Key Takeaways
- Software tools like Moldex, Sigmasoft, and mould flow simulation have significantly advanced IM process development by predicting defects and cycle time before mould manufacturing, though optimization through DOE-based software remains limited to few players.
- Process revalidation remains a major cost burden because machine parameter transparency is insufficient to mathematically predict process windows when transferring moulds between different injection moulding machines.
- High-end machines can mask mould and process deficiencies through advanced feedback systems, making mould transfer to less sophisticated machines unpredictable and requiring physical revalidation at each converter site.
- Mould cooling optimization is a complex and under-researched frontier due to the large number of interacting variables including mould temperature, viscosity, cooling rate, material properties, and geometry making it difficult to isolate and control individual factors.
- No standardized design data book exists for cycle time optimization, leaving part design guidance largely experience-based and case-specific, representing a significant missed opportunity for FMCG brands to reduce time-to-market and production costs.
- Data analytics, FEA-based predictive modelling, and data mining are promising research directions that can reduce dependence on physical trials, improve process estimates, and bridge the gap between research and mainstream industry practice.
While software-based advisory procedures and cybernetic self corrective processes have advanced the mould-material-machine paradigm for plastic injection moulding, some constraints still remain in the trajectory towards further improvements and cost savings. Read on to know more.
IM process development has seen steady progress towards optimizing the mould-material-machine paradigm. Software-led advisory process developments, such as Moldex, Pro-E mould, and mould flow simulation have helped eliminate defects and predict cycle time accurately even before the mould is manufactured.
While working with EIPL, I have had the unique opportunity to explore the process and product design, and pursue research and optimization. The article is a conceptual outlook on what we see as promising branches for further research, factors that would further build bridges to unexplored fields.
The State of Injection Moulding Process Development Today
IM process development has seen steady progress towards optimizing the mould-material-machine paradigm. Software-led advisory process developments, such as Moldex, Pro-E mould, and mould flow simulation have helped eliminate defects and predict cycle time accurately even before the mould is manufactured. While these have been very useful and are now among the mainstream applications of the technology in injection moulding, only a few players have progressed to optimization of DOE-based software.
- Moldex
Software-led advisory tools such as Moldex and mould flow simulations help eliminate defects and predict cycle time before mould manufacturing. - Sigmasoft
Software such as Sigmasoft, Nautilus make it simpler to narrow down the process window, enables a good understanding of constraints and solutions available, and encourages a one-time set-up study to save time for any future process corrections. - cybernetic algorithms
Yet a step further are the cybernetic self-corrective process algorithms which build negative feedback loops to ensure that the process is always close to the ideal. Energy efficient moulding and sustainable moulding are the baseline goals of the technical managers in the industry.
Key Constraints Holding the Industry Back
Given these new methods at hand, what are currently the key constraints of the IM process development? What are the unique ways that we can deploy technological advances to save business costs? We attempt to provide a provoking dialogue through this article to answer these two questions.
A. The Process Revalidation Problem: Why Machine Transparency Matters
- NON-STANDARD PROCESS CONTROL/ CALIBRATION- REDUNDANT NEED FOR PROCESS REVALIDATION
There is a great deal of investment made by the customer on process revalidation i.e. to revalidate the process at the converter site to check the results at the mould maker’s site. From an idealistic standpoint, this is a physics problem. Yet it is wishful to attempt to calculate the process settings without physically loading the mould on a different machine.
Why Mould-to-Machine Transfer Remains Unpredictable
One of the multiple reasons we cannot do so is the unmapped effect of machine parameters and variables on the output of the machine. This is a very interesting area, although a non-transparent and under-researched one. We have used tools and software to predict the filling time and viscosity of the plastic. Although we can model and predict the achieved viscosity in the barrel, we are yet to arrive at definitive answers to mathematically model and predict the different barrel types, screw types, back pressures, and RPM, along with barrel temperatures to predict the viscosity achieved by the material ahead of the screw.
If we were able to do so for a barrel, we could request the IMM OEMs to share the 3D models for the barrel and make a mathematical calculation to predict the process parameters – the process window – for a machine that is completely new vis-a-vis one on which the process has been previously proven, established, or developed.
For example, the high-end IMM machine which has excellent feedback circuits and process control, the sophistication of gathering and representing process data on the IMM interface, and the ability to control process points among many methods of IM process control, is desirable for every moulding shop. However, it is not so easy to transfer a mould from a high-tech IMM to one with lower capabilities as the capabilities of the IMM mask and compensate for issues with the mould, cooling circuit design, or other lacunas in the mould, process, or even the material.
Thus, transparency and data availability for IMM variables and overall parameters, including the mechanisms of the machine, is key and not just the information provided on the HMI. The IMM remains specialized equipment, similar to the backend of software with little information available to the end user for modification. IMM manufacturers do realize it to be a key to a higher market share and have been the torch bearers of the technological shifts.
EIPL’s Two-Pronged Research Approach
We, at EIPL have a two-pronged approach toward research in this area-
- Data analysis and establishing data mining mechanisms, data-generating platforms, and thus the subject matter pruned algorithms to make the best process estimate without making the mould
- FEA-based predictive models
B. Mould Cooling Circuit Design: An Under-Optimised Research Frontier
- RESEARCH ON THE RELATIONSHIP BETWEEN MOULD COOLING CIRCUIT DESIGN AND ACTUAL PLASTIC PROPERTIES.
Mould cooling channel design and cooling efficacy are among the most well-researched fields in the industry. With path-breaking advancements such as conformal cooling and simulation-assisted design, the customer is able to pay for and get the optimal cooling channel design that greatly affects the mould temperature and overall CT.
Variables That Complicate Cooling Optimisation
As we are painfully made aware that viscosity in the mould is a function of mould temperature, injection speed, starting viscosity in the barrel, and plastic temperature. Also, plastic product properties are a function of plastic shrinkage, viscosity, rate of cooling, and hinge design. The number of dependent and independent variables involved makes it complicated to research the area of cooling optimization.
The difficulty to amplify the input/output of controlled and uncontrolled variables makes cooling channel optimization an unenvious research topic to pursue. The resolution of any of these constraints will for sure bring a coveted technological advantage to the mould maker.
Where Data Analytics Can Help
There have been several attempts to use data analytics and mathematical modelling-assisted decision-making to determine the thicknesses of steel parts. Yet, we are not close to making the technology mainstream enough to be used by every tool maker. The industry is yet to perfect the trade-off between efficiency and overdesign that leads to aesthetic issues.
Such type of design education and the establishment of affordable and effective methodologies will plateau in the coming decade. EIPL has certain data-oriented projects that aim at helping the effort.
C. Cycle Time Optimisation: The Missing Design Data Book
- PART DESIGN OPTIMIZATION FOR CYCLE TIME
Multiple organizations specialize in part design optimization for the medical and FMCG industries. Though optimizing part design for CT (cycle time) remains a rather experience-based field.
Why Part Design Guidelines for CT Are Largely Absent
There is little empirical evidence suggesting methods to follow in order to achieve optimal results targeted for product designers. The brand and NPD industry would particularly benefit from the research that mould makers or clients conduct for mould design optimization if an integrated systems approach is leveraged across fields. The research available on the topic is case-based and not translated to design guidelines or readily usable engineering rules that designers can use as a parameter when starting a design process.
The Business Case for Design Education in FMCG
There are design data books available for mould-ability and aesthetics. We have advanced now to software that can help designers simulate any design defects. What we do not have yet is a design data book for a part design that provides design decision guidelines for a part that reaches a targeted CT.
From a business point of view, this would be a very profitable venture, to integrate and make sense of the existing research in the light of part design considerations. If we were to ask the mould makers, they would make many suggestions on the thickness of the part and CT. For example, CT from simulation or a particular part surface finish would help make the same part with lesser hold time, effectively reducing CT.
The brand is often married to the product design, missing out on a huge opportunity to save CT and incidentally a huge business cost. The FMCG industry’s time to market is almost always the focus. Therefore, design education is on the back burner. If the knowledge was to be converted to design data books for mould-ability, it would help designers optimize CT that impacts not only the design but also the business.
The Road Ahead: Bridging Research & Industry Practice
Efficient Innovations Pvt. Ltd. (EIPL) has been in the business of plastic injection moulding and the improving injection mould process for over decades now. We have also invested in material and processing research. Get in touch with us at www.efficientinnovations.in OR Write to us at radhika@efficientengg.com to know more about how we can help you use our literature study and some of our own research to deliver top-class moulding solutions.
Watch this space for the Hype cycle for innovations in IM.
FAQ: Injection Moulding Process Development
- What is injection moulding process development?
It involves optimizing the mould, material, and machine parameters to achieve defect-free parts, efficient cycle times, and consistent production quality. - What is the mould-material-machine paradigm in injection moulding?
It refers to the interaction between mould design, material behavior, and machine settings, which together determine the final product quality and process efficiency. - What software tools are commonly used for IM process development?
Tools like Moldex, Sigmasoft, Nautilus, and mould flow simulation software are widely used to predict defects, optimize parameters, and improve process reliability. - What is mould flow simulation and why is it important?
It is a virtual analysis of how molten plastic flows inside a mould. It helps predict defects, optimize design, and reduce trial-and-error before manufacturing. - Why does process revalidation need to be repeated at different converter sites?
Machine variations and lack of parameter transparency make it difficult to replicate results, requiring revalidation to ensure consistent output. - What makes it difficult to transfer a mould from one machine to another?
Differences in machine design, control systems, and processing parameters affect output, making direct transfer unpredictable. - How do high-end injection moulding machines mask mould or process deficiencies?
Advanced machines compensate for inconsistencies using feedback systems and precise control, hiding issues that may appear on less sophisticated machines. - What role does screw type and back pressure play in achieving target viscosity?
They influence material mixing, melting, and flow behavior, directly impacting viscosity and final part quality. - How does mould cooling channel design affect plastic part properties?
It controls cooling rate and temperature distribution, affecting shrinkage, strength, surface finish, and cycle time. - What is conformal cooling and how does it improve cycle time?
Conformal cooling uses channels that follow the mould shape, improving heat removal and reducing cycle time. - What variables make cooling optimisation so complex to research?
Multiple interacting factors like temperature, viscosity, cooling rate, material properties, and geometry make it difficult to isolate variables. - How does mould temperature affect viscosity during injection moulding?
Higher mould temperatures reduce viscosity, improving flow, while lower temperatures increase viscosity and resistance. - What is cycle time (CT) in injection moulding and what affects it?
Cycle time is the total time to produce one part. It is influenced by cooling time, material properties, mould design, and processing conditions. - How does part design impact cycle time in injection moulding?
Wall thickness, geometry, and surface finish affect cooling and processing time, directly influencing cycle time. - Why are design data books for cycle time optimisation not yet mainstream?
Most knowledge is experience-based and case-specific, with limited standardized guidelines for designers. - How can FMCG brands reduce time-to-market through part design optimisation?
By integrating moulding insights into design, brands can reduce cycle time, minimize iterations, and accelerate production. - What is a DOE-based software approach in injection moulding?
Design of Experiments (DOE) software helps systematically test variables to identify optimal process settings efficiently. - What are cybernetic self-corrective process algorithms in IM?
These are automated systems that use feedback loops to continuously adjust process parameters and maintain optimal conditions. - How can data mining improve injection moulding process estimates?
Data mining analyzes historical and real-time data to predict optimal settings, reducing the need for physical trials. - What is FEA-based predictive modelling in the context of injection moulding?
Finite Element Analysis (FEA) models simulate material behavior and process conditions to predict performance and optimize design before production.
