Manufacturing Process Selection Research Articles

Examining the Progress in Additive Manufacturing in Supporting Lean, Green and Sustainable Manufacturing: A Systematic Review

The quest for waste reduction and the development of manufacturing processes that meet the economic, social and environmental requirements necessitate this study. Additive manufacturing is an emerging digital technology that can be used to seamlessly develop a product through material deposition in layers. The study aims to investigate the progress made in the development of additive manufacturing to support lean, green and sustainable manufacturing. The study employs a systematic literature review approach, specifically the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A total of 158 articles identified from different academic databases that detailed empirical, conceptual and theoretical findings were reviewed, having matched the selection criteria. The outcome of the study indicated that additive manufacturing can be used to achieve waste reduction, reduction in emission generation and carbon footprints with significant energy and material conservation. The findings also indicated that the additive manufacturing process also boasts time- and cost-effectiveness during manufacturing compared to the conventional manufacturing technique. Although the process is energy intensive, careful selection of the suitable additive manufacturing process to be employed based on the requirements coupled with a proper product design may result in considerable energy savings at the preprocessing, processing or post-processing stages. This work adds to the understanding of additive manufacturing and contributes to the existing literature on the relationship among additive, lean, and green manufacturing. The study may help manufacturing organizations in their quest to minimize waste generation and achieve material and energy efficiency throughout their product lifecycles.

Intelligent support in manufacturing process selection based on artificial neural networks, fuzzy logic, and genetic algorithms: Current state and future perspectives

Technological advances, dynamic customer needs, growing uncertainty, and the imperative for sustainable development pressure manufacturing entities to enhance productivity and competitiveness. In this challenging landscape, decision-making in manufacturing process selection is critical. Adopting intelligent support is essential for balancing performance and costs through optimal process selection. Through a comprehensive review of 93 studies published between 2013 and 2023, this paper aims to provide a profound understanding of intelligent support in manufacturing process selection. The findings, which indicate significant interest in intelligent methodologies for manufacturing process selection, are of great importance. Fuzzy logic is prevalent in additive manufacturing due to its ability to handle complex and imprecise data. At the same time, artificial neural networks are favored in conventional manufacturing for leveraging extensive historical data. Genetic algorithms are primarily used for optimization challenges. As manufacturing evolves with new technologies and complex materials, this paper advocates adopting a generalized matrix learning vector quantization neural network for efficient and intelligent process selection in additive and conventional approaches due to its capacity to leverage historical data and handle complex and high dimensional data.

The effect of surface roughness on the performance of 3D printed surface plasmon resonance sensors for refractive index measurements

In this study, a polymer-based surface plasmon resonance (SPR) sensor for refractive index measurements was designed and manufactured via inkjet 3D printing; then, it was optically characterized. Next, it was investigated how the surface finish of the 3D printed optical waveguide affects the sensor performance, i.e., its sensitivity. More in detail, it was studied how the surface roughness changes with the placement of the 3D printed items on the building platform. To achieve this purpose, a Phase I distribution-free quality monitoring analysis of the selected manufacturing process was implemented for a small pilot production run. The aim was to check the stability of surface roughness versus the placement of the 3D printed parts on the building platform. The 3D printed sensor’s surface roughness was assessed through a profilometry study. In particular, the surface roughness was determined for the core of the optical waveguide used to excite the SPR phenomena. Furthermore, the SPR sensors were optically characterized to find the existing relationship between their sensitivity and the considered quality of surface finish. In particular, by varying the surface roughness of the used waveguide, the light scattering in the waveguide changes, and the SPR sensitivity changes too, similarly to the light-diffusing fibers covered by gold nanofilms where the guided light is scattered through a plurality of voids distributed in the core. The procedure followed to investigate the sensor roughness, and establishing their performance enabled the optimal operative range for their application in practice to be identified. Finally, a better knowledge of the 3D printing manufacturing process has been achieved to improve quality of surface finish.

Evaluation of a multi-user requirements axiomatic design decision support tool for manufacturing process selection

AbstractManufacturing process selection presents numerous challenges to designers, including product complexity, consideration of production volumes and part tolerances. This paper introduces a design support tool based on the axiomatic design model to systematically transform requirements into functions and technological capabilities. The results from an evaluation of the implemented prototype tool in the field of medical device design demonstrates its usefulness in selecting the most suitable candidate manufacturing process for a given artifact, while taking into account multiple user requirements.

Manufacturing process selection based on similarity search: incorporating non-shape information in shape descriptor comparison

Manufacturing process selection based on similarity search: incorporating non-shape information in shape descriptor comparison

Selection of a suitable additive manufacturing process for soft robotics application using three-way decision-making

Selection of a suitable additive manufacturing process for soft robotics application using three-way decision-making

Deep learning and sequence mining for manufacturing process and sequence selection

ABSTRACT Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications.

Integrated Continuous Wet Granulation and Drying: Process Evaluation and Comparison with Batch Processing.

The pharmaceutical industry is in the midst of a transition from traditional batch processes to continuous manufacturing. However, the challenges in making this transition vary depending on the selected manufacturing process. Compared with other oral solid dosage processes, wet granulation has been challenging to move towards continuous processing since traditional equipment has been predominantly strictly batch, instead of readily adapted to material flow such as dry granulation or tablet compression, and there have been few equipment options for continuous granule drying. Recently, pilot and commercial scale equipment combining a twin-screw wet granulator and a novel horizontal vibratory fluid-bed dryer have been developed. This study describes the process space of that equipment and compares the granules produced with batch high-shear and fluid-bed wet granulation processes. The results of this evaluation demonstrate that the equipment works across a range of formulations, effectively granulates and dries, and produces granules of similar or improved quality to batch wet granulation and drying.

Mechanism of capture section affecting an intake for atmosphere-breathing electric propulsion

Atmosphere-Breathing Electric Propulsion (ABEP) can compensate for lost momentum of spacecraft operating in Very Low Earth Orbit (VLEO) which has been widely concerned due to its excellent commercial potential. It is a key technology to improve the capture efficiency of intakes, which collect and compress the atmosphere for ABEP. In this paper, the mechanism of the capture section affecting capture efficiency is investigated by Test Particle Monte Carlo (TPMC) simulations with 3D intake models. The inner surface smoothness and average collision number are determined to be key factors affecting capture efficiency, and a negative effect growth model is accordingly established. When the inner surface smoothness is less than 0.2, the highest capture efficiency and its corresponding average collision number interval are independent of the capture section’s geometry and its mesh size. When the inner surface smoothness is higher than 0.2, the capture efficiency will decrease by installing any capture section. Based on the present results, the manufacturing process and material selection are suggested to be prioritized during the intake geometry design in engineering projects. Then, the highest capture efficiency can be achieved by adjusting the length and mesh size of the capture section.

A METHOD FOR REDUCING FUZZINESS AND ACCELERATING NEW PRODUCT MODELLING IN CAD: THE CASE OF DESIGN FOR MANUFACTURING

AbstractImprovements in product development can increase the competitiveness of firms. However, new product development in CAD systems involves difficulties and uncertainties that increase along with the pressure to develop the products. A distinct characteristic of CAD modeling for new product development is its uncertainty. This is because the information is usually approximate and incomplete during CAD modeling. Thus, the main objective of this paper is to propose a robust and flexible CAD approach to reduce uncertainty and accelerate new product modeling in the context of design for manufacturing. This methodology permits the convergence towards different product forms depending on the selected manufacturing process. Application of this approach has shown that when uncertainty is high, approving a complete CAD modeling results in a delay in product development. In contrast, CAD modeling using fuzzy models results in a gain of valuable development time because the model is completed when knowledge about manufacturing technologies, company fit and capabilities, and markets is available.

Assessment of Manufacturing Process Selection Utilizing the TOPSIS Method

Assessment of Manufacturing Process Selection Utilizing the TOPSIS Method

Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS.

For many different types of businesses, additive manufacturing has great potential for new product and process development in many different types of businesses including automotive industry. On the other hand, there are a variety of additive manufacturing alternatives available today, each with its own unique characteristics, and selecting the most suitable one has become a necessity for relevant bodies. The evaluation of additive manufacturing alternatives can be viewed as an uncertain multi-criteria decision-making (MCDM) problem due to the potential number of criteria and candidates as well as the inherent subjectivity of various decision-experts engaging in the process. Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets that are effective in handling ambiguity and uncertainty in decision-making. This study offers an integrated fuzzy MCDM approach based on Pythagorean fuzzy sets for assessing additive manufacturing alternatives for the automotive industry. Objective significance levels of criteria are determined using the Criteria Importance Through Inter-criteria Correlation (CRITIC) technique, and additive manufacturing alternatives are prioritized using the Evaluation based on Distance from Average Solution (EDAS) method. A sensitivity analysis is performed to examine the variations against varying criterion and decision-maker weights. Moreover, a comparative analysis is conducted to validate the acquired findings.

РОЗРАХУНКОВО-АНАЛІТИЧНЕ ПОРІВНЯННЯ ПОКАЗНИКІВ ВЛАСТИВОСТЕЙ ВАРІАНТІВ КОНСТРУКТИВНО-ТЕХНОЛОГІЧНИХ РІШЕНЬ ТРАНСВЕРСАЛЬНИХ З’ЄДНАНЬ “МЕТАЛ + КОМПОЗИТ”

The question of selection of variants for objective optimization of manufacturing preparation of production of different structural-manufacturing solutions of metal-composite articles and units with transversal connections are actualized. Several variants of structural-manufacturing solutions (SMS) of joints are considered. Three types of fastening connections are selected for analysis, i.e. with pyramidal, cylindrical and sheet-formed micro-fasteners (MF).Structural schemes of possible manufacturing processes of creation fastening micro-elements with different shapes are suggested. These schemes take into consideration influence of both main and auxiliary operations, which have to be taking into account at estimation of manufacturing process duration and energy consumption.Possibility of micro-fasteners manufacturing of aluminum, titanium alloya and stainless steels is considered at comparison of numerical parameters of manufacturing processes.Producing of micro-fasteners by means of milling with both disc and cylindrical mills, and up-to-date methods of electro-erosion treatment of materials with electrode-tool or with moving wire is analyzed as comparing parameters of different manufacturing processes.Properties of manufacturing processes of fastening elements forming are determined as actual ones. Duration of connecting elements manufacturing and required energy resources consumption are selected as exact actual parameters,Qualimetric model of manufacturing processes selection with consequent their optimization in accordance with goal functions is synthesized. Incoming, controllable and outcoming parameters of the model are determined. Approach to calculation of complex quality index of micro-fasteners manufacturing is suggested. This approach is used for calculation numerical quality indexes of different manufacturing processes. General conclusions are done.

A study in EDM electrode manufacturing using additive manufacturing

Electrode can be manufacture by conventional as well as non-conventional processes like Thermal spraying and Additive manufacturing (AM). In AM mostly Fused Deposition Modeling (FDM) and Laser Sintering (LS) are used. The selection of a proper manufacturing process has a very vital role when it comes to output characteristics of Electrodes. In this paper Authors have reviewed LS and FDM based EDM tool manufacturing processes. The purpose of this study is to present significant findings about effective EDM electrode manufacturing using LS and FDM. It was concluded that, the RP made tool electrode can be used where higher surface finish are desired and MRR and TWR can be neglected. The electrode manufactured using FDM techniques promising results for rough cutting and semi-finishing cut in EDM operations.

Integration of Industry 5.0 requirements in digital twin-supported manufacturing process selection: a framework

Process selection has been an integral part of manufacturing design and operation, with a close link to manufacturing process modeling. Throughout the years, it has been enriched with many criteria, such as energy efficiency, emissions, resources, and sustainability in general, increasing its complexity in terms of modeling and optimization. Herein, process selection is coupled with (a) the capabilities of the digital twins, (b) the criteria introduced by Industry 5.0 (I5.0) in terms of sustainability and human inclusion, and (c) the opportunities offered by key enabling technologies. A framework is presented capable to implement automated process selection and potentially scheduling through a specific set of integrated technologies, utilizing the concept of the microfactory as a basis in terms of implementation. A case study for the evaluation of two parts, using Additive Manufacturing and laser welding, in different scenarios is presented. It was concluded that the inclusion of the I5.0 criteria not only increases the well-being of the workers but also the energy and time efficiency of the production line, increasing the profit margin.

An Axiomatic Design Methodology for Manufacturing Process Selection Based on Multi-User Requirements Mapping

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Combined manufacturing and cost complexity scores-based process selection for hybrid manufacturing

Smart manufacturing involves the use of emergent technologies and requires dynamic feedback of customer’s demands. These concerns need a rapid Decision Support System (DSS) considering emergent manufacturing processes such as Additive (AM) and Hybrid (HM) Manufacturing and tracking the product changes. This paper proposes a DSS for process selection based on manufacturing complexity and cost. The complexity parameters, deduced from design for manufacturing (DFM), design for additive manufacturing (DFAM) and design for hybrid manufacturing (DFHM) rules, are automatically extracted from computer aided design (CAD) model to follow the product changes. Cost models are defined for each manufacturing process type. In design phase, the manufacturing cost estimation allows considering the cost as a selection factor. The combined complexity based on manufacturing difficulty and cost represents a new paradigm for process selection. The case studies show the reliability of the proposed DSS and its ability to respect the company resources and strategy.

A multicriteria decision-making method for additive manufacturing process selection

Purpose Because of the significant differences in the features and requirements of specific products and the capabilities of various additive manufacturing (AM) solutions, selecting the most appropriate AM technology can be challenging. This study aims to propose a method to solve the complex process selection in 3D printing applications, especially by creating a new multicriteria decision-making tool that takes the direct certainty of each comparison to reflect the decision-maker’s desire effectively. Design/methodology/approach The methodology proposed includes five steps: defining the AM technology selection decision criteria and constraints, extracting available AM parameters from the database, evaluating the selected AM technology parameters based on the proposed decision-making methodology, improving the accuracy of the decision by adopting newly proposed weighting scheme and selecting optimal AM technologies by integrating information gathered from the whole decision-making process. Findings To demonstrate the feasibility and reliability of the proposed methodology, this case study describes a detailed industrial application in rapid investment casting that applies the weightings to a tailored AM technologies and materials database to determine the most suitable AM process. The results showed that the proposed methodology could solve complicated AM process selection problems at both the design and manufacturing stages. Originality/value This research proposes a unique multicriteria decision-making solution, which employs an exclusive weightings calculation algorithm that converts the decision-maker's subjective priority of the involved criteria into comparable values. The proposed framework can reduce decision-maker's comparison duty and potentially reduce errors in the pairwise comparisons used in other decision-making methodologies.

Application of MCDM and Taguchi super ranking concept for materials selection problem

Material selection is a topic of pivotal importance in the optimal design and development of industrial products. Increasing demand for high-performance materials with desirable and tailor-made properties is expected to drive the future market. This paper examines the application of multicriteria decision-making techniques like AHP, TOPSIS, EDAS, VIKOR, and Taguchi-based super ranking concepts for the selection of optimal aluminum alloy material for the sheet metal forming process. The criteria used to evaluate the optimal material selection were identified as yield strength, tensile strength, thermal conductivity, impact, density, specific heat, coefficient of thermal expansion, and % elongation. The most ranked aluminum material frequency is selected for sheet metal forming simulation using DEFORM, a process simulation FEM-based tool. An integrated Taguchi DOE and process simulation were carried out for the best-ranked AA2024 aluminum material. The results showed that the MCDM and Taguchi-based super ranking concept provides an intelligent and methodical assessment for solving material selection from a finite set of alternatives for the sheet metal forming process. The group decision behavior, agility, and flexibility in including multiple decision-making criteria for overall comparative analysis of materials, and manufacturing processes with uncertainty make MCDM methods a definitive tool for materials and manufacturing process selection.

A Package of Established Analytical Tools to Investigate the Solid-State Alteration of Lipid-Based Excipients.

Lipid-based excipients (LBEs) are low-toxic, biocompatible, and natural-based, and their application supports the sustainability of pharmaceutical manufacturing. However, the major challenge is their unstable solid-state, affecting the stability of the pharmaceutical product. Critical physical properties of lipids for their processing-such as melt temperature and viscosity, rheology, etc.-are related to their molecular structure and their crystallinity. Additives, as well as thermal and mechanical stress involved in the manufacturing process, affect the solid-state of lipids and thus the performance of pharmaceutical products thereof. Therefore, understanding the alteration in the solid-state is crucial. In this work, the combination of powder x-ray diffraction and differential scanning calorimetry (DSC) is introduced as the gold standard for the characterization of lipids' solid state. X-ray diffraction is the most efficient method to screen polymorphism and crystal growth. The polymorphic arrangement and the lamella length are characterized in the wide- and small-angle regions of x-ray diffraction, respectively. The small-angle x-ray scattering (SAXS) region can be further used to investigate crystal growth. Phase transition and separation can be indicated. DSC is used to screen the thermal behavior of lipids, estimate the miscibility of additives and/or active pharmaceutical ingredients (API) in the lipid matrix, and provide phase diagrams. Four case studies are presented in which LBEs are either used as a coating material or as an encapsulation matrix to provide lipid-coated multiparticulate systems and lipid nanosuspensions, respectively. The lipid solid-state and its potential alteration during storage are investigated and correlated to the alteration in the API release. Qualitative microscopical methods such as polarized light microscopy and scanning electron microscopy are complementary tools to investigate micro-level crystallization. Further analytical methods should be added based on the selected manufacturing process. The structure-function-processability relationship should be understood carefully to design robust and stable lipid-based pharmaceutical products.