digital divide among vulnerable workers. Concomitantly, the enabling technologies stemming from Industry 4.0 offer significant potential to enhance the competitiveness of manufacturing systems. However, the impact of these enabling technologies on achieving sustainable manufacturing remains uncertain. This paper embarks on a comprehensive exploration to address this knowledge gap. Initially, it assesses the suitability of each enabling technology within Industry 4.0 across the economic, social, and environmental dimensions of sustainability. Subsequently, the needs of the production process are studied to characterize its sustainable performance. For this, the ASTM E3012-22 standard is introduced. Building upon this foundation, the incorporation of Industry 5.0 is introduced to guide the selection of enabling technologies for sustainability based on its core values, encompassing sustainability, human-centricity, and resilience. The integration of new technologies guided by these values can help bridge the technological divide among vulnerable workers. Finally, a theoretical framework is proposed to enable the design of sustainable manufacturing systems guided by Industry 5.0 values. This framework enables the seamless integration of enabling technologies, machinery, and human expertise throughout the system life cycle.

Data-driven hybrid algorithm with multi-evolutionary sampling strategy for energy-saving buffer allocation in green manufacturing

Buffer allocation, which is an important research topic in manufacturing system design, typically focuses on system performance and cost. However, few previous studies have been performed to investigate energy-saving buffer allocation, which can decrease operational energy consumption in green manufacturing. Furthermore, the computational efficiency of solving the buffer allocation problem requires further investigation. This paper proposes a data-driven hybrid algorithm based on multi-evolutionary sampling strategies for solving energy-saving buffer allocation that can maximize the throughput rate and minimize energy consumption. Two evolutionary sampling strategies, that is, global search and surrogate-assisted local search, are integrated to balance exploitation and exploration. In addition, a database containing historical data pertaining to buffer allocation solutions is used to develop surrogate models that can rapidly predict the throughput and energy consumption and improve the evaluation efficiency of the local search strategy. Experimental results demonstrate the efficacy of the proposed algorithm. This study contributes to an efficient buffer allocation and presents a new perspective on energy-saving measures for green manufacturing designs.

Multi-agent cooperative swarm learning for dynamic layout optimisation of reconfigurable robotic assembly cells based on digital twin

AbstractTo meet the requirement of product variety and short production cycle, reconfigurable manufacturing system is considered as an effective solution in addressing current challenges, such as increasing customisation, high flexibility and dynamic market demand. Dynamic factory layout design and optimisation are the crucial factors in response to rapid change in the mechanical structure, software and hardware integration, as well as production capability and functionality adjustment. Nevertheless, in the current research, the layout design for reconfigurable manufacturing systems is usually simplified with autonomous devices being regarded as 2D shapes. Issues such as overlapping and transportation distance are also addressed in an approximate form. In this paper, we present a novel multi-agent cooperative swarm learning framework for dynamic layout optimisation of reconfigurable robotic assembly cells. Based on its digital twin established in the proposed learning environment (constructed in Visual Components and controlled by TWINCAT), the optimisation framework uses 3D digital representation of the facility models with minimal approximation. Moreover, instead of using a traditional centralised learning manner, multi-agent system could provide an alternative way to address the layout issues combined with the proposed decentralised multi-agent cooperative swarm learning. In order to verify the application feasibility of the learning framework, two aerospace manufacturing use cases were implemented. In the first use case, the layout compactness is reduced by 3.8 times compared with the initial layout setting, the simulated production time is reduced by 2.3 times, and the rearrangement cost decreased by 33.4$$\%$$ % . In addition, all manufacturing activity within the cell can be achieved with a feasible robot path, meaning without any joint limits, reachability or singularity issue at each key assembly point. In the second use case, we demonstrated that with the proposed dynamic layout optimisation framework, it is possible to flexibly adjust learning objectives by selecting various weight parameters among layout compactness, rearrangement cost and production time.

A comparative evaluation of physical properties of CAD/CAM complete denture resins- an in vitro study

BackgroundIn dentistry, there is a growing preference for computer-aided design and computer-aided manufacturing (CAD/CAM) systems over traditional laboratory procedures. However, there is not much literature comparing various CAD/CAM materials. Thus, this study aimed to assess and compare the color stability and hardness of gingival and tooth colored milled and 3D-printed acrylic resins.Materials and methodsFour types of CAD/CAM materials were prepared: 3D-printed pink shade (PP), milled polymenthymethacrylate (PMMA) pink shade (MP), 3D-printed tooth shade (PT) and milled PMMA tooth shade (MT) (n = 6). For hardness, disc shaped samples of 15 mm × 2 mm and for color stability, bar shaped samples of 65mmx10mmx2.5 mm were prepared and polished. Vickers hardness test was performed in a microhardness tester. Color stability test was done by immersing in coffee solution and coca cola for 7 days. Day 0 and day 7 measurements were recorded using a digital spectrophotometer and the change in color was calculated. For statistical analysis, one-way ANOVA and Tukey’s post hoc tests were done.ResultsFor color stability, milled PMMA was superior to 3D-printed resin samples. Milled pink and tooth shade samples had similar color stability, whereas 3D-printed tooth shade samples were more color stable as compared to pink shade 3D-printed samples. For hardness, milled tooth shade PMMA was the most superior one, followed by 3D-printed tooth shade, whereas pink shade milled PMMA and 3D-printed resin samples had similar hardness values and were inferior to the tooth shade CAD/CAM materials.ConclusionColor stability of milled PMMA is superior to that of 3D-printed resins. Hardness of tooth shade milled and 3D-printed resins is more than that of pink shade milled and 3D-printed resins.

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company.

Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

A practical simulation approach for the support of wire arc DED additive manufacturing systems design

Wire Arc Directed Energy Deposition (DED) is a subcategory of the DED Additive Manufacturing (AM) technology that is rapidly gaining popularity due to its potential to produce large-scale, complex metal parts while maintaining a lower cost and higher productivity and sustainability when compared with other AM technologies. Additionally, Wire Arc DED permits a high level of customization in terms of designing and implementing manufacturing systems. However, there are still important quality issues hindering wire Arc DED, along with limited simulation approaches that offer the necessary practicality, from an industrial perspective, to support the design of wire Arc DED manufacturing systems. Towards this goal, aiming to the minimization of the experimental cost and time, a simulation approach of wire arc DED is presented in this study. The model has been developed in COMSOL Multiphysics, allowing for a high level of customization, while maintaining practicality and ease of use, both in terms of implementation as well as computational cost. The modeling approach is analyzed, followed by the implementation of the methodology in a case study. Preliminary results are presented, discussed, and accompanied by suggestions for future research.

Generalizable AI Pipeline for the cost estimation of manufacturing system structures in special machine engineering

Offer management in special machine engineering is characterized by high complexity in the design and cost calculation of manufacturing systems based on customer-specific requirements. To maintain competitiveness, offers should be returned to the customer as quickly and accurately as possible, while minimizing internal effort to save costs and resources. Intelligent tools that support employees estimating subsystem costs add significant value. This paper describes a generalizable, AI-based approach to estimate cost and efforts of individual manufacturing system structures based on historical data. The steps of data ingestion and preprocessing as well as model training and evaluation are orchestrated by a pipeline.

From connectivity via intelligence toward sustainability? Maturity of shopfloor automation technology in the manufacturing industry

The manufacturing industry is experiencing a significant transformation driven by digital technologies, Industry 4.0 concepts, and the potential for sustainable practices. This research presents an overview of the manufacturing industry, focusing on the design of manufacturing systems, digital transformation, and sustainability, identifying the gap between technological advancements and their application in industrial settings. In order to examine this, a panel survey of 899 industrial participants was conducted. This analysis assesses the potential for automation and digital factory implementation alongside the dedication for sustainability in the manufacturing industry. The findings indicate that while research has advanced in these areas, practical applications in the manufacturing industry still lag behind, particularly in terms of technology readiness levels and data implementation for automation. This paper serves as a foundation for future research, providing insights into technologies ready for industrial implementation and identifying areas requiring further optimization and study. Therefore, a matrix is presented that aids in selecting or strategizing technology utilization during the design of manufacturing systems. However, this research primarily focused on technological aspects, recommending future research to explore the interplay between organizational, human related factors, and technology to design manufacturing systems successfully with respect to sustainability.

Design and implementation of intelligent manufacturing system based on sensor networks and cloud computing technology

Design and implementation of intelligent manufacturing system based on sensor networks and cloud computing technology

Design of multi-arc collaborative additive manufacturing system and forming performance research

In view of the key issue of high-quality wire-arc-additive-manufacturing (WAAM) of large metal components, this paper has devised a suite of multi-arc collaborative additive manufacturing equipment endowed with functionalities for controlling arc behavior, managing melt pool dimensions, conducting 3D measurements, and executing subtractive machining. In the initial phase, a cutting-edge five-arc additive manufacturing head apparatus was meticulously engineered. This apparatus ingeniously blends two single-wire torches augmented by three-wire oscillating torches, synergizing deposition metal performance and forming effective. Subsequently, extensive research was carried out to explore a master-slave control-based method for orchestrating the synchronized movements of multiple robotic entities. This diligent investigation culminated in the seamless integration of a multi-arc collaborative additive manufacturing equipment, comprising five-arc high-efficiency additive units, a 3D measurement module, and a precision-driven subtractive machining unit. The influence of different horizontal distances between arc guns on the temperature field and mechanical properties of components was investigated through process experiments. The research results indicate that as the horizontal spacing between the arc guns increases, the cooling rate of the deposited metal gradually slows down, leading to an extended period of elevated temperatures. As a result, both tensile strength and yield strength gradually decrease, while elongation and impact absorption capacity first increase and then decrease. In the end, the developed equipment was utilized to fabricate large ship propeller bracket components, achieving a remarkable 4.7 times increase in manufacturing efficiency compared to single-arc additive manufacturing.

Concurrent Value-Driven Decision-Making Process for the Aircraft, Supply Chain and Manufacturing Systems Design

The integration of product design and supply-chain management can lead to an increase in the profitability and efficiency of companies. However, considering manufacturing, supply chain, and aircraft criteria in the early design phase increases the size of the solutions’ trade space and, thus, the complexity of performing the decision-making process. This paper demonstrates how to leverage value-model theory to simplify the decision-making process when multiple criteria related to multiple systems are considered at the same time. The proposed concurrent approach is formalized from a systems-engineering perspective, considering the interactions between the lifecycle stages of the System of Interest, i.e., the aircraft, and Enabling Systems like the supply chain and manufacturing. A value-based interactive dashboard, called VALORISE, is developed to automatize the process, support decision-makers in modeling their expectations, analyze real-time strategic scenarios, and easily explore the value-driven trade space for best-solution identification. An aeronautical application case highlights the advantages of leveraging the proposed concurrent approach to overcome the limits of traditional approaches, in which decisions about supply chain and manufacturing are addressed once the aircraft configuration is decided.