scholarly journals Additive Manufacturing in SMEs: Empirical Evidences from Italy

2018 ◽  
Vol 15 (01) ◽  
pp. 1850007 ◽  
Author(s):  
Giacomo Marzi ◽  
Lamberto Zollo ◽  
Andrea Boccardi ◽  
Cristiano Ciappei
Keyword(s):  
Additive Manufacturing ◽  
Production Process ◽  
Growth Strategy ◽  
Positive Outcomes ◽  
Innovative Technique ◽  
Gold Jewelry ◽  
Managerial Implications ◽  
And Performance ◽  
Technological Methods ◽  
Latent Content

Research on innovative technological methods in SMEs’ production processes is progressively receiving attention. However, little is known about the emerging phenomenon of additive manufacturing (AM), which may represent a significant strategic lever for fostering a company’s competitiveness and performance, especially for SMEs. Our aim is to investigate the effects of AM on SMEs’ production process, in order to better understand the relative outcomes of such an innovative technique. We used latent content analysis for empirically analyzing SMEs present in one of the most important Italian gold jewelry districts. Our findings suggest that the AM introduction in a company’s production process effectively results in many positive outcomes, such as process innovation, customer satisfaction, costs, revenues, profits, and competitive advantage. Specifically, there is a positive linkage between AM and a company’s performance. Hence, such an innovative technique may be interpreted as a viable growth strategy for SMEs. Theoretical and managerial implications are discussed.

scholarly journals SURFACE ROUGHNESS CONSIDERATIONS IN DESIGN FOR ADDITIVE MANUFACTURING - A LITERATURE REVIEW

2021 ◽  
Vol 1 ◽  
pp. 2841-2850
Author(s):  
Didunoluwa Obilanade ◽  
Christo Dordlofva ◽  
Peter Törlind
Keyword(s):  
Surface Roughness ◽  
Additive Manufacturing ◽  
Literature Review ◽  
Future Research ◽  
Reduction Potentials ◽  
Research Fields ◽  
Post Process ◽  
Accessibility Issues ◽  
End Use ◽  
And Performance

AbstractOne often-cited benefit of using metal additive manufacturing (AM) is the possibility to design and produce complex geometries that suit the required function and performance of end-use parts. In this context, laser powder bed fusion (LPBF) is one suitable AM process. Due to accessibility issues and cost-reduction potentials, such ‘complex’ LPBF parts should utilise net-shape manufacturing with minimal use of post-process machining. The inherent surface roughness of LPBF could, however, impede part performance, especially from a structural perspective and in particular regarding fatigue. Engineers must therefore understand the influence of surface roughness on part performance and how to consider it during design. This paper presents a systematic literature review of research related to LPBF surface roughness. In general, research focuses on the relationship between surface roughness and LPBF build parameters, material properties, or post-processing. Research on design support on how to consider surface roughness during design for AM is however scarce. Future research on such supports is therefore important given the effects of surface roughness highlighted in other research fields.

scholarly journals A photo-reversible crosslinking resin for additive manufacturing: reversibility and performance

RSC Advances ◽  
2020 ◽  
Vol 10 (72) ◽  
pp. 44323-44331
Author(s):  
Hanyu Xue ◽  
Xinzhong Li ◽  
Jianrong Xia ◽  
Qi Lin
Keyword(s):  
Additive Manufacturing ◽  
And Performance

Improving the adhesion between layers and achieving the recycling of resins are challenges in additive manufacturing (AM) technology.

APPLYING ANSOFF’S GROWTH STRATEGY MATRIX TO INNOVATION CLASSIFICATION

2018 ◽  
Vol 22 (04) ◽  
pp. 1850039
Author(s):  
TUGBA GURCAYLILAR-YENIDOGAN ◽  
SAFAK AKSOY
Keyword(s):  
Industrial Clusters ◽  
Performance Outcomes ◽  
Growth Strategy ◽  
Production Performance ◽  
Market Growth ◽  
Innovation Activities ◽  
Firm Innovativeness ◽  
Innovation Capacity ◽  
And Performance ◽  
Manage Innovation

This study aims to determine innovation capacity of a firm and to investigate the correlations between performance outcomes and innovation types. In this study, a questionnaire-based survey was conducted to classify firms with respect to different novelty degrees of innovation activities in developing new products and the magnitude of market impact shortly after innovations have been introduced and then appraise the association between innovation types and performance outcomes. The data obtained from the Turkish industrial clusters show that the higher firm innovativeness in product and market with a wide-spread diffusion effect of innovations, the greater is the market and production performance. To the best of our knowledge, this study is one of the few studies applying the product-market growth matrix to determine/manage innovation portfolio of firms.

Automatic Parametric Modeling From Non-Feature Based Designs for Additive Manufacturing

2021 ◽  
Author(s):  
Xinyi Xiao ◽  
Byeong-Min Roh
Keyword(s):  
Additive Manufacturing ◽  
3D Models ◽  
Parametric Modeling ◽  
Parametric Models ◽  
Performance Simulation ◽  
Cad Modeling ◽  
3D Cad ◽  
Feature Based ◽  
And Performance ◽  
The Given

Abstract The integration of Topology optimization (TO) and Generative Design (GD) with additive manufacturing (AM) is becoming advent methods to lightweight parts while maintaining performance under the same loading conditions. However, these models from TO or GD are not in a form that they can be easily edited in a 3D CAD modeling system. These geometries are generally in a form with no surface/plane information, thus having non-editable features. Direct fabricate these non-feature-based designs and their inherent characteristics would lead to non-desired part qualities in terms of shape, GD&T, and mechanical properties. Current commercial software always requires a significant amount of manual work by experienced CAD users to generate a feature-based CAD model from non-feature-based designs for AM and performance simulation. This paper presents fully automated shaping algorithms for building parametric feature-based 3D models from non-feature-based designs for AM. Starting from automatically decomposing the given geometry into “formable” volumes, which is defined as a sweeping feature in the CAD modeling system, each decomposed volume will be described with 2D profiles and sweeping directions for modeling. The Boolean of modeled components will be the final parametric shape. The volumetric difference between the final parametric form and the original geometry is also provided to prove the effectiveness and efficiency of this automatic shaping methodology. Besides, the performance of the parametric models is being simulated to testify the functionality.

scholarly journals A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Sheng Hu ◽  
Shuanjun Song ◽  
Wenhui Liu
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Control Method ◽  
Cloud Model ◽  
Data Driven ◽  
Monitoring Method ◽  
Control Approach ◽  
Acceptable Method ◽  
And Performance ◽  
Model Similarity

Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.

An Investigation Into the Driving Factors of Creativity in Design for Additive Manufacturing

2017 ◽  
Author(s):  
Michael Barclift ◽  
Timothy W. Simpson ◽  
Maria Alessandra Nusiner ◽  
Scarlett Miller
Keyword(s):  
Additive Manufacturing ◽  
Self Report ◽  
Risk Attitudes ◽  
Design For Additive Manufacturing ◽  
Domain Specific ◽  
Common Framework ◽  
And Performance ◽  
Student Risk ◽  
Prior Experiences ◽  
Design Freedom

Additive manufacturing (AM) provides engineers with nearly unlimited design freedom, but how much do they take advantage of that freedom? The objective is to understand what factors influence a designer’s creativity and performance in Design for Additive Manufacturing (DFAM). Inspired by the popular Marshmallow Challenge, this exploratory study proposes a framework in which participants apply their DFAM skills in sketching, CAD modeling, 3D-Printing, and a part testing task. Risk attitudes are assessed through the Engineering Domain-Specific Risk-Taking (E-DOSPERT) scale, and prior experiences are captured by a self-report skills survey. Multiple regression analysis found that the average novelty of the participant’s ideas, engineering degree program, and risk seeking preference were statistically significant when predicting the performance of their ideas in AM. This study provides a common framework for AM educators to assess students’ understanding and creativity in DFAM, while also identifying student risk attitudes when conducting an engineering design task.

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