When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development

Liu, Chenang; Tian, Wenmeng; Chen, Kan

doi:10.1016/j.jmsy.2022.04.010

Cited by 47 publications

(11 citation statements)

References 137 publications

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“…Overall, the use of AM and other I4.0 technologies is an important aspect of I5.0, which is driving the evolution of manufacturing towards more efficient, agile, and personalized production. The capabilities of I4.0 technologies including simulation, AI, CPS are promising in providing secured timely picture of ongoing work with real-time detection and responding to manufacturing anomalies for increasing effectiveness, quality, and accurate forecasts [97,98].…”

Section: Discussionmentioning

confidence: 99%

“…AM induces a learning custom in its users as conventional manufacturing workflows are not applicable. LBAM in driving human-centricity offers means to create high valued individualized HPM to fit specific needs of application, particularly medical applications [97]. Examples by enabling the production of patient-specific metal implants and prosthetics in the medical industry.…”

Section: Laser Based Additive Manufacturing Contribution To Human-cen...mentioning

confidence: 99%

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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: Laser Based Additive Manufacturing Contribution To Human-cen...mentioning

confidence: 99%

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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“…Another example could be bioengineering. In recent times, additive manufacturing (AM) has been widely used to create things specifically designed for human use, including orthoses, prostheses, therapeutic helmets, finger splints, and other customised devices (Liu et al, 2022). Product design is a domain that shows significant potential for application, where GAI might serve as a crucial tool for exploring and optimising design spaces (Di Vaio et al, 2020).…”

Section: Application Of Generative Ai In the Design Of New Productsmentioning

confidence: 99%

Generative AI in the Manufacturing Process: Theoretical Considerations

Doanh,

Dufek,

Ejdys

et al. 2023

Engineering Management in Production and Services

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The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.

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“…Currently, artificial intelligence is increasingly involved in the decision-making stages of 3D printing product development, including design, manufacturing, and evaluation, bringing new opportunities for cost-effective product development. 12 However, due to the influence of process repeatability in AM technology, selecting optimal process parameters to obtain robust and reliable quality characteristics poses a challenge. The academic community has proposed two mainstream approaches to address this challenge.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method

Ding,

Wang,

et al. 2024

Quality & Reliability Eng

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The instability of product quality and low printing efficiency are the main obstacles to the widespread application of 3D printing in the manufacturing industry. Optimizing printing parameters can substantially improve product quality and printing efficiency. However, existing methods for optimizing process parameters primarily rely on computationally expensive numerical simulations or costly physical experiments, which cannot balance model accuracy and experiment cost. To the best of our knowledge, almost no relevant papers have been found to address the issues of product quality and printing efficiency in 3D printing from experimental data‐driven perspective. In this paper, we propose a method that integrates multiobjective Bayesian optimization (MOBO) with experimental data‐driven, aiming at obtaining more accurate optimization results at a lower cost. Distinguishing from previous studies, the proposed method utilizes experimental data instead of predicted values to update the model and find the optimal process parameters based on expected hypervolume improvement. The results of the 3D printing case study show that the proposed method can better model and optimize the highly fluctuating 3D printing process and obtain the optimal process parameters at a much lower cost. In addition, confirmatory experiments verify that the proposed method achieves higher printing efficiency while maintaining product quality.

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When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development

Cited by 47 publications

References 137 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

Generative AI in the Manufacturing Process: Theoretical Considerations

Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method

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