Closed Circuit of 3-Dimensional Polymer Powders

Abstract

This article describes the most important stages of testing the levels of innovative readiness of a new machine and material construction solutions for closed loops of polymer powders in 3D additive manufacturing. The aim of this study was to indicate the state and directions of development of the technology of geometric recycling of polymer powders in 3D additive manufacturing, the need to control the geometric quality of the powders, and the quality of the design of machines for their processing in a closed circuit. The method was aimed at creating a strategy and verifying the stages of development of a new idea for stabilizing the geometric and dynamic design features of polymer powders for additive manufacturing (3D printing). Connections and relationships of the variable design features of powders, machines and devices with variable postulated states of products and processes allowed for the analysis, knowledge, description and development of variables for stabilizing geometric features of polymer powders in recycling. The results show the possibilities of supporting innovative creativity according to the adopted method, allow for determining the level of compliance of the quality of the products of the technical system, the effectiveness of preparatory processes for recycling and the non-toxicity of the products and processes of the new technology and their closed loop. The goal of developing useful design values, according to an integrated method of analysis, assessment and environmental development, was achieved, including the proposal of design features of razor blade shredders, supported by genetic algorithms, and devices for stabilizing polymer powders in additive manufacturing with recycling.

1. Introduction

The system, optimal design features of machines and equipment, and innovative parameters of the process of preparation for recycling of the pure polymer powders used in 3D printing technology, are novelties and the main solutions of this work. A new closed-circuit system of machines and devices for the geometric process of stabilizing polymer powders for 3D additive manufacturing is the subject of the study. An integrated system (Figure 1) with one input u (construction of machines and process devices) and one output x (construction of polymer powder for 3D printing) was considered.

Optimal functions u(t) and x(t) are defined for each t. Industrial integration of innovative machine design and stabilization of polymers powder design features can be described as a reactor with a continuous flow of operating potentials, including resources (Figure 2). If the flow of any of the potentials of machines or materials is disturbed ($\mathrm{human}-P^P\left(t\right),\mathrm{techniques}-P^T\left(t\right),\mathrm{energy}-\mathrm{matter}-P^{EM}\left(t\right),\mathrm{control}-P^{\mathrm{C}}\left(t\right)$), the disruption of potential flows is reflected in the design and ultimately in the quality indicators of powders, machines, environment, emissions, operational efficiency, non-toxicity of the process and product.

Additive manufacturing (AM) in closed circuits is an advanced production process involving the thermodynamic layer-by-layer deposition of melted construction materials, including the primary material, the material remaining after the production process, and the recycled material [1]. In general, this applies to geometric forms (powder, liquid, sheet, fiber) of various materials (polymers, ceramics, metals, composites).

It was concluded that appropriate energy and environmental models would support the development of separation, comminution and, above all, geometric stabilization of polymer powders [2,3,4,5,6,7,8,9,10,11,12]. Also, the existing but dispersed knowledge bases on the design solutions of machines, process devices, their characteristics and descriptions of the properties of the processing product [3,4,5,6] are only the basis for starting work on systematizing methods for identifying and energy-environmental assessment of a specific PTP processing technique for manufacturing purposes [13,14]. This project contributes to the creation of integrated databases of innovative knowledge about the goals and solutions to the problems of machines, process devices and materials (polymer materials) for 3D printing technology.

There are currently no known systems or strategies enabling multi-factor analysis, evaluation and development of polymer materials that allow automatic selection of machines and devices and the adjustment of optimal processing conditions that translate into reduced energy consumption, high product quality, rational efficiency, and non-toxicity of the product and process.

It is commonly assumed that in a functional system with a variable closed-loop structure, continuous reactions are required [2,3,4,5,6,7,15,16,17,18,19]: normal, adaptive and quarterbacks in the fields of axiological, ecological use, operation and power supply. Adaptive stabilization and monitoring are subsystems supporting additive manufacturing and the organization of activities toward system optimization based on the use of knowledge bases and collected information [3,8,9].

Additionally, low efficiency of unit processes, unsatisfactory grain functions and low efficiency [5,6,10,11,12,13,14] justify the implementation of an innovative project. The importance of the issue of power consumption related to the process results from the fact that this process is characterized by high energy consumption and, at the same time, low energy efficiency. Energy consumption for grinding itself is high; in practice it is at the level of (200–500) KWh/t [20,21], which constitutes 25–50% of the total electricity needed in processing.

The ubiquitous polymer materials (plastic) are a source of huge amounts of waste, the management of which is a serious problem. Global plastic production in 2018 amounted to 359 million metric tons (of which 61.8 million metric tons were in the EU) (www.plasticeurope.org). In the EU, 75.1% of plastic waste was processed (32.5% recycling and 42.6% energy recovery), and 24.9% still ended up in landfills [22]. However, biodegradable materials are being designed, such as various types of polyesters, including polylactide (PLA) or polycaprolactone (PCL) [23], degrading under the influence of environmental conditions, which are used in the production of 3D printing fibers.

Theoretical and practical issues of machines and devices for additive manufacturing processes in 3D are discussed in numerous publications, e.g., [24,25] and others.

In closed industrial production, process systems, power supply, control, and logistics, as well as operations, are mechanized and their implementation is carried out using modern specialized machines and devices. There is an increasing demand for machines and devices for 3D manufacturing in home, local and distributed environments. These machines are subject to increasingly higher requirements regarding the inter-operation quality of the product, susceptibility to assembly, further processing, e.g., heat treatment in the technology of the implemented process, as well as the overall intensity and efficiency, especially in terms of mass and volume efficiency and operational reliability of the technical system.

2. Literature Review, Introduction

Popular additive manufacturing technologies are assigned to specific groups and dominant processing phenomena [4,26,27]: FDM—Fused Deposition Modeling melted material; RC—Robocasting; MJS—Multiphase Jet Solidification; SFF—Solid Freeform Fabrication; SLA—Stereolithography; DLP—Digital Lamp Processing; LAMP—Large Area Maskless Photopolymerization; 2PP—Two-Photon Polymerization; DOD—Drop On Demand; MJ—Material Jetting; NPJ—NanoParticle Jetting; BJ—Binder Jetting binders; SLS—Selective Laser Sintering; SLM—Selective Laser Melting; DMLS—Direct Metal Laser Sintering; EBM—Electron Beam Melting; MJF—Multi Jet Fusion; LENS—Laser Engineered Net Shaping; EBAM—Electron Beam Additive Manufacturing; DMT—Direct Metal Tooling; LOM—Laminated Object Manufacturing; and UC—Ultrasonic Consolidation.

According to the study by the Production Engineering Committee of the Polish Academy of Sciences, Production Digitization Section [3], additive manufacturing methods are divided according to the type of bonding of materials, i.e., chemical bonds, sintering and gluing. Most often, they are referred to using abbreviations derived from their English names. The most popular methods are [3]: SLA (Stereolithography); FDM (Fused Deposition Modeling—shaping with plastic material); JM (English: Jet Modeling—stream modeling); 3DP (3D Printing)—3D printing by combining powder with a binder; SLS (Selective Laser Sintering)—selective laser sintering of plastic and metal powders; and LOM (Laminated Object Manufacturing)—production of products by lamination.

One of the standards defines the nomenclature that should be used in issues related to additive technologies, as well as the method of dividing these technologies in accordance with the current state of knowledge (ISO/ASTM 52900:2021 [28]). The standard specifies division technology molding additive on seven main groups: forming by spraying binder (binder jetting); forming with a directed energy deposition; material extrusion; material spraying (material jetting); powder fusion (powder bed fusion); lamination (sheet lamination); polymerization in a vat (vat polymerization).

Based on a review of the current bibliography of the problem, it can be concluded that: firstly, different process efficiencies, product quality and non-toxicity of products and processes do not indicate the dangers of closed-loop engineering, but do indicate that there is an urgent need to develop and implement a new approach for recycling polymer powders for 3D additive manufacturing; secondly, there is a need for the creation of the structure of machines and devices for geometric stabilization of post-consumer powders; thirdly, there is a need for a systemic approach to the topic in terms of further development of a specific, intelligent system for regenerating polymer powders (RePTP) based on original techniques, ideas, and designs of machines and devices for their preparation (processing). Connections and structural changes between green and conventional energy markets supplying CO₂ emission cycles need to be clarified [29].

A new method of recycling polyamide powders for reuse in the selective laser sintering process is a new idea, a new automated design of a machine installation [30], new process parameters and new configuration of the input powder composition for the additive manufacturing of technical elements. In addition to available technologies, home 3D printing devices using waste polymers are a new, potential solution with the highest possible degree of future implementation, e.g., compare [31,32,33].

3. Materials and Methods

3.1. Data and Model

Low energy processing efficiency of the machine and low efficiency of the use of processing machines indicate the need for research and development of machines and devices for regenerating polymer powders in 3D printing processes:

• the plan developed by the European Council assumes rationalization of energy use and, consequently, reduction of its consumption by 20% (reduction of energy intensity indicators for the economy),
• the manufacturing process is one of the key technologies for the production of almost all products currently on the market (coal, powders, plastics)—global energy consumption for this purpose is as much as 350 billion kWh,
• the production process in processing industry plants consumes up to 50% of the total electricity needed,
• low energy efficiency of currently used machining machines.

To justify the adoption of the purpose of the study:

There is a need to study the machine and process capabilities of industrial implementation to identify solutions that allow for increasing process efficiency while minimizing energy consumption, e.g., by implementing 3D manufacturing processes in machines and devices and by qualitative and quantitative geometric stabilization of post-consumer polymer powders.

The innovative approach developed in the conceptual part should lead to practical implementations in the next stage of development work. This applies to original methods of computer and equipment-supported powder processing engineering, e.g., geometric stabilization of polymer powders by precise and special grinding.

3.2. Machine Readiness Models

To achieve the goal, it was decided to solve the problem: Will a new technique of qualitative and quantitative geometric stabilization of polymer powders in a machine system allow for increasing the efficiency of the composite recycling process while reducing energy consumption and their 3D additive manufacturing?

The assessment of the novelty of the technique and the possibility of its implementation in a closed circuit of polymer powders was carried out on the basis of the design and process solution. The model of adopted criteria for assessing the state of innovation readiness concerns the achievement of postulated states (SP):

• product quality, matter, power and energy;
• energy, ecological and economic efficiency of the technical system operation process;
• non-toxicity of the product and/or process;
• opportunities to acquire and enrich knowledge about the recycling of polymer powders, and scientifically in mechanical engineering, mechanics construction, powder construction, production, operation of machines and devices of SLS processes.

Each level of innovative technological readiness was described, analyzed, assessed, and researched toward achieving the assumed requirements of the IRL strategy, and, above all, the goals presented in

Table 1. The level of innovation readiness in accordance with the IRL methodology (own elaboration, after NCBiR, TRL NASA).

IRL Level	Characteristic Indicator Level Readiness	Range Indicator
IRL 0	Description of the innovative solution	0–10
IRL 1	Identification of basic rules and actions	0–10
IRL 2	Wording concept solutions	0–10
IRL 3	Confirmation of the correctness of the concept experimentally	0–10
IRL 4	Validation of the technology in laboratory conditions	0–10
IRL 5	Validation of the technology in simulated operating conditions	0–10
IRL 6	Creating and checking the operation of a product prototype in simulated conditions close to the real ones	0–10
IRL 7	Checking the operation of the prototype in target operating conditions	0–10
IRL 8	Execution of the final version of the product	0–10
IRL 9	Preparation of a trial series and obtaining product compliance certificates and use permits	0–10
Total indicator of the readiness level of the innovative solution		0–100

.

The analysis and assessment of the innovative readiness of machines and devices for integration processes with the reuse of powders was carried out at three levels (Table 1):

• first—conceptual (IRL 0–IRL 3): description of the innovative solution; identification of basic operating principles; formulating a solution concept; confirmation of the correctness of the concept experimentally;
• second—creative, evaluative (IRL 4–IRL 6): validation of technology in laboratory conditions; technology validation in simulated operating conditions;
• third—manufacturing, operational (IRL 7–IRL 9): checking the operation of the prototype in the target operating conditions; creating the final version of the product; execution of a trial series and obtaining product conformity certificates and use permits.

Table 2. Elements and their material design features contacting powders [own elaboration].

Material of Structural Elements	Description of the Elements that Come into Contact
Stainless steel PUR330 TPU, nitrile PA802-CF	Pipelines, sieves, cyclones, hoses Compensators, nozzle, lance holders, sieve housing

lists the materials and design relationships of the device with which the processing powder comes into contact. The selection was made in such a way that the contamination of the above-mentioned powders can be excluded. Due to the material design features (CK) of the solution, few elements had to be made of conductive polymer. Selective laser sintering processes are carried out, which means that contamination is also present. To prevent this as much as possible, the elements were additionally chemically smoothed. The design should eliminate dust during mixing as much as possible (safety of the operator and maintaining cleanliness in the workplace).

3.2.1. Description of New Features (IRL 0–IRL 3)

Table 3. Creative, conceptual confirmation of the correctness of the innovation on an experimental basis (IRL-0–IRL-3) [own elaboration].

The Machinery Design Idea, Conception Powder Milling, Mixing Models of Innovation
Conception of Knife blade with needle sections Razor blade, thin rotor insert.	Conception of Razor blade, thin stator insert
Assembling the thin razor blade insert of the rotor and stator.		Assembly of numerous, thin rotor and stator inserts.

presents solutions and descriptions of creative design concepts of shredding, mixing, and homogenizing machines confirming the accuracy and rightness of innovation on creative, computational and experimental principles (IRL-0–IRL-3).

In one of the innovative solutions of polymer powder stabilization devices using SLS technology (IRL-0), a razor blade mill was used as described: Multi-ring rotary–vibration mill (Utility Model PL 62990, WUP 03/07, 2007, Utility Model 118888, WUP 10/2012 developed at Bydgoszcz University of Science and Technology and approved by The Patent Office of the Republic of Poland), shown in Figure 3. The device consists of a base 5 to which the stator 3 is attached along with its cover 4 equipped with a charge and air inlet hole, a rotor 2 with working elements (tools) and razor blade ring 6 is attached to the shaft 1 mounted in the base 5 and cover 12 internal, fixed with sleeves 10 and the distribution head 11. The razor blades 7 of the outer working ring are mounted in the stator 3 through the locating sleeves 8 and spacer sleeves 9. The vertical distance between the razor blades 6 of the internal set and the razor blades 7 of the outer set is the working gap of the shredding unit. The rotational movement of the shaft 1 with the rotor 2 and the razor blades 6 of the internal set, in relation to the razor blades 7 of the external set is the relative grinding speed. The efficiency is shaped on the way from the input of the charge through the hole in the cover 4, the cutting-contact space of the razor blades 6 and 7, and the passage through the channel spiral made in the base 5, ending with a tangential outlet pipe, until the product exits from this channel. This efficiency may also depend on a special air stream: from the input of the charge to the outlet of the product shaped by blowing holes made in the base 5.

To achieve the goal, a general problem was formulated in the form of a question: How to rationally describe new solutions, identify the basic principles of operation, formulate and, above all, confirm the correctness of the solution concept experimentally?

Depending on the type of generalized operating energy, the concepts of machines and devices for regenerating powders for additive processes and processing of polymer materials are implemented on powders under the conditions of standard preparation and processing:

• mechanical, carried out for the purpose of: transforming, grinding, sorting, pressing, moving, etc.;
• hydrodynamic, carried out for the purpose of: mixing, homogenization, filtration, dehydration, sedimentation, pressing, etc.;
• thermal, carried out for the purpose of: heating, cooling, evaporation, condensation, etc.;
• with mass exchange, carried out for the purpose of: absorption, adsorption, extraction, drying, distillation, crystallization, concentration, agglomeration, consolidation, compaction, etc.

Integrated optimization methods consist of randomly searching the area of acceptable solutions for: X = CK and SP = e_E. The general relationship between the innovative design of the powder, machines, devices and the postulated state of energy efficiency (e_E) of the process (general form of the adaptation function) has the form (2) (the indicators are described in relationships (9) to (12)):

$$CK=C_g\cup C_m\cup C_d\cup C_{ś}$$

(1)

$$CK=C_g\cup C_m\cup C_d\cup C_{ś}=f \{\begin{array}{c}{\Pi }_g \cup \\ {\Pi }_m \cup \\ {\Pi }_d \cup \\ {\Pi }_{ś} \cup \end{array} \begin{array}{c}{W}_g \cup \\ W_m \cup \\ W_d \cup \\ W_{ś} \cup \end{array} \begin{array}{c}{T}_g\\ T_m\\ T_d\\ T_{ś}\end{array}=f\left(P_o,P_e,P_s,P_{od},E_j,O_{nq}\right)$$

(2)

Deterministic methods strive for the optimal value using appropriate algorithms. The best results are obtained by skillfully combining both methods.

Over recent years, there has been a growing interest in algorithms that are modeled on biological and physical mechanisms that exist in nature. The most famous ones include genetic algorithms (AG) and evolutionary strategies, simulated annealing, artificial neural networks, fuzzy systems, learning machines, ant algorithms and immune research systems [9,21,34].

Genetic algorithms are used as search, optimization and learning algorithms, and also to solve combinatorial optimization problems. Multi-criteria optimization is a novelty in the optimal monitoring of distributed objects because the result of such optimization is a set of compromise solutions that require additional expert assessment in order to decide on the optimal solution. The genotype consists of chromosomes (

Table 4. Concepts used in genetics and genetic algorithms for optimization of machines and devices for polymer powder preparation processes (own elaboration, after [9,21,34]).

Genetics	Genetic Algorithms of Machines and Process Devices Optimizations
Gene	A design feature of a grinder, mixer, a sign, a detector, a single element that stores the values of individual variables
Allele	A variant of a geometric, dynamic material feature, the value of a given gene
Chromosome	A chain, a code sequence, an ordered sequence of genes
Locus	Position, the location of a given gene on the chromosome
Individual (organism)	Sets of task parameters, i.e., solutions, encoded in the form of chromosomes
Population	A set of individuals of a certain size
Genotype	The structure and set of chromosomes of a given individual
Phenotype	Solution, point of the search space, set of task parameters
Generation	A new generation of individuals created
Adaptation function	The fitness function for assessing efficiency and quality, a measure of the adaptation of a given individual (machine, device) in the regeneration population
Selection (reproduction)	Chromosome selection based on fitness function for creating the next generation
Crucifixion	Replacing part of the code information of one individual with another
Mutation	Random changes in a gene—one feature from the code

), where the phenotype and possibly some auxiliary information for the genetic algorithm are encoded. The chromosome consists of genes.

Assessment of chromosome adaptation in the population of powder structures, machines and devices for geometric stabilization processes (including moistening/drying) before the AM process consists of calculating the adaptation function (eval) for each chromosome from this population:

$$eval\left(\overline{v}\right)=f\left(\overline{x}\right)$$

(3)

The chromosomes with the highest value of the adaptation function have the greatest chance of participating in the creation of new individuals (innovative, modernized, optimal solutions).

At the database design stage (Figure 4), an entity diagram was developed, systematically describing the basic tables storing data in the database and the relationships (connections) between them [2].

The created model of the database, the innovative structure, its transformations in the mechanical engineering of machines and devices in the process of regeneration of polymeric materials and powders, meets the assumptions and takes into account the limitations related to the identified functionality of the measurement data collection module. The essence of a database includes blocks and tables [3]:

• Calculations—results of the optimization process (best adapted solutions). The table stores statistics about optimal technology estimators, identifiers of records storing object features stored in separate tables, and information about the settings for which a given solution was obtained;
• Machine—optimal design features of the technological facility, machines and devices for grinding and moisturizing/drying polymer powders;
• Process—features of the process for which a pro-optimal solution was achieved;
• Material—features of the input/product of regenerated polymer powders for the construction innovation process carried out;
• Material Types—dictionary table used to identify the input processed during the simulation;
• Optimization Parameters—table identifying the type, criteria and parameters of optimization;
• Machine Parameters—a dictionary table containing the names of machine parameters subject to modification using a genetic algorithm;
• Material Parameters—a dictionary table containing the names of material parameters subject to modification using a genetic algorithm;
• Process Parameters—a dictionary table containing the names of process parameters subject to modification using a genetic algorithm;
• Criteria—a dictionary table storing the optimization criteria that can be set;
• Optimizations—a table storing the identifiers of optimization parameters so far (name and description);
• GAU Settings—parameters of the genetic algorithm for which the optimal design and processing solution was obtained due to the set criterion;
• Result Classification—a dictionary table containing the names and descriptions of the defined result classifiers that can be used;
• Sessions—an auxiliary table used to identify users performing calculations;
• Users—table containing personal data of people registered in the system;
• Roles—a table storing the roles of system users that supports the process of collecting and searching for information;
• User Roles—a table defining the relationship between a specific system user and their role.

The color scheme of Figure 4 defines the function of a given support floor in the context of the stored information. The following types of floors can be specified for optimal monitoring of mechanical engineering machines and devices for regenerating polymer powders:

• dictionary table—marked in yellow, it stores a set of independent data, constituting a list of available values used when completing aggregation tables;
• aggregation table—marked in green, it stores historical data generated during calculations and identification data related to a specific entity (process, machine, user, etc.);
• association table—marked in blue, it defines the dependencies between dictionary tables and aggregation tables.

MySQL database was used with an appropriate driver (JConnector) enabling access to the database from the JAVA programming environment [2,8].

Methodological procedure for assessing three subsequent levels of innovation readiness:

• validation in laboratory conditions (IRL-4): concerns the analysis and assessment of the regulation, control (ST), drive (N), process (R), and logistics (X) subsystems and their relationships with the geometric and moisture stabilization of polymer powders;
• technology validation in simulated operating conditions (IRL-5): includes an assessment of the quality of the input, product (e.g., geometric, material, dynamic, and environmental characteristics of the powder), process efficiency and non-toxicity of the product and the process of preparing new, used and recycled powders for the process to repeat the additive processing;
• making and checking the operation of a product prototype in simulated conditions close to real ones (IRL-6): an assessment of the production of elements and relations of the system, i.e., machines and devices for multi-faceted stabilization of polymer powders.

The process was carried out in accordance with the principles of supporting creative activities with digital, analytical and machinery experiments and in the conditions of real machines and devices for powder stabilization processes.

The basic achievement of the theoretical analysis carried out in the measurements of the air flow rate through the aspirator with local resistance on the air supply pipeline is installation which allows us to determine the average velocity of dust and air in the pipeline leading to the aspirator. In the study of the pneumatic properties of the grinding system, the throttling holes were replaced with real elements, which made it possible to determine the pneumatic resistance of local elements of the crushing, mixing and stabilization system.

3.2.2. Construction of a Prototype of IRL-6 Powder Stabilization Machines and Devices

When manufacturing and checking the operation of the product prototype in simulated conditions close to real ones, including machines and devices for multi-faceted stabilization of polymer powders, the technical system goes through the following phases:

• Inventory analysis of the condition of mechanical engineering facilities;
• Creation of the concept of a technical and IT solution;
• Implementation of a monitoring system;
• Adoption of operational characteristics, data acquisition and modeling;
• Adoption of the mathematical model, optimization criteria and optimization procedure;
• Analysis of benefits and energy, ecological and financial costs of implementing the process;
• Simulation (visualization) of a variant, concept in conditions similar to real ones;
• Creating an optimal solution for machines and devices for stabilizing polymer powders;
• Pro-development changes in means and methods of stabilizing polymer powders.

The process system (SPr) was constructed and the operating characteristics of the prototype were checked in simulated conditions close to real ones (Figure 5) for the mill machine and equipment (Figure 5a) and the logistics and control unit (Figure 5b).

To check the operation of the product prototype in simulated conditions close to real ones, dependent variables of the state and changes of machines and process devices and their indicators were used [2,3,6,10,14]):

• material variable indicators:

  −

  d_o—average powder size before processing, μm;

  −

  w₀—average humidity of the polymer powder before processing (sintering), %;

  −

  a₁-, b₂-, and c₃- percentages of polymer powders (UM—used, NM—new and ReM—recycled material; polyamide—nylon: PA12, PA11 and PA6; PA12, polystyrene (PS), polypropylene (PP) fibers, %.

• traffic variable indicators:

  −

  ω₁—angular speed of the gear input shaft, rad·s⁻¹, (rpm);

  −

  ω₂—angular speed of the gear output shaft, rad·s⁻¹, (rpm);

  −

  M₁—torque on the gear input shaft, N·m;

  −

  M₂—torque on the gear output shaft, N·m;

  −

  η_s—engine efficiency;

  −

  η_p—gear efficiency.

The instantaneous courses of the above-mentioned characteristics allow you to determine the relationships: poprawiono

• kinematic gear ratio:

$$i_k={\displaystyle \frac{{\omega }_2}{{\omega }_1}},$$

(4)

• dynamic ratio:

$$i_d={\displaystyle \frac{M_2}{M_1}},$$

(5)

• power at the gearbox input equal to the power at the engine output:

N₁ = N_S = ω₁ · N₁

(6)

• power at the gear output equal to the grinding power:

N₂ = N_R = ω₂ · N₂

(7)

• energy indicators and estimators:

  −

  $\stackrel{•}{m}$—mass flow defined as the increase in the amount of stabilized plastic powder (grinding, dried, moistened) over time, dm·dt⁻¹, kg·s⁻¹;

  −

  W₁—mass efficiency understood as the amount of product after grinding as a function of time, kg·h⁻¹;

  −

  E_R—specific energy consumption as the amount of energy needed to produce one kilogram of a product meeting the criteria of processing and additive manufacturing, kJ·kg⁻¹;

  −

  e_R—energy efficiency index of processing.

By definition, the shredding process is based on operational needs defined in terms of value and quality [34,35]:

• machine design—arrangement and type of knife discs;
• movement of knife elements, material and degree of fragmentation—rotational speed of knives (in the range of 420–1900 min⁻¹);
• efficiency, power, energy of the process (process efficiency)—the amount of vacuum in the batch tank/shredding product and at the inlet of the material to the shredder and the setting of the aspirator (pneumatic transport);
• no blocking by the batch—the amount of material fed to the hopper as determined by the control room hopper screw feeder motor.

The test stand (Figure 6) consists of: a material container with outlet control; a precision shredder RPW-11TN; Z1—a container for the material to be crushed, with a camera view through the inspection window; E4—a cyclone; and E3—an aspirator.

The stand uses control elements—a flow meter, vacuum gauges and a Prandtl tube, all with digital measurement transducers. The computer application for controlling the actuators and visualizing process variables was created in the LabVIEW environment. A view of the control panel is shown in Figure 7.

The kinematic pressure P_k measured by the Prandtl tube is a measure of the flow velocity in the cross-sectional axis of the tube (maximum velocity v_C), according to the formula:

$$v_{\mathrm{C}}=\sqrt{{\displaystyle \frac{2P_{\mathrm{k}}}{{\rho }_{\mathrm{air}}}}}$$

(8)

where ρ_air is the density of the air flowing.

Therefore, semi-empirical relationships are used with the distribution of velocities according to the Prandtl formula [34,35].

If we assume that the velocity distribution is given by the power Prandtl formula and the elementary section is given by $\mathrm{d}A=0.5\pi r\mathrm{d}r$, the elementary flow rate on 1/4 of the elementary cross-section is given by $\mathrm{d}Q=0.5\pi v\left(r\right)r\mathrm{d}r$. The average velocity can be determined after substituting $v=v\left(r\right)$. After it is multiplied by $4v_{\mathrm{C}}$, we will get the flow rate in the cross-section. The mentioned integral causes some analytical problems, but it can be calculated using a special function $\Gamma$.

The nature of the flow is given by the Reynolds number expressing the ratio of inertial forces to viscous forces after using the mathematics formula.

During aspirator tests, the maximum speed on the pipeline axis $v_{\mathrm{C}}$ is measured by a Prandtl tube. For medium values ρ = 1.2 kg/m³, μ = 1.7 × 10⁻⁵ kg/m∙s, and i D = 45 × 10⁻³ m. Received values $Re\approx 15000÷55000$, which indicates a turbulent flow of air in the aspirator.

3.3. Material Readiness Models

Mixing and seasoning of material in the form of powders applies to polyamide–nylon—more precisely: PA12, PA11 and PA6; and PA12 + aluminum addition. This is the material group most often used in this technology: powder with a globular morphology ranging in size from 20 to 100 μm, with a dominant fraction of 60 μm. The material is very loose.

The input to the basic 3D printing process is the result of mixing three components, i.e., post-process material (UM), new (fresh) (NM) and recycled (ReM). Examples of mixture proportions are, for example UM/NM/ReM, e.g., 50/40/10. The grading system sifts through 200 micro sieves. Ultrasonic sieves (e.g., 20–40 kHz) can be used to speed up this process. The mixed material is seasoned for approximately 24 h. During this time, the humidity must be stabilized in the range of 40–60%.

Fineness Check under Target Operating Conditions (IRL-7)

Measurement using the Dynamic Image Analysis method (ISO 13322-2) [36] with a system of two cameras working simultaneously allows for the analysis, assessment and verification of the polymer powder and the measurement of a wide range of geometric features, and the results obtained are repeatable and characterized by a resolution many times higher than the measurement results that are possible to be obtained by sieving.

The purpose of granulometric analysis of polymer powder is to find out the particle size distribution in the obtained sample (Figure 8, Figure 9 and Figure 10). The polymer particles are too small and the material is naturally susceptible to electrostatic phenomena, so the traditional and popular analytical method of sieve analysis cannot be used here. The dynamic image analysis method was used (Method: Analyses of the form and geometric dimensions of polymer particles, ISO 13322-2) [36]. Device: CAMSIZER X2 (Microtrac Retsch GmbH, Haan, Germany), dry dispersion in an air stream (X-Jet). Material type: polymer in powder form. Sample quantity: approx. 5 mL per one measurement.

TEST 1: Repeatability test: The test involved taking two measurements of the same material and then comparing the results to determine whether the tested measurement method and the device itself provide repeatable results.

Measurement 1: Two samples with a volume of approximately 5 mL (2 teaspoons) taken from the same batch of material were measured successively.

Summary and conclusions:

• The first look (Figure 9) allowed us to conclude that the same material was measured (and it was NM and ReM), and the measurement itself was highly repeatable.
• A maximum difference of just over 0.4% appeared on the flattened curves, i.e., for the range of particles whose percentage share in the total volume is large.

Such a deviation is practically negligible, especially when you consider random sampling (spoon collection without the use of a sample splitter).

TEST 2: Repeatability test continued. It is possible to present the measurement result in tabular form for freely defined classes (virtual sieves).

Table 5. Results from ten linearly arranged sieves ranging from 0 to 100 μm for both series of measurements performed; p3 and Q3 are the percentages of dimension classes p3—real, Q3—cumulative.

Size Class [μm]		p3 [%]		Q3 [%]
		File 1	File 2	File 1	File 2
>100.000		0.2	0.4	100.0	100.0
90.000	100.000	0.3	0.1	99.8	99.6
80.000	90.000	0.8	0.8	99.5	99.5
70.000	80.000	3.5	3.1	98.7	98.7
60.000	70.000	12.4	13.0	95.2	95.6
50.000	60.000	26.3	26.2	82.8	82.6
40.000	50.000	31.3	31.7	56.5	56.4
30.000	40.000	19.5	18.9	25.2	24.7
20.000	30.000	4.3	4.4	5.7	5.8
10.000	20.000	1.0	1.0	1.4	1.4
	<10.000	0.4	0.4	0.4	0.4

presents the results for ten linearly arranged sieves ranging from 0 to 100 μm for both measurements, where p3 and Q3 are the percentages of dimension classes p3—real, Q3—cumulative.

TEST 3: Repeatability test continued. Because the device can save photos of analyzed particles (all or according to a given criteria), it can look at individual particles in terms of various parameters and geometric features, e.g., size and shape.

4. Results and Discussion

Logic maximizing one (K—benefits) and minimizing the other (N—inputs) operational characteristics of the postulated states of the system and surroundings (environment) was implemented according to relationships (9) to (12). To find a solution, optimal technical conditions, e.g., design features $C_k^{*}$: recycled polymer powders belonging to the permissible set Φ, such that the operational characteristics of any design feature are worse (smaller when maximizing—dependencies (9) and (10); greater when minimizing—the dependence ((11) and (12)) on the performance characteristics for the optimal feature. In the analytical tests, the performance characteristics were calculated:

$$\begin{gathered} \left\{C_k^{*}\in \varnothing \right\}:\left(\underset{c_{k\in \Phi \varnothing }}{\bigwedge }{H}_u \left(C_k\right)<H_u \left(C_k^{*}\right)\right)dla H_{u }:Q_{P,E}\uparrow ,e_{en}\uparrow , e_{Eco} \\ \uparrow ,e_{eko}\uparrow ,W_u\uparrow ,\eta \uparrow ; C_k:C_{kg},C_{km},C_{kd}, C_{ks};P_{pr},T_u \end{gathered}$$

(9)

or

$$\begin{gathered} \left\{C_k^{*}\in \varnothing \right\}:\left(\underset{c_{k\in \Phi \varnothing }}{\bigwedge }{H}_u \left(C_k\right)<H_u \left(C_k^{*}\right)\right)dla H_{u }:K_u\uparrow , P\uparrow , E\uparrow , Q_{P,E} \\ \uparrow ,e_{en}\uparrow , e_{Eco}\uparrow ,e_{eko}\uparrow ,W_u\uparrow ,\eta \\ \hspace{1em}\hspace{0.17em}\uparrow ,C_k:C_{kg},C_{km},C_{kd}, C_{ks};P_{pr},T_u \end{gathered}$$

(10)

Logical models of optimization procedures with minimization of operational characteristics describe relationships (11) and (12):

$$\begin{gathered} \left\{C_k^{*}\in \varnothing \right\}:\left(\underset{c_{k\in \varnothing }\Phi }{\bigwedge }{H}_u \left(C_k\right)>H_u \left(C_k^{*}\right)\right)dla H_{u }:{\Delta }_{P,E}\downarrow ,E_{CO2}\downarrow , E_{PO4} \\ \downarrow ,E_{PM}\downarrow , C_k:C_{kg},C_{km},C_{kd}, C_{ks};P_{pr},T_u \end{gathered}$$

(11)

or

$$\begin{gathered} \left\{C_k^{*}\in \varnothing \right\}:\left(\underset{c_{k\in \varnothing }\Phi }{\bigwedge }{H}_u \left(C_k\right)>H_u \left(C_k^{*}\right)\right)dla H_{u }:N_u\downarrow , {\Delta }_{P,E}\downarrow ,E_{CO2} \\ \downarrow , E_{PO4}\downarrow ,E_{PM}\downarrow ,C_k:C_{kg},C_{km},C_{kd}, C_{ks};P_{pr},T_u \end{gathered}$$

(12)

where:

• $C_k$—design features of powders and machine elements (geometric, material, dynamic and environmental);
• $C_k^{*}$—optimal design features of powders and machine elements (e.g., in recycling);
• Φ—area of permissible solutions (concepts) of the design feature vector;
• $H_u$—performance characteristics of powders with the given design features;
• $K_u$—energy, ecological and economic benefits from using the construction solution;
• $N_u$—energy, ecological and economic expenditure during the use of the construction solution;
• $P$—electrical and mechanical power in the selected installation node;
• $E$—electrical and mechanical energy of the selected installation node;
• $Q$_P,E—power and energy quality in the selected installation node;
• $e_{en}$—energy efficiency of the process, e.g., energy conversion;
• $e_{Eco}$—ecological efficiency of the process;
• $e_{eko}$—economic efficiency of the process;
• $W_u$—efficiency of use and operation of machines and process devices;
• $\eta$—efficiency and susceptibility to useful action;
• ${\Delta }_{P,E}$—power and energy losses accompanying idle, useful and dynamic operations of machines and devices;
• $E_{C{O}_2}$—CO₂ emissions from operation, useful and balancing activities;
• $E_{P{O}_4}$—PO₄ emissions from operation, useful and balancing activities;
• $E_{PM}$—emissions of particles and dust from operation, utility and balancing activities;
• $C_{kg}$—structural geometric features of powders (form, dimension, tolerance);
• $C_{km}$—design material features of powders (form, dimension, tolerance);
• $C_{kd}$—dynamic design features of powders (form, dimension, tolerance);
• $C_{ks}$—environmental design features of powders (form, dimension, tolerance);
• $P_{pr}$—selected parameters of the use of powders, machines and process devices;
• $T_u$—annual use of machines and process equipment.

Creation of the Final Version of the Product (IRL-8)

Powders of primary and secondary polymer materials (from use or recycling), used e.g., in SLS and FFF technologies, are heterogeneous, anisotropic and multiphase media, and the processing conditions are variable as a result of their input properties, hence the concept of a primary processing machine should be conventional, standard or individual.

It was assumed that the use of AM is always characterized by obtaining more favorable values of the considered indicators.

The total active power consumed depending on the technique of preparation, processing, drying, moisturizing and cooling of materials is shown in Figure 11 as an example for variable thicknesses of the blade of the grinding tool (“razor blade”). The variability of the power consumed by the drive unit (engine, gears) was used during processing and idle operation, as well as for the actual mill, and the resulting power of the process was determined, e.g., (relation (9)).

The specificity of construction and operation, an attempt to sort out the issues, selected methods and means of innovation of machines and devices for humidification/drying processes were presented by validating the technology in simulated operating conditions. Due to the significant role of humidity in objects occurring in the method of recycling polyamide powders in the selective laser sintering process, the implementation of the humidity stabilization system was subordinated to the assumptions about the postulated states of the ambient air and the polymer powder (

Table 6. Assumption of the desired air humidity states and polymer powder charge.

No.	Postulated State	Status Value	Volume	Object Temperature	Cleanliness of the Facility
1.	Air and ambient humidity	(40–70)%	(45–60) m3	(14–22) °C	(95–99)%
2.	Powder moisture	(0.1–1.2)%	(215–310)·10−15 m3	(10–35) °C	(97–100)%

).

Moisture measurement of polymer powders used the dielectric method LabVIEW-2. This method belongs to the group of dielectric measurement methods, using the dependence of the dielectric constant ε the tested material on its moisture. It also allows you to measure from a distance, without contact with the material to be tested.

The main reason for stabilizing air humidity is electrostatic charges generated during the movement and friction of polymer powders. Humidity stabilization and, in most cases, drying of polymer plastic powders, guarantees effective processing of all hygroscopic polymers, and may also be useful in the processing of some non-hygroscopic polymers. Temperature stabilization affects the movement of moisture molecules in the environment, and its appropriate selection is an important aspect in the context of powder drying. Air humidity below 30% favors the accumulation of electric charges on the surface of insulators. Additionally, the formation of a thin layer of moisture on the surface of materials changes their thermal conductivity.

Levels of innovation (NCBiR/TRL NASA) were completed at 88% (

Table 7. Table of the level of innovation readiness using the IRL NCBiR methodology (source: TRL NASA).

IRL Level	Characteristic Indicator Level Readiness	Range Indicator
IRL 0	Description of the innovative solution	10
IRL 1	Identification of basic rules and actions	9
IRL 2	Wording of concept solutions	9
IRL 3	Confirmation of the correctness of the concept experimentally	10
IRL 4	Validation of the technology in laboratory conditions	9
IRL 5	Validation of the technology in simulated operating conditions	10
IRL 6	Creating and checking the operation of a product prototype in simulated conditions close to real ones	8
IRL 7	Checking the operation of the prototype in target operating conditions	10
IRL 8	Execution of the final version of the product	7
IRL 9	Preparation of a trial series and obtaining product compliance certificates and use permits	6
Total indicator of the readiness level of the innovative solution		88

), which are the basis for further promotional work and development research.

Each level of innovative (technological) readiness was described, analyzed, and assessed (researched) toward achieving the assumed requirements of the IRL strategy and project goals. The values of the characteristics of the readiness level indicators, according to the IRL research, are presented in the table below (Table 7).

Based on the study of the levels of innovative readiness of the machine solution for closed cycles of polymer powders in 3D additive manufacturing, it can be assumed that this is an 88% fulfillment of the requirements for an innovative solution to the problem, in terms of: conceptual, creative, evaluative, and finally, productive and operational readiness. Carrying out a trial series and obtaining product conformity certificates and use permits is envisaged in the project continuing to engineering solutions for stabilizing polymer powders in 3D technology, supported by recycling.

5. Summary and Conclusions

The multi-faceted hypothesis was confirmed that in closed loops of polymer powders for additive manufacturing: firstly, there is an urgent need to develop and implement a new approach to recycling polymer powders for the purposes of 3D additive manufacturing; secondly, proposing the design of machines and devices for the geometric stabilization of post-consumer powders is a significant achievement of the project; and thirdly, a systemic approach to the topic in terms of further development of a specific, intelligent system for regenerating polymer powders (RePTP), based on original techniques, ideas, structures of machines and devices for their preparation, is the basis of scientific innovative work.

Using the results of the project team’s own research and development work on the granulation of polymer and fiber materials aimed at reducing the energy consumption of recycling processes, the chances of achieving the planned project effects were increased, as the standard of an intelligent solution (based on knowledge and innovation).

A concept of a quasi-intelligent engineering system for the regeneration of polymer powders was obtained, which includes:

• machine mechatronic system,
• high-tech special system,
• process design,
• control structure,
• information and logistics construction.

The assumption was confirmed that powders of primary and secondary polymer materials (from use or recycling), used, e.g., in SLS, FFF technologies, are heterogeneous, anisotropic and multiphase media, and the processing conditions are variable as a derivative of their input properties; hence, the concept should be conventional machines for basic, standard or unit processing. At the same time, however, the operating conditions of the machine for stabilizing geometric features or drying may include all of the above-mentioned standards.

Thanks to the research methodology, innovative development and analyses, the scientific and technical goals of the work were achieved, consisting of the adoption of models, theories, selection and verification of ideas, structures, and process parameters (technical conditions (Wt)) of machines and devices for the stabilization of polymer powders (postulated states (SP)), moving toward higher product quality, higher process efficiency, and better knowledge of the circular economy (CE).

The scientific and practical basis for the methodology of innovation of machines and devices for the processes of geometric and physical stabilization of polymer powders, conception and selection of new design solutions for the mechanical processing of polymer materials was developed and improved.