Explore the vast range of titles available.

Smart manufacturing, tailored by the 4th industrial revolution and forces like innovation, competition, and changing demands, lies behind the concurrent evolution (also known as co-evolution) of products, processes and production systems. Manufacturing companies need to adapt to ever-changing environments by simultaneously reforming and regenerating their product, process, and system models as well as goals and strategies to stay competitive. However, the ever-increasing complexity and ever-shortening lifecycles of product, process and system domains challenge manufacturing organization’s conventional approaches to analysing and formalizing models and processes as well as management, maintenance and simulation of product and system life cycles. The digital twin-based virtual factory (VF) concept, as an integrated simulation model of a factory including its subsystems, is promising for supporting manufacturing organizations in adapting to dynamic and complex environments. In this paper, we present the demonstration and evaluation of previously introduced digital twin-based VF concept to support modelling, simulation and evaluation of complex manufacturing systems while employing multi-user collaborative virtual reality (VR) learning/training scenarios. The concept is demonstrated and evaluated using two different wind turbine manufacturing cases, including a wind blade manufacturing plant and a nacelle assembly line. Thirteen industry experts who have diverse backgrounds and expertise were interviewed after their participation in a demonstration. We present the experts’ discussions and arguments to evaluate the DT-based VF concept based on four dimensions, namely, dynamic, open, cognitive, and holistic systems. The semi-structured conversational interview results show that the DT-based VF stands out by having the potential to support concurrent engineering by virtual collaboration. Moreover, DT-based VF is promising for decreasing physical builds and saving time by virtual prototyping (VP).

This paper deals with an integrated design approach for additive manufacturing (AM), as a design for additive manufacturing (DfAM) approach. This DfAM approach focuses on two important activities of manufacturability analysis, as well as material and process selection in the concept of concurrent engineering and integrated design approach (CE/ID). It could be considered as a guideline for the researchers in the early phase of the product development process. For this purpose, a novel process and resource selection problem for AM is formalized as an additive manufacturing process and resource selection problem (AMPRSP). This problem is defined to investigate simultaneously the manufacturability, and process and resource selection issues for AM. A multi-criteria evaluation system (MCES) is proposed to solve this problem by evaluating the manufacturability of the product, and selecting the AM resources. Firstly, the AM process, machine, and material are explored and selected regarding technical and economic evaluation criteria. Then, the most appropriate alternative for fabrication with AM is selected by considering the sub-criteria including build time, accuracy performance, and cost. To solve this multi-criteria problem, an analytic hierarchy process (AHP) method is employed. Overall, this evaluation system is a step-by-step methodology that includes screening, comparative assessment, and a ranking process to select the most suitable alternative for AMPRSP. Finally, the proposed approach is illustrated by an industrial case study.

As competition shifts away between competitors to supply chains, simultaneous sharing of technical expertise on product design is key to manufacturing success. Thus, purpose of this study is to examine the mediating effect of concurrent engineering of product design (CEPD) on the relationship between advanced manufacturing technology (AMT) and supply chain performance (SCP). The paper is a cross-sectional study, and data was collected among top managers of manufacturing companies. Cluster and systematic random sampling techniques were used to select the respondents. Structural equation modeling (Amos graphic) was used for analysis. The study found a positive relationship between advanced manufacturing technology and supply chain performance, advanced manufacturing technology and concurrent engineering of product design, and concurrent engineering of product design and supply chain performance. It also concluded that concurrent engineering of product design is a full mediator between advanced manufacturing technology and supply chain performance.

Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts.

Set-based design (SBD), sometimes referred to as set-based concurrent engineering (SBCE), has emerged as an important component of lean product development (LPD) with all researchers describing it as a core enabler of LPD. Research has explored the principles underlying LPD and SBCE, but methodologies for the practical implementation need to be better understood. A review of SBD is performed in this article in order to discover and analyse the key aspects to consider when developing a model and methodology to transition to SBCE. The publications are classified according to a new framework, which allows us to map the topology of the relevant SBD literature from two perspectives: the research paradigms and the coverage of the generic creative design process (Formulation–Synthesis–Analysis–Evaluation–Documentation–Reformulation). It is found that SBD has a relatively low theoretical development, but there is a steady increase in the diversity of contributions. The literature abounds with methods, guidelines and tools to implement SBCE, but they rarely rely on a model that is in the continuum of a design process model, product model or knowledge-based model with the aim of federating the three Ps (People–Product–Process) towards SBCE and LPD in traditional industrial contexts.

ABSTRACT Solution spaces are sets of designs that meet all quantitative requirements of a given design problem, aiding requirement management. In previous works, ways of calculating subsets of the complete solution space as hyper-boxes, corresponding to a collection of permissible intervals for design variables, were developed. These intervals can be used to formulate independent component requirements with built-in tolerance. However, these works did not take physical feasibility into account, which has two disadvantages: first, solution spaces may be useless, when the included designs cannot be realized. Second, bad designs that are not physically feasible unnecessarily restrict the design space that can be used for requirement formulation. In this paper, we present the new concept of a requirement space that is defined as the largest set of designs that (1) allows for decomposition (e.g., into intervals when it is box-shaped), (2) maximizes the useful design space (good and physically feasible), and (3) excludes the non-acceptable design space (bad and physically feasible). A small example from robot design illustrates that requirement spaces can be significantly larger than solution spaces and thus improve requirement decomposition.

The research aims to use the latest technology, namely \"Concurrent Engineering \", to redraw production and assembly lines to be more efficient and take less time in manufacturing and delivery. Which will help to earn more money, reduce the cost, achieve quality standards, and continue the production line in the assembly lines of the General Company for Mechanical Industries / Automotive Assembly Line, calculating the workstations and stages until the final production and presenting it to the customer. It was noted that there were multiple problems that led to losses on the assembly line as a result of the lack of distribution and timing of activities across the stages of the production line, causing bottlenecks in some workstations. This was reflected in a loss of about (398) minutes at a cost of about \\$3,900 per minute, which led to insufficient use of human or material resources (machinery and equipment). The current study presented a proposal through which the assembly line can be restructured and the implementation of some activities that are close in completion time can be synchronized, which in turn leads to reducing the total completion time from (937.5 to 772 minutes), that is, by 16.75%, in addition to maximizing working times and machine operating efficiency. And reduce wasted time at most stations.

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems.

The Concurrent Engineering (CE) approach was developed in the 1980s, based on the concept that different phases of a product life cycle should be conducted concurrently and initiated as early as possible within the Product Creation Process (PCP).

The structures laboratory is a crucial facility for supporting research development related to the construction industry. This kind of laboratory has several components, and one of its key components is the reaction wall. The reaction wall supports the execution of experimental tests on structural elements under specific configurations and loads. Constructing a reaction wall presents challenges due to the highest standards to be met. This research analyzes the construction process of a reaction wall at a private university in Peru. Initially, a schedule was proposed by a contractor. However, the client, knowing the complexity of such construction, asked to review the initial schedule to ensure that it would meet the constructability requirements under the integrated product-organization-process approach given by the virtual design and construction (VDC) methodology. The result of this study pointed out the need to revise the original proposed schedule, extending the construction time to 66% due to the lack of construction processes compatible with the requirements for the reaction wall’s execution. Finally, a survey was conducted to observe the learning curve associated with implementing the VDC methodology.