Continuous Milling Process Research Articles

AI-based analysis of in-line process endoscope images for real-time particle size measurement in a continuous pharmaceutical milling process

This paper presents a case study on the first in-line application of AI-based image analysis for real-time pharmaceutical particle size measurement in a continuous milling process. An AI-based imaging system, which utilises a rigid endoscope, was tested for the real-time particle size measurement of solid NaCl powder used as a model API in the range of 200–1000 µm. After creating a dataset containing annotated images of NaCl particles, it was used to train an AI model for detecting particles and measuring their size. The developed system could analyse overlapping particles without dispersing air, thus broadening its applicability. The performance of the system was evaluated by measuring pre-sifted NaCl samples with the imaging tool, after which it was installed into a continuous mill for in-line particle size measurement of a milling process. By analysing ∼100 particles/s, the system was able to accurately measure the particle size of sifted NaCl samples and detect particle size reduction when applied in the milling process. The Dv50 values and PSDs measured real-time with the AI-based system correlated well with the reference laser diffraction measurements (<6% mean absolute difference over the measured samples). The AI-based imaging system shows great potential for in-line particle size analysis, which, in line with the latest pharmaceutical QC trends, can provide valuable information for process development and control.

Exploration of the heat generation within the intensified vibratory mill.

Recently, the intensified vibratory milling (IVM) has emerged as a viable milling technology known for its nanosizing and high-throughput potential. The extent of the heat generation is ambiguously discussed within the literature. For this reason, the impact of formulation and process parameters for both particle size reduction and heat generation, needs further investigation to develop robust processes using the IVM technology. For this reason, the impact on particle size and heat generation of three different process parameters: - bead-suspension ratio; milling time; and acceleration - as well as one formulation parameter - API concentration - was investigated by a one-variable-at-a-time approach. Further the effect of additional fixtures on the heat generation was noted. The data obtained in this work showed that if the impact on heat generation was taken into account, the process parameters could be fine-tuned to optimize the nano-sizing potential while containing a continuous milling process. A longer milling time and production of highly concentrated formulations were proposed as gentle optimisation steps for the herein presented comminution process.

Simulation of the deformation caused by the machining cutting force on thin-walled deep cavity parts

The sidewalls of thin-walled deep cavity components can easily deform during the milling process because of their low rigidity, which seriously affects the machining accuracy. To resolve this problem, this paper proposes a novel method to predict milling errors based on the finite element method (FEM). A dynamic model of the tool is first established, and the motion state of an arbitrary point on the tool during the continuous milling process is obtained by solving for the key parameters. In addition, both the deflection of the workpiece/tool system and the springback deformation of the workpiece are considered. The material is then removed based on a Boolean operation and a hexahedral mapping algorithm for the points; finally, the continuous tooling process and the milling error of the arbitrary point on the finished surface are obtained. This study uses a typical waveguide cover as the simulation object and verifies the veracity of the simulation method through experiments.

Nonlinear effects in particulate processes

The nonlinear effects in various particulate processes are analyzed within the context of population balance modeling. Our main objective is to indicate the origin of the nonlinear effects and to categorize them in a systematic manner. To illustrate the importance of the nonlinear effects, a continuous milling process is considered, which has been analyzed in the literature with linear population balance models only. A continuous stirred tank mill (CSTM) and a plug-flow tube mill (PFTM) of identical volume were numerically simulated. It is well known that the accumulation of fine particles has a slowing-down effect on the breakage kinetics, which leads to non-intuitive results in terms of the produced particle size distribution. A nonlinear population balance theory is presented to account for these effects, and numerical simulations indicate the enormous potential of the new theory.

Accuracy Optimisation for Mould Surface Profile Milling

In the profile milling process for mould surfaces, for any type of curve combination, when implementing the milling process on a CNC machine, the surface can be produced from straight lines or curves in a piecewise or continuous milling process. This work employs the features of a CNC machine: 1. To divide the various sizes of curve at different slopes. 2. To use with different milling spindle speeds. 3. To use cutting feeds for actual milling experimentation. The results of the profile dimension accuracy and profile surface roughness from these experiments are then used in a neural network to establish an experiment result and a model for the milling variables. The neural network is composed of a number of functional nodes. Once the milling parameters (spindle speed, feed speed and milling angle) are given, the milling processing performance (the surface roughness, the surface profile-error) can be accurately predicted by the network developed. The optimal milling processing parameters can be searched for by a simulation annealing (SA) optimisation algorithm with a performance index to obtain a satisfactory mould surface. The experiment is then used to show that improved conditions for mould profile processing can indeed be obtained.