Selection Of Manufacturing Technologies Research Articles

Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.

Selection of Manufacturing Technologies in the Context of Digital Transformation: A Systematic Review

Currently, the digital transformation phenomenon is evident in all decision areas within modern firms and directly impacts the technology management process. For manufacturers, the management and selection of appropriate manufacturing technologies are relevant processes for the achievement of strategic objectives, including a digital strategy. This work is characterized as a review paper, based on a systematic literature review approach. It is derived from the observation of a real problem in a recognized world-class automaker, regarding the selection of manufacturing technologies. The identified problem consists of the lack of a structured and consolidated decision-aiding procedure to support managers in selecting manufacturing technologies in the context of digital transformation, from defining performance objectives to recommending technologies according to the decision objective. The main theoretical implications is the proposition of a research agenda for the development of a decision-aiding procedure concerning two main decision objectives: (i) develop a technology roadmap for a long-term investment plan, and (ii) address specific issues to improve manufacturing performance. The main managerial implication is the synthesis of decision methods and decision criteria observed in the set of related works that can be used for future applications regarding the selection of manufacturing technologies.

Manufacturing Technologies of Polymer Composites-A Review.

Polymer composites have been widely used in the aviation, aerospace, automotive, military, medical, agricultural and industrial fields due to their excellent mechanical properties, heat resistance, flame retardant, impact resistance and corrosion resistance. In general, their manufacturing process is one of the key factors affecting the life cycle of polymer composites. This article provides an overview of typical manufacturing technologies, including surface coating, additive manufacturing and magnetic pulse powder compaction, which are normally used to reduce the failure behaviour of polymer composites in service so that the quality of composite products can be improved. Advanced polymer composite powder manufacturing processes, the processing mechanism and experimental methods are described, and the influence of different manufacturing processes on the moulding quality is revealed. This investigation can provide suitable methods for the selection of manufacturing technology to improve the quality of polymer composite products.

Selection of appropriate manufacturing technology for development of Oxycodone prolonged release film-coated tablets using QbD approach

Selection of appropriate manufacturing technology for development of Oxycodone prolonged release film-coated tablets using QbD approach

Cross-efficiency for advanced manufacturing technology selection: A multi-task approach

Advanced manufacturing technologies (AMTs) are more and more used by firms to perform repetitive tasks in the production processes. As opting for an ATM represents an important investment for firms, several methodologies have been suggested to help firm decision-makers selecting the best one. A popular concept in that context is the cross-efficiency technique. In short, it endogenously selects the best ATM by computing scores using linear programmings. In this paper, we extend the cross-efficiency technique by adding a new feature: we model ATMs as multi-task processes. The multi-task approach presents two main advantages. One, it naturally gives the option to allocate inputs/costs and indicators/attributes to every task, yielding to a more realist modelling of the AMT processes. Two, AMTs can be compared for every task separately, increasing the discriminatory power of the selection process. As a consequence, the overall performances can be better understood, and, in particular, the reasons for declaring a specific AMT to be best can be investigated. We demonstrate the usefulness of our approach by considering a numerical example and two applications. In each case, we demonstrate the practical and managerial usefulness of our approach.

Predictive cost comparison of manufacturing technologies through analyzing generic features in part screening

The selection and interaction of different manufacturing technologies are major challenges in product development and production processes. Costs are one criterion for selecting the most suitable manufacturing technology for a component. Therefore, they must be considered for an automated technology selection in part screening approaches. These costs arise during the component manufacture, during the transport of the components to the production site, and within the factory’s assembly line. However, holistic cost data is not available at an early stage during product development. Hence, these information gaps must be filled to ensure the correct selection of a component’s manufacturing technology. This paper first analyzes the cost data of past product developments regarding potential differences between manufacturing technologies. Abstracted geometric part information and production-related aspects are derived and predicted to serve as input features for the subsequent cost prediction. These AI-based cost prediction models are trained for relevant cost types along the part’s lifecycle. The most capable prediction models are combined into a manufacturing technology-dependent cost analysis of the components. Based on that, the forecasted costs of different manufacturing scenarios represent one input for the overarching part screening methodology to select the most suitable manufacturing technology for each component.

A Framework for Additive Manufacturing Technology Selection

Additive manufacturing is a popular emerging technology of producing parts directly from digital models. This technology has presented benefits such as freedom of design, the ability to customise, and shortened process chains. Presently, there are different Additive Manufacturing (AM) processes that are available in the market. Often, transport equipment manufacturing companies are faced with challenges while selecting the AM processes that are suited to their needs. Decision-makers need to consider all the underlining factors before a conclusion is reached. This paper proposes an approach that can be used by companies in the rail sector to select AM technologies that are suited to their applications. The approach involves identifying suitable parts, comparing applicable AM technologies and selecting the most suitable technology. The next stage involves re-designing the parts based on the selected technology. The approach is applied to benchmark parts from the industry. The study provides enlightenment on how AM can be applied in the rail industry.

Determination of destruction cause of experimental mill roll of stand roughing group of mill 150.

High quality of purchased resources plays a critical role under the conditions of severe international competition. Raw materials, materials and components are purchase for product production and their quality is an essential part in the production of steel at OJSC “BSW – Management Company of the Holding “BMC” holding». The problem of excessively high level of dynamic loads is especially acute for roughing stands. The main reasons for dynamic overloads are intense transient processes occurring in the rolling cycle, the presence of elastic constraints and gaps in the system, imperfection of electric drive system and errors made during development. Therefore, there is a need for selection of material and manufacturing technology for mill rolls capable to withstand such loads for a sufficiently long period. Investigations of destruction causes of experimental mill roll of stand roughing group of mill 150 of section rolling shop at OJSC “BSW – Management Company of the Holding “BMC” holding were carried out. Main classification features are established based on the analysis of the fracture. The results of metallographic studies of microstructure and chemical composition of central and subsurface zone roll fragments are presented. The reason of destruction of experimental mill roll was discovered – nonconformance of roll material grade with the requirements of technological instructions and normative documentation for rough group of stands No. 1–3

Methodology for an automatic and early manufacturing technology selection on a component level

Production systems of the automotive industry process parts that were previously designed and manufactured according to different manufacturing technologies. In car body architectures, additive manufacturing (AM) has become a relevant technology for supplementing conventional manufacturing technologies, e.g., casting or forming technologies. This paper presents a methodology for an automatic and objective early-stage analysis of part features and the subsequent identification of the parts’ most suitable manufacturing technology. For this purpose, a comprehensive database is required, in which several technological and economic parameters need to be derived and predicted, including part requirements, production inherences, expected lifecycle costs, as well as geometric information. Based on this, data screening allows to effectively evaluate the technological and economic potential for a component to be manufactured either conventionally or additively in early product development phases. One core element is the part requirements derivation and analysis within one novel module of the part screening methodology. Subsequently, the product development process and the production system can be adapted according to the identified, most promising manufacturing technologies. Hence, this early-stage decision allows for cost reduction through an increased planning reliability. This work thus contributes to a successful co-evolution of smart product development and the production processes.

A Fuzzy Network DEA Approach to the Selection of Advanced Manufacturing Technology

The selection of advanced manufacturing technologies (AMTs) is an essential yet complex decision that requires careful consideration of various performance criteria. In real-world applications, there are cases that observations are difficult to measure precisely, observations are represented as linguistic terms, or the data need to be estimated. Since the growth of engineering sciences has been the key reason for the increased utilization of AMTs, this paper develops a fuzzy network data envelopment analysis (DEA) to the selection of AMT alternatives considering multiple decision-makers (DMs) and weight restrictions when the input and output data are represented as fuzzy numbers. By viewing the multiple DMs as a network one, the data provided by each DM can then be taken into account in evaluating the overall performances of AMT alternatives. In the solution process, we obtain the overall and DMs efficiency scores of each AMT alternative at the same time, and a relationship in which the former is a weighted average of the latter is also derived. Since the final evaluation results of AMTs are fuzzy numbers, a ranking procedure is employed to determine the most preferred one. An example is used to illustrate the applicability of the proposed methodology.

Selection of Computer-integrated Manufacturing Technologies Using a Combined Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Selection of Computer-integrated Manufacturing Technologies Using a Combined Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Selection of Subtractive Manufacturing Technology Versus Additive Manufacturing Technology for Rapid Prototyping of a Polymeric Product

The Additive Manufacturing (AM) industry has expanded steadily, occupying the market very quickly. New types of 3D printers have appeared and new types of polymeric and composite materials have been developed for these printers. Thus it passed very quickly from the stage in which the parts that were made by rapid prototyping (RP) only to be exposed (demonstration parts) to stage AM the parts are fully functional. Of course, the future of AM is still on the horizon, it is barely visible. The other technologies for forming the geometry of the part, ie subtractive manufacturing technology and formative manufacturing technology are still the basis of industrial production. Each technology has its own advantages and disadvantages and is chosen on a case-by-case basis, depending on the objectives pursued. In this paper, a study is made on the rapid prototyping of a single pump rotor part. The material of the piece is of polymer type, ABS. The piece was made in two variants: by additive manufacturing technology (PolyJet) and by subtractive manufacturing technology (milling). After processing, several parameters were followed, such as the functionality of the part, the surface quality, the mechanical tensile strength, the dimensional accuracy, and last but not least the manufacturing cost and the duration of the manufacturing cycle. The data thus obtained were processed with an artificial intelligence program for decision making.

Multi-Criteria Assessment of Continuous Manufacturing Reactor Technologies in Upstream Pharmaceutical Supply Chains

The COVID-19 pandemic highlighted vulnerabilities in upstream pharmaceutical supply chains (PSC) associated with the manufacture of essential active pharmaceutical ingredients (APIs). The need to develop health countermeasures provided new impetus to supporting advanced manufacturing technologies such as process intensification through continuous manufacturing (CM). Recent advanced manufacturing developments suggest potential advantages in adopting CM over traditional batch technologies. However, at an early stage of evaluation detailed quantitative information is limited, and selecting specific reactor technologies based on multiple, conflicting criteria is challenging. To address these early-stage manufacturing technology selection challenges, this paper applies an Analytical Hierarchy Process approach, integrating multiple managerial and engineering criteria. The analysis focuses on reactor technologies in upstream PSC manufacturing. Findings suggest that microreactor technologies outperform alternatives all things considered. However, PSC managerial considerations introduce nuances in specific therapeutic areas e.g., antivirals where a tension between complex chemistry and the need for flexibility in unit operations may favour batch manufacturing. For analgesics the need to exploit the existing manufacturing base whilst addressing inventory reduction favour technologies that incorporate elements of batch and CM. Research enriches previous conceptual frameworks predicated on volume-variety considerations, providing an empirical workflow for the multi-faceted evaluation of alternative CM reactor technologies in PSC.

Factors for metal additive manufacturing technology selection

PurposeThis paper addresses the most important factors for the selection of additive manufacturing (AM) technology as a method of production of metal parts. AM creates objects by adding material layer by layer based on 3D models. At present, interest in AM is high as it is hoped that AM contributes to the competitiveness of Western manufacturing industries.Design/methodology/approachA literature study is conducted to identify the factors that affect the selection of AM technology. Expert interviews and the best–worst method are used to prioritize these factors based on relative factor weights.FindingsTechnology, demand, environment and supply-related factors are categorized and further mapped to offer a holistic picture of AM technology selection. According to expert assessments, market demand was ranked highest, although market demand is currently lacking.Research limitations/implicationsThe composition and size of the expert panel and the framing of some of the factors in light of previous literature cause validity limitations. Further research is encouraged to differentiate the selection factors for different AM implementation projects.Originality/valueThe paper presents a more complete framework of factors for innovation selection in general and the selection of AM technology specifically. This framework can serve as a basis for future studies on technology selection in the (additive) manufacturing sector and beyond. In addition to AM-specific factor weights, the paper explains why specific factors are important, reducing uncertainty for managers that have to choose between alternative manufacturing technologies.

Non-Antagonistic Contradictoriness of the Progress of Advanced Digitized Production with SARS-CoV-2 Virus Transmission in the Area of Dental Engineering

The general goals of advanced digitized production in the Industry 4.0 stage of the industrial revolution were presented along with the extended holistic model of Industry 4.0, introduced by the authors, indicating the importance of material design and the selection of appropriate manufacturing technology. The effect of the global lockdown caused by the SARS-CoV-2 virus transmission pandemic was a drastic decrease in production, resulting in a significant decrease in the gross domestic product GDP in all countries, and gigantic problems in health care, including dentistry. Dentists belong to the highest risk group because the doctor works in the patient’s respiratory tract. This paper presents a breakthrough authors solution, implemented by the active SPEC strategy, and aims to eliminate clinical aerosol at the source by negative pressure aspirating bioaerosol at the patient’s mouth line. The comparative benchmarking analysis and its results show that only the proprietary solution with a set of devices eliminates the threat at the source, while the remaining known methods do not meet the expectations. The details of this solution are described. Photopolymer materials and additive Digital Light Printing (DLP) technology were used.

Comparison of different additive manufacturing patterns on the performance of rapid vacuum casting for mating parts via the Taguchi method

Rapid vacuum casting has been proven to be a successful method in producing high-quality parts in small series. However, a challenge lies in the selection of proper Additive Manufacturing (AM) technologies for the development of a master pattern for the vacuum casting process. Each AM technologies differ from one another in terms of dimensional accuracy, surface finish, cost and lead times. The aim of this study is to investigate the performance of casting mating parts based on different additive manufacturing patterns for small batch. Three types of AM-based patterns: Fused Deposition Modeling (FDM), Stereolithography (SLA) and Multi-Jet Fusion (MJF) were compared. The Taguchi method, Signal to Noise ratio (S/N), Analysis of Variance (ANOVA) and T-test were conducted in determining the optimized parameters. From the findings, curing time is shown to be a significant parameter for dimensional accuracy and surface finish. Dimensional deviation varies in different directions of parts. For surface finish, there was only a slight change from the master pattern whereas the surface roughness of casting parts decreased within the range 0.23% to 2.85%. Tolerance grades for the selected dimensions of the parts were in the permissible range, based on ISO286-1:2010. When using distinct kinds of AM patterns to create replacement components, design tolerance is needed. It was suggested to select AM technology similar to that had been used for the original components. Battery cover was selected as a case study to represent the mating application parts.

An AHP and Fuzzy AHP Multifactor Decision Making Approach for Technology and Supplier Selection in the High-Functionality Textile Industry

Using the lamination process in the high-functionality textile industry, this paper investigates the development of an approach for technology and supplier selection based on 12 factors affecting manufacturing technology selection with respect to the supply chain. In many manufacturing industries, technology selection still represents a challenging and not fully understood area, especially when it comes to choosing between competing technologies with similar levels of performance. The methodology employed identified two competing lamination technologies with high levels of development and mechanization: 1) full lamination/solvent type; and 2) dot lamination/solvent free. This was followed by the identification of multiple factors affecting manufacturing technology selection with respect to the supply chain, the use of analytical hierarchy process techniques, and a case study involving site visits and interviews with the senior management of a company operating in the high-functionality textiles industry. The analysis of empirical data gathered from the case study revealed how supply chain related factors are more important than those directly related to the technical merit of the technology such as low-cost manufacturing or automation. The proposed approach has the potential to be transferable to other industries using lamination processes and/or advanced fiber and fabric technology.

Selection of additive manufacturing technologies in productive systems: a decision support model

Abstract: Additive Manufacturing (AM) has seen continued growth in adoption by organizations in recent years, changing production processes, supply chain, maintenance, product development and the global economy. There are several Additive Manufacturing technologies and equipment on the market, however, there are no guidelines, benchmarking or decision support tools for proper selection. After a systematic review of the literature, the lack of propositions that act during the development of the product and process was evidenced. This research focuses on the selection of Additive Manufacturing technologies for a production system. The general objective being to propose a decision support model based on the characteristics of additive technologies and competitive criteria, resulting in a choice aligned with the guidelines of organizations and their production systems. For the operationalization of the model, the AHP techniques and conjoint analysis were used together, where the characteristics of the Additive Manufacturing technologies were related to the competitive criteria for the model to indicate the recommended technology to the production system or organization in question. Finally, the artifact recommended the right technology in three distinct situations, from a vendor, user, and expert point of view. Thus, this research contributes to both academia and business by developing a functional artifact of additive manufacturing technology selection. Also, by contributing to the increased availability of information on the nine most commonly used additive technologies in industry.

Ceramic Porous Materials with Catalytic Properties

Undoubted leadership among catalytic materials for the purification of exhaust gases is occupied by composite cermets. Creation of catalytic materials for gas purification indicates that the main problem is partial or complete replacement of rare earth metals in their composition. In this regard, the creation of new permeable catalytic materials with partial or complete replacement of rare earth metals is relevant. Selection of composition and manufacturing technology is of great importance for the production of porous materials. The basis of charge for the production of porous materials is industrial waste such as metal oxides, metal powders, and polymetal ores (bastnaesite) containing rare earth cerium. Charge composition for the production of porous permeable metal-ceramic materials based on metal oxides with the addition of ground bastnaesite is proposed. The influence of bastnaesite on the physicomechanical and functional properties of materials obtained by self-propagating high-temperature synthesis is studied. It is shown that the neutralizer filters from materials with additives of bastnaesite have catalytic properties and can be successfully used in the purification of exhaust gases of internal combustion engines.

A new DEA common-weight multi-criteria decision-making approach for technology selection

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.