Knowledge based process planning and design for Additive Manufacturing (KARMA)

Singh, Bhoopendrakumar; Sewell, N. T.

doi:10.1201/b11341-99

Cited by 4 publications

(5 citation statements)

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“…The knowledge engineering based approaches implemented the selection of AM processes based on the recommendation of expert systems or knowledge-based systems, which were developed using specific artificial intelligence techniques, such as database query, knowledge modelling, rule-based reasoning, and fuzzy reasoning. Representative examples are the expert systems of Bibb et al [13], Masood and Soo [14], Lan et al [15], Palmer [16], and Munguia et al [17], which were either developed based on rule-based reasoning, a combination of rule-based reasoning and fuzzy reasoning, or a combination of rule-based reasoning, fuzzy reasoning, and database query, and the knowledge-based systems of Bernard et al [18] and Singh and Sewell [19], which were both developed using the knowledge modelling technique. Compared to the database based approaches, the knowledge engineering based approaches can reduce the user involvement to some extent.…”

Section: Related Workmentioning

confidence: 99%

An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators

Qin

Scott

et al. 2020

Robotics and Computer-Integrated Manufacturing

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Selecting an appropriate additive manufacturing (AM) process or machine to fabricate an end-use product is an important issue in design for AM. One of many types of approaches for AM process selection is based on multi-criteria decision making (MCDM). Most of the MCDM based approaches have an advantage in taking into account the relative importance of performance parameter types and a few of them also consider the interrelationships of performance parameter types. Each of these approaches can work well in its specific context. They are however not entirely satisfactory, as they do not have the capabilities to reduce the influence of the deviation of performance parameter values on the decision-making result and to capture the risk attitudes of users in their decision-making models. In this paper, an MCDM approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators with such capabilities is proposed for AM process selection. A fuzzy Archimedean weighted power Bonferroni mean operator and a fuzzy Archimedean weighted power geometric Bonferroni mean operator are firstly constructed. Based on these operators, an MCDM approach for selection of AM processes are then developed. After that, four practical examples are adopted to illustrate the developed approach and a set of sensitivity analysis experiments on the basis of these examples are carried out. Finally, qualitative and quantitative comparisons between the approach and the existing MCDM based approaches are reported to demonstrate its feasibility, effectiveness, and advantages.

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Section: Related Workmentioning

confidence: 99%

An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators

Qin

Scott

et al. 2020

Robotics and Computer-Integrated Manufacturing

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“…Weighted rating and similar (Chuk and Thomson 1998;Jones and Campbell, 1997;Junioret al, 2014;Robersonet al, 2013;Ghazy, 2012;Singh and Sewell, 2012…”

Section: Weighting Values or Goal Values Or Lowest Acceptable Valuesmentioning

confidence: 81%

“…Some databases or rule repositories host the data of some common attributes, e.g. build envelope and material properties (Lan et al, 2005;Munguia et al, 2010;Ghazy, 2012;Singh and Sewell, 2012). A benchmark database was used in (Mahesh et al, 2005).…”

Section: Performance Evaluationmentioning

confidence: 99%

Selection of additive manufacturing processes

Wang

Blache

2017

RPJ

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Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

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“…A knowledge-based process planning tool for AM has been proposed by Singh and Sewell (2012) and Munguia et al (2011). By providing a technology and processing materials guide, the approach aims to enable AM users to assess the capabilities of AM technologies, materials and build scenarios given a finalized 3D design.…”

Section: Evaluation Process Selection and Planningmentioning

confidence: 99%

Animaris Geneticus Parvus

Jansen¹,

Doubrovski

Verlinden

2014

Rapid Prototyping Journal

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Purpose -This paper investigates how designers exploit the full potential of additive manufacturing (AM). AM yields a broad range of advantageous properties including the possibility to fabricate mechanical multi-body structures. Design/methodology/approach -This case study explores the possibilities and limitations in designing mechanical multi-body structures for AM, focused on the development of a selective laser sintering (SLS) version of Theo Jansen's "Strandbeest" walking mechanism, dubbed Animaris Geneticus Parvus (AGP). We discuss the design process and considerations involved and attempt to distill design guidelines. Findings -Novel structural solutions were developed to enable SLS fabrication of the AGP, specifically cross-shaped pivot pins, increased clearance between bodies, spacing studs, restricting axial play with pins, partial disassemblies and increased clearance around extremities. The result is a functioning walking mechanism of 74 components can be fabricated at once without human intervention. Research limitations/implications -This article represents a case study; although it does mention adapted design rules for SLS, its greatest contribution is the holistic approach -to integrate a number of engineering challenges in one prototypical manifestation. Practical implications -Part consolidation by AM could bring great benefits in future product design applications. The findings show that complex multi-body mechanical structures with more than 70 elements are feasible by AM without assembly. This presents new business opportunities for AM service bureaus and novel product opportunities for designers. Originality/value -As a case study, this article provides inspiration of the mechanical complexity beyond regular products -from original idea to end result. For researchers, key contribution is the approach in obtaining design optimization strategies which provides engineering designers with a new language to consider SLS.

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Knowledge based process planning and design for Additive Manufacturing (KARMA)

Cited by 4 publications

References 0 publications

An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators

An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators

Selection of additive manufacturing processes

Animaris Geneticus Parvus

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