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Michael Crockett

Industrial Manufacturing & 3D Printing: A Path Forward

Most recent investment during the boom in 3D printing have targeted reducing print time, improving perceived quality, and unlocking new potential commercial applications. These are all required elements for generating the hype needed to inspire adventurous customers to try the technology, but I do not believe this alone has succeeded in keeping these customers engaged and satisfied. The barrier of entry to use 3D printing has declined, but the complexity and effort required has not.

Successful integration of additive manufacturing processes for the long haul will at minimum require increased investment in improved convenience, realistic assessment for intended application, and reasonable generative part design and optimization.

In this entry I’m going to describe some challenges facing the adoption of 3D Printing technologies in industrial manufacturing environments including

  • MRP/ERP Integration
  • Big-O Manufacturing Analysis
  • Generative Geometry

Successful integration of additive manufacturing processes for the long haul will at minimum require increased investment in improved convenience, realistic assessment for intended application, and reasonable generative part design and optimization. Continue reading further if you are interested in learning more of the details. Original article posted here, full article posted at my blog.

1. Improved Convenience | MRP/ERP Integration

Today’s production and warehouse environments are driven from all angles to become as lean as possible to minimize operation costs and risk while maintaining tolerable production rates, safety margins, and quality control. On the warehouse side, lean means maintaining only the minimum required inventory, and on the production side lean means the minimum number of people who need to interact with a fabrication or assembly process.

MRP Systems are designed to systematically assess the purchasing, fabrication, warehousing, and assembly needs of a production environment on a quantized iterative basis from immediate and forecasted order demand volume. Production environments staying in business today need some level of robust and reasonably accurate MRP coordination to remain profitable in the face of growing global competition.

An MRP/ERP integration layer between 3D Printing and commercial MRP/ERP systems will significantly reduce many of the non-value added aspects of the 3D printing toolchain.

3D Printing is a fabrication process that is at the core versatile and automated. However, most of the 3D printing ecosystem is fragmented and generation of print job machine instructions, print job scheduling, and printer maintenance is a manual and tedious process requiring attention to details only expert-level users can support. Hours and effort spent sustaining these machines, preparing print jobs, and debugging problems is what is known as “non-value added” labor in a manufacturing environment, e.g. an unnecessary cost, complication, and even a potential source of costly errors or delays. What would help is an automated demand-driven scheduling of print job tray machine instructions, maintenance orders, and feedmaterial supply orders. Below is a detailed system-block level conceptual layout for how an efficient and robust MRP/ERP system can integrate with an existing microservice architecture 3D Printing Platform.

Overall System Block Diagram Layout: External Business MRP/ERP links to Scheduler and Database Apps via API

Detailed Core Integration using MRP Middleware

Graceful Fault-Handling of Scheduling or Maintenance Errors

MRP/ERP integration is one the keys to enabling convenient access to the growing number of advanced technologies and applications 3D printing has provided. In addition to this, automated MRP unlocks the high-degree of flexibility and customization inherent in the 3D printing process, thus enabling the most efficient utilization of print farms like the one shown below. An MRP/ERP integration layer between 3D Printing and commercial MRP/ERP systems will significantly reduce many of the non-value added aspects of the 3D printing toolchain.

2. Realistic Assessment | Big-O Manufacturing Analysis

Assessment for 3D printing as a primary manufacturing option can be quickly determined to be viable or not by looking first at part size. Big O Analysis is a technique that computer scientists use to theoretically compare the performance of algorithms under worst-case situations as they are pushed to their computational limits, and Big-O Notation is means of describing algorithm performance by mathematically describing their limiting behavior as its inputs tend toward a particular value, or infinity (e.g. datasets of increasing size).

Extrapolation to Manufacturing

Below is my attempt to extrapolate Big-O Analysis for computational algorithmic efficiency to industrial manufacturing processes for parts of dimensions L [length] x W [width] x D [depth]. Since each manufacturing technology has varying degree of significant working dimension (e.g. laser cutting a sheet is only a function of L x W, while 3D printing is a function of L x W x D) I will be representing all significant working dimensions with the variable “n” without loss of extrapolation generality. More details on the assumptions, process, and resulting table of Big-O Manufacturing Complexity Functions here.

Assessing the implication of part size becomes increasingly important for manufacturing processes with complexities of higher order. This is especially true for 3D printing processes.

For Example: take a part of size n=10 running on a FDM 3D printing process. This complexity result is n³ = 1000. Now take that same part of size n=10 on an FDM 3D printing process where the machine operates twice as fast. There are still n³ operations, but each operation is twice the speed. The result is a complexity of 500. Compare that to a machining process, n² log(10)=100. Despite the investment in a 3D printer twice as fast, FDM 3D Printing is still only 20% the output rate compared to using conventional machining. How about making that same n=10 sized part using progressive stamping? Progressive stamping has a Big-O Complexity of O(n)=n, and thus the resulting complexity is 10. Theoretically, progressive stamping takes only 2% of the time required for the 2x speed 3D Printer to produce a part of size n=10. To put in other words, progressive stamping will have completed 50 parts in the time it takes the 2x speed FDM 3D printer to complete a single part of size n=10! Assessing the implication of part size becomes increasingly important for manufacturing processes with complexities of higher order. This is especially true for 3D printing processes.

It is critically important to assess the overall size of the part before deciding on manufacturing process to design for. For smaller sized parts, nearly every manufacturing option is equivalent in complexity. However, when you get to larger part sizes the field of selection for reasonable manufacturing processes quickly thins out. The Strati 3D printed car is a clear example of a poor decision for manufacturing options based on part size. Despite the increase in plastic bead width and layer height, the manufacturing process is still of n³ complexity that does not scale well for larger parts.

3. Reasonable Design | Generative Geometry

Despite the drawbacks evident in the production of large parts using 3D printing technologies, there are certain areas where 3D printing can excel in an industrial manufacturing environment based on their improved performance compared to conventional manufacturing options. These are realizable when the production rates are within an allowable range for the need of high performance parts. Take for example the below antenna mounting structure for an aircraft. The conventional manufacturing process using multi-axis machining yields a lightweight part, but the resulting part is not as light as theoretically possible for the component’s functional constraints.

Using generative design techniques software can aid designers in determining optimum part shape based on physical constraints (e.g. boundary conditions, allowable deflections, maximum loads, vibration response, etc.). For the production volumes needed for this component, additive manufacturing provides both the means to enable the described shape and a production rate that meets the demand requirements.

Integrating UI/UX handles on the rendered generative geometry can provide user input and modifications to further tune the resulting generative part towards a solution viable based on constraints the software cannot perceive. Components can be imported into view, constraints and boundary conditions can be specified, and then the computational system is run to generate a part that satisfies the first pass requirements. The user can then visualize the result and manually adjust parameters using UI/UX tools to adjust the generative part to perfection. This last step is also a potential opportunity for machine learning to feedback desired design characteristics into the generative algorithms.

Optimize Internal Geometry for Additive Fabrication Performance:

Another area key for resulting part improvement using generative geometry is algorithmic development of optimum internal structure to improve the functional requirements of the part, all while reducing weight, material used, and also print time. Adjusting internal geometry can result in parts with improved strength at reduced weights when compared to existing conventional manufacturing processes. Strength should be optimized by placing more material along outer shells, and support should progressively become less dense towards interior.

Load bearing areas should have special attention given to load path axes for internal geometry optimization. Print preparation receives part geometry and desired indication for relative or absolute strengths (load conditions or specific regions for increased stiffness).

The user of internal geometry generative software should be able to quickly alternate between preview mode and detailed print mode to ensure viability of the determined internal geometry prior to printing, and if changes are necessary provide UI/UX tools to make manual adjustments. Primitive shapes can be superimposed on the part to define regions of influence (e.g. areas that require greater, or less stiffness that the algorithm can take into account when rendering internal features). More details on this process can be found on one of my earlier blog entries here, but if you take a look at the below images you can get an idea of the biological inspiration behind optimization of internal geometry as well as some industrial applications.

Summary

MRP/ERP Integration, Big-O Manufacturing Analysis, and Generative Geometry are critical to enabling sustained growth in Industrial 3D Printing.

Generative Geometry is off to a good lead due to existing investments, but MRP/ERP Integration and Improved Assessment using Big-O Manufacturing Analysis desperately need attention today. Parts not tailored for 3D printing at the production rates need should not be 3D printed, and non-value-added steps in the 3D printing toolchain need to be eliminated immediately because they introduce waste and human error that further drags the 3D printing industry behind its potential capacity.

If you are interested in learning more details on this or similar topics, please see my blog. If you’d like to discuss with me please find my contact information here.