Conventional machining process like milling, drilling, rolling, forming and so on, are still the most common go-to manufacturing operations for medium to large volume production. Additive manufacturing (AM) has been disrupting the manufacturing environment in the past decade with new possibilities for customised small volume production. Optimised manufacturing, for example, is very difficult to achieve with conventional machining processes, whereas with the help of additive manufacturing, this is easy with relatively fewer tooling changes, and much less time involved with the manufacturing operation.

Additive manufacturing comes with its own set of challenges with the kind of manufacturing technology used, namely vat polymerization, powder bed fusion, material extrusion, material jetting, binder jetting, direct energy deposition or sheet lamination. With post-processing improvements in additive manufacturing, there have been significant leaps in manufacturing capabilities, turning it from a prototyping methodology to finished-product method.

As more well-established equipment manufacturers are looking to capitalise on additive manufacturing, with proprietary machines and software, it becomes increasingly important to evaluate currently available manufacturing options to optimally produce products efficiently and to the required specifications and tolerances of the job, or the needs of the customer. This will have more of a profound impact from a designer’s perspective. Seasoned designers’ are aware of conventional production layout and machine specifications which allows them to implement Design for Assembly (DfA), Design for Manufacturability (DfM), Design for sustainability (DfS) principles or any other verticals of DfX with relative ease because they are already familiar with the standard practices. Unfortunately, the current standards for Design for Additive Manufacturing (DfAM) are still under development. Designers with intricate knowledge of manufacturing technology and machine specifications used, can develop products faster than a novice designer.

At University College Dublin, Laboratory for Advanced Manufacturing Simulations and Robotics (UCD-LAMS), we are developing projects deploying technologies with advanced analytics and feedback, including augmented reality (AR) visualization for improved designer feedback. Our research explores assessing geometric complexity and how this could be used to build predictive models to determine the production technique and process configurations required to optimally build a part. The goal is to bridge the existing knowledge gap between designers and AM machine specifications and capabilities currently available in the market.     

Currently, UCD-LAMS is engaged in conducting experiments with assessing the complexity of parts, to evaluate which additive manufacturing process configurations are best suited for building the part. Progressing from material extrusion, our know-how is currently being extended into metal additive manufacturing technologies. The experimental data are analysed in-house with regression modelling and machine learning approaches to devise a strategic model capable of predicting AM machines and process configurations to effectively build the part. UCD-LAMS is also exploring process automation measures to cycle through different process configurations, which otherwise would typically take a designer, hours of tedious and monotonous work to complete, to just a matter of few mins to give the designer, an analysis based result. Finally, augmented reality (AR) is used to help the designer in the process of designing products with ergonomics and product aesthetics principles in mind. This is facilitated by testing a computer-generated design model in a mixed virtual / real-world environment, so that the viability of a design be validated.

Figure. Depicting a colour map for geometric deviation for a test part

Practical impacts

In summary, the advantages of employing these new digital manufacturing approaches include:

• Designers can assess a part complexity to decide on the best AM process configuration to build that part.
• Evaluate which manufacturing technology, additive or conventional to deploy.
• Improved design workflow.
• Process automation of monotonous work enables designers to save time in the design cycle.
• Provide a better awareness of current production capabilities.
• Optimise build with appropriate process configurations.

References:

1. N. Papakostas, A. Newell, V. Hargaden, A novel paradigm for managing the product development process utilising blockchain technology principles, CIRP Annals, Volume 68, Issue 1, 2019, pp. 137-140
2. A. Newell, A. George, N. Papakostas, H. Lhachemi, A. Malik and R. Shorten, On Design for Additive Manufacturing: Review of Challenges and Opportunities utilising Visualisation Technologies, 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Valbonne Sophia-Antipolis, France, 2019,
3. A. Newell, A. George, N. Papakostas, Product Lifecycle Management Strategies Focusing on Additive Manufacturing Workflow, 17th International Conference on Manufacturing Research (ICMR), Belfast, UK, 2019,
4. A. George, A. Newell, N. Papakostas, Intellectual Property Protection and Security in Additive Manufacturing, 17th International Conference on Manufacturing Research (ICMR), Belfast, UK, 2019,