A Decision-making Methodology Integrated in Product Design for Additive Manufacturing Process Selection

Purpose: The decision-making for additive manufacturing (AM) process selection is typically applied in the end of the product design stages based upon an already finished design. However, due to unique characteristics of AM processes, the part needs to be designed for the specific AM process. This requires potentially feasible AM techniques to be identified in early design stages. This paper aims to develop such a decision-making methodology that can seamlessly be integrated in the product design stages to facilitate AM process selection and assist product/part design. Design/methodology/approach: The decision-making methodology consists of four elements, namely, initial screening, technical evaluation and selection of feasible AM processes, re-evaluation of the feasible process and production machine selection. Prior to the design phase, the methodology determines whether AM production is suitable based on the given design requirements. As the design progresses, a more accurate process selection in terms of technical and economic viability is performed using the analytic hierarchy process technique. Features that would cause potential manufacturability issues and increased production costs will be identified and modified. Finally, a production machine that is best suited for the finished product design is identified. Findings: The methodology was found to be able to facilitate the design process by enabling designers to identify appropriate AM technique and production machine, which was demonstrated in the case study. Originality/value: This study addresses the gap between the isolated product design and process selection stages by developing the decision-making methodology that can be integrated in product design stages

Investigation of part distortions as a result of hybrid manufacturing

In today's society, the customer driven markets has led to the rapid developments and continuous evolution of manufacturing technologies. The ability to accurately produce complex parts, and to reuse and remanufacture used parts is becoming more prevalent, requiring more efficient and rapid methods to be developed. One such method being currently developed is the hybrid process combining additive, subtractive and inspection processes for the manufacture of complex part geometries from any given raw material in terms of shape and size. A major element of the hybrid process is decomposition of a part into a number of subparts, which are additively manufactured and machined in sequence. However, the residual stresses resulting from the temperature difference between the solidified material (i.e. already manufactured subparts) and the material being deposited (i.e. new subparts being built) leads to part distortions, which significantly reduces the dimensional accuracy of finished parts. This study investigates part distortions that take place in the additive manufacturing process under the context of hybrid manufacturing. A method for conducting this investigation was first proposed. A mathematical model was developed, identifying the influential parameters that contribute to part distortions. These parameters were then incorporated in the experimental design by employing the Taguchi design of experiments strategy. Distortion behaviour arising from thermally induced stress was experimentally explored, indicating that part length, height, and layer thickness have significant effects on part distortions.</p

A new algorithm for build time estimation for fused filament fabrication technologies

The manufacture of highly complex and accurate part geometries with reduced costs has led to the emergence of hybrid manufacturing technologies where varied manufacturing operations are carried out in either parallel or serial manner. One such hybrid process being currently developed is the iAtractive process, which combines additive (i.e. fused filament fabrication, which is sometimes called fused deposition modelling. However, the latter term is trademarked by Stratasys Inc. and cannot be used publicly without authorisation from Stratasys) and subtractive (i.e. computer numerical control machining) processes. In the iAtractive process production, operation sequencing of additive and subtractive operations is essential. This requires accurate estimation of production time, in which the fused filament fabrication build time is the determining factor. There have been some estimators developed for fused deposition modelling. However, these estimators are not applicable to hybrid manufacturing, particularly in process planning, which is a vital stage. This article addresses the characteristics of fused filament fabrication technologies and develops a novel and rigorous method for predicting build times. An analytical model was first created to theoretically analyse the factors that affect the part build time and was subsequently used to facilitate the design of test parts and experiments. The experimental results indicate that part volume, interaction of volume and porosity and interaction of height and intermittent factor have significant effects on build times. Finally, the estimation algorithm has been developed, which was subsequently evaluated and validated by applying a wide range of identified influential factors. The major advantage of the new proposed algorithm is its ability to estimate the build time based on simple geometrical parameters of a given part. The key factors that drive the algorithm can be directly obtained from part dimensions/drawings, providing an efficient and accurate way for fused filament fabrication time estimation. Test part evaluations and analysis have clearly demonstrated that estimation errors range from 0.1% to 13.5%, showing the validity, capability and significance of the developed algorithm and its applications to hybrid manufacture. </jats:p

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage