Evolutionary algorithms in additive manufacturing systems: Discussion of future prospects

Leirmo, Torbjørn Langedahl; Martinsen, Kristian

doi:10.1016/j.procir.2019.03.174

Cited by 13 publications

(7 citation statements)

References 59 publications

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“…The emergence of Automation makeup language eliminated data discrepancies and promotes systems integration for modern machine system. AI-AM research is promising to develop intelligent closed-loop in-situ monitoring and controlling AM machines [7,17,34,91,92]. For example, integrated self-configuration and adaptability AM machine systems capable of correcting in-process fluctuations [4].…”

Section: Discussionmentioning

confidence: 99%

“…LBAM allows the combining of multiple materials to suit varying requirements, a possibility cable of maximizing product performance with functionally gradient and nature inspired designs [51,55]. Despite the several LBAM offered benefits, there are also some challenges and limitations associated method [17]. The application of AM example for HPM comes with specific challenges which may deter adoption rate, for example, high machine cost [50], limited materials [43] and slow build time [81].…”

Section: Benefits and Challenges Of Am For High-performance Metalsmentioning

confidence: 99%

“…The unique properties of AM specific HPMs feedstock enable highly efficient, accurate, and reliable functional products not amenable to CM counterparts. AM can be used solely or in a hybrid manufacturing route to offset CM constraints [17]. Manufacturers can leverage the unique capabilities of LBAM through paired I4.0 technologies to produce parts with intricate designs and optimized shapes that were traditionally impossible or too expensive to produce.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing industry 5.0 goals through laser based additively Manufactured High-Performance Metals

Nyamekye,

Westman,

Tepponen

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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In the current industry 4.0 (I4.0) and emerging Industry 5.0 (I5.0) eras, the integration of digitalization and sustainable goals is driving nature-centric and human-centric products. The production of these products are also creating digital supply chain that boosts agility and resiliency in operations, management, and supply chain interlinks. Additive manufacturing (AM), an element of I4.0, for instance, offers an integrated connection of all process steps and value chains using computer-based designing, data-driven simulations, cloud-based processing, storing, and managing software along seamless digital threads to create agile and resilient supply chain. The integration of AM and other I4.0 technologies enables greater flexibility offering intrinsic sustainable, human-centric and resilience advantages. Laser based additive manufacturing (LBAM), one of the subcategories of AM, offers opportunities to manufacture new intricate and conventionally impractical metal product designs in an ecological and economic competitive stance. High performance metals (HPMs) suited for high-stress and corrosive demanding applications are tough to machine and prone to thermal cracking in case of welding. Conventionally difficult and lengthy to manufacture yet unavoidable HPMs such as nickel alloys and titanium alloys are effortlessly possible via LBAM. Different industrial sectors that utilize these grades of metal alloys continue to adopt AM for the offered design flexibility for achieving goals such as customization, lightweight, on-demand manufacturing, raw material efficiency and cost saving. This study uses literature review and manufacturing case studies to demonstrate the flexibility and digital nature of LBAM towards I5.0 goals. The study objectively highlights the promising responsiveness of AM in the eventuality of supply disruptions that may be caused by sudden changes. The novelty of the study lies in the pragmatic emphasis on the potentials of LBAM and paired I4.0 technologies in revolutionizing the industry towards industry 5.0 goals. The study shows how I4.0 elements can be paired to enable operational efficiencies, lower carbon emissions, and foster sustainability in promoting I5.0 transformation. This study offers a fundamental understanding of the role of LBAM in the advancement of sustainability, human-centricity, and resilience.

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Section: Discussionmentioning

confidence: 99%

Section: Benefits and Challenges Of Am For High-performance Metalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing industry 5.0 goals through laser based additively Manufactured High-Performance Metals

Nyamekye,

Westman,

Tepponen

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The GA is a computational method employed for optimizing process parameters within both conventional manufacturing industries [65] and in the realm of FDM. Specifically, it played a pivotal role in optimizing a myriad of process parameters.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

Batu,

Lemu,

Shimels

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Manufacturing is the foundation of any industrialized country and involves making products from raw materials using various processes. Additive manufacturing (AM) was originally created as a method for swift prototyping, allowing the visualization, testing, and validation of a design prior to final production for end-users. FDM is the most commonly used additive manufacturing process for constructing products and prototypes. It encompasses numerous process parameters that impact the quality of manufactured products. Properly selecting these process parameters is crucial for producing products at a lower cost while enhancing mechanical properties, build time, and part quality, among other factors. Therefore, in the past, researchers have optimized the process parameters to achieve the desired product outcomes. In the present study, we provide an overview of FDM process parameters and review various design optimization methods. We present several experimental designs, such as the Taguchi method, response surface methodology, and design of experiments, as well as computational approaches like artificial intelligence, and machine learning.

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“…Application: Production systems. Gong et al [38] Chen et al [35] Zhou et al [15] Zhao et al [10] Sun et al [39] Leirmo et al [40] Theory: evolutionary computation, evolutionary algorithms for product design, genetic algorithm for production scheduling. Methods: product gene and evolutionary algorithm, genetic algorithm.…”

Section: Table 1 Categories Of Bionics In Product Developmentmentioning

confidence: 99%

Digital Twin Bionics: A Biological Evolution-Based Digital Twin Approach for Rapid Product Development

et al. 2021

IEEE Access

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With intensified market competition, product development procedure is accelerated, requiring rapid product innovation and efficient collaboration between design and manufacturing. However, there still exist information islands, prohibiting integration of product life cycle processes. To address this issue, bionics and digital twin (DT) are combined as a potential solution. The concepts, framework, and features of digital twin bionics (DTB) is originally proposed, with co-evolution mechanism of product-twins (including virtual and physical products) and production-twins (including virtual and physical production) elaborated. A symbiotic co-evolution mechanism is presented to integrate the processes of product development and manufacturing. In detail, the supporting technologies, such as industrial internet of things, cloud edge computing, big data, and artificial intelligence, as well as the potential key technologies, are illustrated. A case study for rapid development of automotive body-in-white in an industrial robotics welding production line is investigated to verify the applicability of the proposed framework. The results suggest that the integration of bionics and DTs can accelerate the innovations and developments of new products and management the process of production construction.

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Evolutionary algorithms in additive manufacturing systems: Discussion of future prospects

Cited by 13 publications

References 59 publications

Enhancing industry 5.0 goals through laser based additively Manufactured High-Performance Metals

Enhancing industry 5.0 goals through laser based additively Manufactured High-Performance Metals

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

Digital Twin Bionics: A Biological Evolution-Based Digital Twin Approach for Rapid Product Development

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