© S.M. Bhosle and S.C. Mahadik, Published by EDP Sciences 2026

1 Introduction

Titanium and its alloys are widely used for orthopedic and dental implants due to their favorable combination of mechanical strength, corrosion resistance, and biocompatibility. Nevertheless, implant-associated infection and suboptimal osseointegration continue to compromise long-term clinical outcomes and contribute to high revision rates [1–3]. In response, nanoscale surface engineering, particularly electrochemical anodization to produce titania (TiO₂) nanotube (TiNT) arrays, has attracted extensive interest. These nanotextured surfaces enhance interfacial area for cell attachment and provide a versatile platform for biochemical functionalization, including localized drug delivery and incorporation of antibacterial species [4–6].

While numerous studies have demonstrated promising biological responses from TiNT-coated surfaces at the laboratory scale, the translation of such coatings to clinically relevant implants remains constrained by manufacturing-scale challenges. In particular, reliable high-throughput production requires not only control of nanotube morphology but also robust management of electrolyte chemistry during repeated use, reproducibility across complex implant geometries, and thermal processing strategies that are compatible with industrial cycle times and energy constraints. These considerations are especially critical for additively manufactured (AM) implants, where surface roughness, geometric complexity, and part-to-part variability further complicate process control.

From a manufacturing perspective, three interdependent bottlenecks limit the scalability of TiNT coatings. First, electrolyte reuse, an economic necessity in large-scale anodization, leads to progressive fluoride depletion and accumulation of dissolved metallic species (Ti, Al, and V), both of which alter anodization kinetics and can degrade nanotube morphology if left uncontrolled [7,8]. Second, while filtration strategies can mitigate particulate contamination, they are often insufficient to remove dissolved ionic species, necessitating predictive control strategies such as fluoride replenishment scheduling to preserve electrolyte fidelity over large cumulative anodized areas. Third, post-anodization thermal processing presents a major scalability constraint: although the amorphous-to-anatase transformation of TiO₂ nanotubes is kinetically accessible at moderate temperatures, furnace cycle time, batch utilization, and thermal uniformity ultimately govern throughput, energy efficiency, and cost in industrial settings [9–10].

Recent literature has also explored hybrid electrolyte systems, such as combinations of ammonium fluoride (NH₄F) and silver fluoride (AgF), as a potential route to enable in situ incorporation of antibacterial ions during anodization [11,12]. However, systematic experimental validation of such hybrid electrolytes under scalable manufacturing conditions, particularly with respect to electrolyte stability, ion management, and reproducibility, remains limited and is beyond the scope of the present study. Similarly, while silver-modified TiNT surfaces have demonstrated antibacterial efficacy in vitro, comprehensive in vitro and in vivo evaluation of osteogenic and antimicrobial performance is required prior to clinical translation [13–15].

Accordingly, the present work focuses on upstream manufacturing and process-engineering challenges rather than biological validation. We benchmark anodization workflows on additively manufactured Ti–6Al–4V substrates produced by direct metal laser sintering (DMLS) and electron beam melting (EBM), comparing an optimized anodization protocol with conventional EG + NH₄F and HF-based baselines. We systematically investigate electrolyte reuse through cycle-wise measurements of fluoride depletion and dissolved metal accumulation, evaluate filtration strategies for contamination control, and develop a predictive fluoride replenishment framework based on cumulative anodized area. In parallel, we model furnace energy demand and throughput for implant-scale annealing, emphasizing the dominant role of batch processing and cycle time in determining manufacturing productivity [16–17].

By integrating experimental characterization with process modeling, this study provides a data-driven framework for scaling TiO₂ nanotube coatings from laboratory demonstrations toward industrially viable orthopedic implant manufacturing. The results highlight practical control strategies for electrolyte lifecycle management and furnace utilization, offering actionable insights for translating TiNT surface technologies into high-volume, cost-effective production environments [18].

2 Materials and methods

2.1 Substrates and pretreatment

Commercial Ti–6Al–4V alloy cylinders (10 × 5 mm) were fabricated by DMLS and EBM (N2 Biomedical, USA). All samples were polished, ultrasonically cleaned in acetone, ethanol, and deionized (DI) water (5 min each), and dried under nitrogen.

2.2 Electrolyte preparation

The baseline anodization electrolyte was prepared by dissolving 2 g of ammonium fluoride (NH₄F; Sigma–Aldrich, USA) in 400 ml of deionized water, followed by the addition of 600 ml of ethylene glycol with a water content ≤0.2%. The resulting solution was mixed thoroughly to ensure homogeneity and stored in polypropylene containers to minimize contamination.

For each anodization experiment, 100 ml of either fresh or reused electrolyte was employed. This electrolyte composition was selected to represent a low-water, high-fluoride anodization environment commonly reported in laboratory-scale TiO₂ nanotube fabrication and to enable systematic investigation of electrolyte reuse, fluoride depletion, and replenishment strategies under controlled conditions.

2.3 Anodization protocols

Electrochemical anodization was performed under three representative conditions to enable direct benchmarking between an optimized process and commonly reported baseline approaches. For additively manufactured DMLS substrates processed using the optimized protocol (OurProcess), anodization was conducted at a constant potential of 60 V DC for 40 min in 100 ml of electrolyte, using a carbon rod as the counter electrode [19–20].

For comparison, EBM substrates were anodized using a baseline condition of 30 V DC for 4 h under identical electrolyte composition and volume. In addition, a conventional ethylene glycol-based NH₄F baseline protocol reported in the literature was implemented at 60 V DC for 40 min to provide a direct reference for commonly used laboratory-scale anodization conditions. Following anodization, all samples were thoroughly rinsed with deionized water to remove residual electrolyte and subsequently dried under a nitrogen stream. These anodization conditions were selected to ensure meaningful comparison between the optimized protocol and established baseline processes while maintaining consistent electrolyte chemistry across all experiments.

2.4 Electrolyte reuse and replenishment strategies

Electrolyte reuse was investigated over up to ten anodization cycles, corresponding to a cumulative anodized surface area of approximately 345 cm² per cycle. After each anodization cycle, aliquots of the electrolyte were collected and analyzed to quantify chemical changes associated with reuse. Fluoride concentration was measured using an ion-selective electrode (Thermo Scientific), while dissolved metal content (Ti, Al, and V) was determined by inductively coupled plasma optical emission spectroscopy (ICP–OES, Agilent).

To compensate for fluoride depletion during reuse, replenishment was performed using NH₄F stock solution additions in the range of 500–2000 ppm per ∼500 cm² of processed surface area. Based on experimental observations, fluoride depletion exhibited an approximately linear dependence on cumulative anodized surface area. Accordingly, a predictive fluoride depletion model was formulated as

$F=F_0-k\cdot A_{\text{cum}}$

where F is the remaining fluoride concentration (ppm), F₀ is the initial fluoride concentration, A_cum is the cumulative anodized surface area (cm²), and k is an experimentally derived fluoride consumption coefficient (ppm/cm²). In the present system, k was approximately 7−8 ppm/cm². This relationship enables the design of replenishment schedules to maintain fluoride concentration within a defined operating window, typically 60−70% of the initial setpoint, thereby preserving stable nanotube growth during electrolyte reuse.

In parallel, filtration strategies were evaluated to assess their effectiveness in controlling contamination during electrolyte reuse. The methods investigated included syringe filtration using 0.45 μm PVDF membranes, serial membrane filtration (0.5 → 0.2 → 0.1 μm), and stirredcell filtration using PTFE membranes with a nominal pore size of 100 nm. Filtration efficiency was quantified by comparing titanium concentrations measured before and after filtration.

All filtration experiments were conducted at laboratory scale and were intended to provide quantitative insight into achievable removal efficiencies and their inherent limitations rather than to replicate industrial-scale filtration systems. The measured filtration efficiencies were subsequently used as input parameters in process modeling to evaluate electrolyte lifecycle behavior under extended reuse conditions, enabling informed extrapolation to industrial-scale anodization environments.

2.5 Post-anodization annealing and furnace modeling

Post-anodization annealing was carried out in a programmable muffle furnace (Nabertherm LHT 02/17) using a controlled heating ramp of 10^∘C/min to a target temperature of 350^∘C, followed by a 5-min isothermal soak and natural cooling to room temperature. These conditions were selected based on prior kinetic studies demonstrating complete amorphous-to-anatase transformation of TiO₂ nanotubes within short annealing durations.

To evaluate scalability and industrial feasibility, furnace energy demand was estimated using a lumped-parameter thermal model, assuming uniform heating of the bulk implant. The total energy required per annealing cycle was calculated as

$E_{\{\text{total}\}}=m\cdot c\cdot \mathrm{\Delta }T+E_{\{\text{overhead}\}}+({\mathrm{loss}}_{\{\text{rate}\}}\times t_{\{\text{heating}\}}),$

where m is implant mass, c is the specific heat capacity of Ti–6Al–4V, ΔT is the temperature increase from ambient to annealing temperature, E_overhead represents fixed furnace energy per cycle, and loss_rate accounts for time-dependent thermal losses during heating.

For the representative hip stem implant considered in this study, the following parameters were used: m = 2246 g, c = 0.57 J g^{−1 ∘}C⁻¹, and ΔT = 325^∘C. Furnace overhead energy and thermal loss terms were taken as E_overhead = 0.05 kWh per cycle and loss_rate = 0.005 kWh min⁻¹, respectively. Using this model, throughput per 8-h production shift was calculated for batch sizes ranging from 1 to 100 implants and heating times between 5 and 30 min. Electrical energy cost was estimated assuming an electricity price of $0.12 kWh⁻¹. This modeling framework enables quantitative assessment of the trade-offs between batch size, cycle time, energy consumption, and throughput for industrial-scale annealing of TiO₂ nanotube-coated implants.

2.6 Surface characterization

Surface morphology and phase composition of the anodized samples were characterized using complementary techniques. Nanotube diameter and length were measured by scanning electron microscopy (SEM, FEI Nova NanoSEM 450), with quantitative analysis performed using ImageJ software based on five randomly selected fields per sample. Crystalline phase identification and quantification of the anatase fraction were carried out using X-ray diffraction (XRD, Bruker D8 Advance) through peak deconvolution analysis.

Surface wettability was evaluated by static contact angle measurements using a sessile drop method (Dataphysics OCA 15), with three measurements performed per sample. Although contact angle is not a direct indicator of biological performance, it was included as an indirect measure of surface uniformity and coating consistency. In a manufacturing context, reduced variability in wettability is associated with improved process reproducibility and higher yield in batch surface treatments.

2.7 Statistical analysis

Statistical analysis was applied to compare nanotube diameter, length, anatase fraction, and yield across different anodization workflows. Normality was assessed using the Shapiro–Wilk test. For normally distributed data, one-way ANOVA followed by Tukey’s HSD post-hoc test was employed to identify significant differences between groups (α = 0.05). For non-normal data, Kruskal–Wallis tests with Dunn’s post-hoc comparisons were used.

3 Results and discussions

3.1 Anodization behavior on additively manufactured substrates

Electrochemical anodization of additively manufactured Ti–6Al–4V substrates produced uniform and conformal titania nanotube (TiNT) layers on both DMLS and EBM surfaces. Scanning electron microscopy (SEM) revealed well-ordered nanotube arrays covering fused powder particles as well as interparticle regions, confirming that the anodization process accommodated the inherent surface roughness and geometric complexity associated with additive manufacturing.

For DMLS substrates processed using the optimized anodization protocol (OurProcess), nanotube diameters ranged from approximately 90 to 110 nm, with average tube lengths of ∼500 nm. On EBM substrates anodized under reduced voltage and extended duration conditions, nanotube diameters of 80−100 nm and lengths of 400−470 nm, as illustrated in Fig. 2. were observed. Despite differences in melt pool morphology and surface topology between DMLS and EBM parts, no discontinuities or localized failures in nanotube formation were detected. These observations demonstrate that the anodization protocol is robust across distinct additive manufacturing routes, a prerequisite for scalable implant production where part-to-part variability is unavoidable.

From a manufacturing standpoint, the ability to achieve conformal nanotube coverage on irregular AM surfaces is critical, as localized coating defects or non-uniform morphology can lead to inconsistent functional performance and reduced yield in batch processing.

The anodization of additively manufactured titanium cylinders resulted in the formation of a uniform titania nanotube (TiNT) layer across both powder particle surfaces and bulk substrate regions. The DMLS-fabricated sample exhibits well-defined nanotubular morphology with consistent coverage and pore structure, as shown in Figure 1. Similarly, the anodized EBM titanium sample demonstrates uniformly distributed TiNT arrays with slightly smaller tube diameters ranging from 80–100 nm and lengths between 400–470 nm, as illustrated in Figure 2.

Fig. 1 SEM micrograph of DMLS-fabricated titanium cylinder after anodization, showing uniform titania nanotube (TiNT) coverage on powder particle surfaces and surrounding regions. Nanotube diameters range from 90−110 nm, with average lengths of ∼500 nm.

3.2 Benchmarking of anodization workflows

To quantitatively assess the performance of different anodization strategies, DMLS and EBM samples were processed using three representative workflows: the optimized protocol developed in this study (OurProcess), a conventional ethylene glycol + NH₄F baseline, and an HF aqueous baseline. Key morphological and surface properties are summarized in Table 1, with values reported as mean ± standard deviation (n = 5 per group).

Statistical analysis revealed a significant effect of anodization workflow on nanotube diameter (one-way ANOVA: F = 8.91, p = 0.0043). Post-hoc Tukey comparisons indicated that OurProcess produced significantly larger nanotube diameters than the HF aqueous baseline (p < 0.01), while differences between OurProcess and the EG + NH₄F baseline were less pronounced. Differences in nanotube length and anatase fraction exhibited favorable trends toward OurProcess; however, these did not reach statistical significance within the limited sample size, highlighting the need for larger data sets in future scale-up studies.

The optimized workflow also achieved a higher anatase fraction following a short annealing cycle (84.9 ± 1.7%) compared with both baseline processes. From an industrial processing perspective, this result is particularly important, as higher crystallinity achieved within shorter annealing times directly reduces furnace energy consumption and increases throughput.

Fig. 2 SEM micrograph of anodized EBM titanium cylinder, illustrating uniform titania nanotube (TiNT) arrays distributed across powder particle surfaces and bulk substrate regions. Tube diameters range from 80−100 nm with lengths between 400−470 nm (50,000 × magnification).

3.3 Manufacturing-relevant surface metrics and yield

The relative manufacturing performance of different anodization strategies was evaluated by processing DMLS and EBM substrates using three representative workflows: the optimized protocol developed in this study (OurProcess), a conventional ethylene glycol-based NH₄F baseline, and an HF aqueous baseline. Key morphological, surface, and yield metrics are summarized in Table 1, with values reported as mean ± standard deviation (n = 5 per group).

Statistical analysis revealed a significant effect of anodization workflow on nanotube diameter (one-way ANOVA: F = 8.91, p = 0.0043). Post-hoc Tukey comparisons showed that OurProcess produced significantly larger nanotube diameters than the HF aqueous baseline (p < 0.01). Differences in nanotube length and anatase fraction exhibited favorable trends toward OurProcess; however, given the limited sample size, these differences did not reach statistical significance, indicating that expanded datasets would be required for definitive confirmation.

Beyond nanotube geometry and phase composition, surface wettability and process yield were assessed as indicators of manufacturing robustness. Contact angle measurements showed lower average values for samples processed using OurProcess compared with baseline workflows.

Although contact angle is not a direct measure of biological performance, it provides a practical indication of surface uniformity and coating consistency. In manufacturing environments, improved wettability consistency is associated with enhanced reproducibility and reduced susceptibility to localized coating defects across complex implant geometries.

Process yield, defined as the percentage of parts meeting predefined morphology and coverage specifications, exhibited the most pronounced differences among the evaluated workflows. OurProcess achieved a yield of 90.7 ± 2.3%, compared with 79.1 ± 3.8% for the EG + NH₄F baseline and 69.2 ± 7.8% for the HF aqueous baseline. From an industrial perspective, these differences are highly consequential: a 10−20% increase in yield directly translates into reduced scrap rates, lower reprocessing effort, and decreased per-part production cost in batch anodization operations.

Overall, the benchmarking results demonstrate that the optimized anodization workflow improves not only nanotube morphology and crystallinity but also process reliability and yield, two critical determinants of scalability in industrial TiO₂ nanotube fabrication. Importantly, these gains are achieved without increasing anodization complexity or cycle time, reinforcing the suitability of the proposed process for high-throughput orthopedic implant manufacturing.

4 Electrolyte reuse, filtration, and fluoride replenishment

4.1 Metal contamination during electrolyte reuse

Electrochemical anodization of Ti–6Al–4V substrates inherently results in partial dissolution of substrate elements, including Ti, Al, and V, into the electrolyte. With repeated anodization cycles or increasing cumulative anodized surface area, these dissolved species progressively accumulate, altering electrolyte chemistry and influencing nanotube growth kinetics. Figure 3 illustrates the proportional increase in dissolved metal concentration as a function of cumulative anodized area, consistent with previously reported observations for fluoride-based anodization systems.

Quantitative measurements obtained during electrolyte reuse experiments confirm this trend. As shown in Table 2, titanium concentration in the electrolyte increased from approximately 140 mg/L after the first cycle to nearly 170 mg/L after ten cycles, corresponding to a cumulative anodized area of ∼3450 cm². This predictable accumulation highlights a fundamental challenge for scalable anodization: without intervention, electrolyte degradation is unavoidable during reuse, even under controlled laboratory conditions.

From a manufacturing perspective, uncontrolled accumulation of dissolved metals can lead to gradual drift in anodization behavior, increased variability in nanotube morphology, and reduced process reproducibility. These effects directly impact yield and consistency in batch production, underscoring the need for active electrolyte management strategies [21, 22].

4.2 Effectiveness and limitations of filtration strategies

To evaluate filtration as a control strategy for electrolyte reuse, previously used anodization electrolyte was subjected to syringe-based microfiltration, serial membrane filtration, and stirred-cell filtration using PTFE membranes. The effectiveness of each approach was assessed by comparing dissolved metal concentrations before and after filtration using ICP–OES analysis.

Elemental analysis (Tab. 3) showed that syringe filtration using 0.2−0.45 μm PVDF membranes resulted in modest reductions in dissolved Ti, Al, and V concentrations, typically in the range of 8−15%. Serial membrane filtration provided similar levels of removal, while stirred-cell PTFE filtration achieved slightly higher reductions, up to approximately 20% under laboratory conditions. These results indicate that filtration is effective primarily for removing particulate and colloidal species, whereas the majority of dissolved ionic contaminants remain in solution.

Even under idealized laboratory filtration conditions, more than 80% of dissolved metal content persisted after treatment. As a result, filtration alone was insufficient to restore electrolyte chemistry to near-fresh conditions once large cumulative anodized surface areas had been processed. This behavior is illustrated in Figure 3, which shows the proportional accumulation of dissolved metallic species as a function of total anodized area. Increasing filtration efficiency shifts residual metal concentrations downward but does not eliminate contamination at high cumulative areas. For example, even at filtration efficiencies exceeding 80%, residual titanium concentrations remain significant (>20 mg/L) after extended reuse.

It is important to emphasize that all filtration experiments reported here were conducted at a laboratory scale and were not intended to replicate industrial filtration systems. Rather, the measured filtration efficiencies provide quantitative bounds on achievable contamination reduction and were used as input parameters for electrolyte lifecycle modeling. In industrial anodization environments, filtration capacity, pressure limits, and flow rates will differ substantially from laboratory setups; however, the fundamental limitation observed here, namely the persistence of dissolved ionic species, remains relevant.

Results from the 10-cycle reuse experiment (Tab. 2) further illustrate this limitation. Despite filtration, titanium concentration increased progressively from approximately 140 mg/L after the first cycle to nearly 170 mg/L after ten cycles, while fluoride concentration simultaneously decreased. These trends confirm that filtration delays but does not prevent electrolyte degradation during repeated reuse.

Collectively, these findings demonstrate that filtration should be viewed as a supporting control measure rather than a standalone solution for electrolyte reuse. For industrial-scale anodization, filtration must be integrated with complementary strategies such as predictive fluoride replenishment, partial electrolyte replacement, or selective ion-removal techniques to maintain long-term process stability and reproducibility. The limited filtration efficiencies observed here reinforce the necessity of combined chemical and process-control approaches for scalable TiO₂ nanotube manufacturing.

Fig. 3 Proportional accumulation of dissolved metallic species as a function of the total anodized surface area, based on previously reported experimental observations [22,23].

4.3 Fluoride depletion and predictive replenishment control

In parallel with metal accumulation, fluoride ions are steadily consumed during anodization. Fluoride depletion was measured cycle-by-cycle during electrolyte reuse and exhibited a strong correlation with cumulative anodized surface area. As shown in Table 2, fluoride concentration decreased from approximately 3032 ppm in fresh electrolyte to ∼422 ppm after ten cycles, corresponding to an average consumption rate of ∼7−8 ppm/cm².

This experimentally observed behavior is well described by the linear depletion model introduced in Section 2.4:

$F=F_0-k\cdot A_{\text{cum}}$

where F is the remaining fluoride concentration, F₀ is the initial fluoride concentration, A_cum is the cumulative anodized surface area, and k is the fluoride consumption coefficient. The predictability of this relationship enables the design of proactive replenishment schedules rather than reactive correction after morphology degradation has occurred.

From a process-control standpoint, maintaining fluoride concentration within a defined operating window is essential for stable, self-organized nanotube growth. Based on the present data, a practical replenishment strategy is to restore fluoride when its concentration falls below approximately 60−70% of the initial setpoint. For the electrolyte volume and anodization conditions used in this study, this corresponds to the addition of ∼1000 ppm NH₄F after processing approximately 500 cm² of substrate area. Insufficient fluoride availability is known to disrupt field-assisted dissolution and pore self-organization, leading to irregular or collapsed nanotube morphologies. Accordingly, timely replenishment is critical for preserving process stability during extended electrolyte reuse.

The combined effects of anodized surface area and filtration efficiency on electrolyte contamination are illustrated in Figure 4, which presents residual titanium concentration as a function of cumulative anodized area in a contour (heatmap) representation. In the absence of filtration, titanium concentration increases nearly linearly with processed area, exceeding 80–90 mg/L after approximately 2000 cm². Increasing filtration efficiency shifts the contamination contours downward, reducing residual Ti concentration; however, filtration alone does not restore baseline electrolyte quality at large cumulative areas. Even at filtration efficiencies of 80–90%, residual titanium levels remain above ∼20 mg/L after extended reuse.

These results highlight the inherent limitations of membrane-based filtration for controlling dissolved ionic contamination. While filtration delays electrolyte degradation by removing particulates and some colloidal species, it cannot fully mitigate the accumulation of dissolved metals during large-scale anodization. From a manufacturing perspective, effective electrolyte lifecycle management, therefore, requires a combined strategy that integrates particulate filtration with predictive fluoride replenishment and complementary chemical control approaches, such as partial electrolyte replacement or selective ion-removal techniques. The linear fluoride depletion model presented here provides a scalable foundation for implementing such control strategies in high-throughput anodization environments.

Fig. 4 Ti concentration (mg/L) remaining in electrolyte after filtration as a function of cumulative anodized area and filtration efficiency.

4.4 Implications for scalable electrolyte management

Taken together, the electrolyte reuse results demonstrate that long-term process stability cannot be achieved through filtration alone. Dissolved metal accumulation and fluoride depletion follow predictable trends that must be actively managed using combined control strategies. Filtration delays electrolyte degradation by removing particulates, while predictive fluoride replenishment preserves anodization kinetics and nanotube morphology over extended reuse.

For industrial-scale TiO₂ nanotube manufacturing, these findings support a hybrid electrolyte management approach comprising (i) routine particulate filtration, (ii) continuous or periodic monitoring of fluoride concentration, and (iii) scheduled replenishment based on cumulative anodized area. This integrated strategy minimizes chemical waste, reduces operational costs, and enhances reproducibility, key requirements for translating TiNT coatings from laboratory demonstrations to industrial production.

5 Furnace energy and throughput modeling for industrial annealing

5.1 Thermal requirements for anatase transformation

The amorphous-to-anatase transformation of TiO₂ nanotubes is thermodynamically and kinetically accessible at moderate temperatures. Prior kinetic analysis reported an activation energy of approximately 77 kJ/mol, indicating that complete crystallization can be achieved using short annealing cycles at ∼350 ^∘C [22,23]. Consistent with these findings, the present study adopts a rapid annealing protocol consisting of a controlled heating ramp to 350 ^∘C followed by a 5-min soak.

Although the mass of the TiO₂ nanotube layer is negligible relative to the bulk implant, the dominant thermal demand arises from heating the metallic implant body. Accordingly, furnace design and energy consumption must be governed by bulk implant properties rather than coating characteristics.

5.2 Implant-scale energy demand

To quantify furnace energy requirements under industrially relevant conditions, a representative hip stem implant was modeled as a Ti–6Al–4V cylindrical body with a mass of approximately 2246 g and a surface area of ∼367 cm². Assuming a specific heat capacity of 0.57 J·g⁻¹ · ^∘C⁻¹ and a temperature rise of 325^∘C (room temperature to annealing temperature), the ideal thermal energy required to heat a single implant is:

$\mathrm{Q}=\mathrm{m}\cdot \mathrm{c}\cdot \mathrm{\Delta }\mathrm{T}\approx 4.17\times 105\mathrm{J}\approx 0.116.$

This value represents a theoretical lower bound on energy demand. In practical furnace operation, additional contributions arise from fixed overhead energy per cycle and time-dependent thermal losses. Incorporating these effects using a lumped-parameter thermal model yields total energy requirements per implant in the range of approximately 0.12−0.19 kWh, depending on batch size and heating duration.

At an electricity cost of $0.12 kWh⁻¹, the resulting direct energy cost per implant remains low (≈$0.01–0.02), indicating that energy consumption alone is not a dominant cost driver for TiO₂ nanotube annealing. Instead, overall manufacturing economics are governed primarily by throughput and furnace utilization efficiency.

Importantly, the mass of the TiO₂ nanotube layer itself is negligible relative to the bulk implant. For a hip stem with a ∼367 cm² surface area, the nanotube layer mass is approximately 0.085 g, corresponding to a minimal thermal requirement compared with that of the metallic substrate. Consequently, furnace power sizing and energy management must be dictated by the thermal load of the bulk Ti–6Al–4V implant rather than the nanotube coating.

For example, achieving the required temperature rise within a 5-min heating cycle corresponds to an average power demand of approximately 1.4 kW per implant. This scaling highlights that furnace design for industrial TiNT annealing must prioritize rapid, uniform heating of the implant body to enable short cycle times and high throughput.

5.3 Batch processing and throughput scaling

To assess throughput under industrially relevant production scenarios, batch annealing was modeled for batch sizes ranging from 1 to 50 implants and heating times between 5 and 30 min. Throughput per 8-h production shift was calculated based on total cycle time, including heating, soak, unloading, and handling overheads.

The results, summarized in Tables 4 and 5 and illustrated in Figure 5, reveal two dominant trends. First, at a fixed heating time, throughput increases approximately linearly with batch size, reflecting improved furnace utilization. Second – and more importantly – throughput is substantially more sensitive to heating time than to batch size. For a given batch size, reducing the heating duration from 15 min to 5 min nearly triples the number of implants processed per shift. In contrast, increasing batch size beyond approximately 20 parts yields diminishing returns due to constraints associated with loading, unloading, and thermal uniformity.

These results demonstrate that cycle time reduction is the primary lever for improving productivity in industrial annealing of TiO₂ nanotube-coated implants. While batch size contributes to throughput gains, its influence is secondary compared with the impact of rapid, uniform heating.

A representative hip stem implant was used as a model case to evaluate throughput and cost scaling. Based on the implant geometry and thermal parameters described in Section 5.2, direct energy costs associated with annealing remain minimal (≈$0.01–0.02 per implant at an electricity cost of $0.12 kWh⁻¹), even under short heating cycles. When labor and capital amortization are included, total per-implant processing cost is dominated by non-energy contributions, emphasizing that furnace utilization and throughput, rather than energy consumption, govern cost efficiency.

Batch modeling further illustrates this effect. For example, approximately 130 implants per shift can be processed using 10-part batches with a 5-min heating cycle, while batch sizes of 20 parts enable throughput exceeding 250 implants per shift. Longer heating durations significantly reduce productivity, even when larger batch sizes are employed. Energy per implant decreases modestly with increasing batch size due to shared furnace overhead, but this reduction is small relative to the gains achieved through shorter cycle times.

Overall, the batch processing analysis highlights that scalable annealing of TiO₂ nanotube-coated implants requires furnace designs optimized for rapid heating and consistent thermal uniformity. Short-cycle annealing strategies provide the most effective pathway to high-throughput, cost-efficient production, reinforcing the importance of cycle time optimization in industrial TiNT manufacturing.

5.4 Implications for industrial furnace design

The integrated energy and throughput analysis provides clear guidance for the design of industrial furnaces intended for annealing TiO₂ nanotube-coated implants. First, furnace power capacity must scale with batch size, as simultaneous heating of multiple implants requires proportionally higher delivered power to achieve short cycle times. For example, achieving a 5-min heating cycle for a 10-part batch of hip stems requires an effective furnace power on the order of 14 kW, based on the thermal load of the bulk implants.

Second, thermal uniformity across complex implant geometries is critical to ensure consistent amorphous-to-anatase transformation. Temperature gradients within the furnace can result in incomplete crystallization or part-to-part variability, thereby undermining the benefits of rapid annealing. Industrial implementation therefore requires validation of temperature uniformity using appropriate diagnostic tools, such as embedded thermocouples, infrared thermography, or equivalent monitoring techniques.

Figure 5 illustrates the combined effects of heating time and batch size on furnace throughput. At any fixed heating time, throughput increases approximately linearly with batch size, while increasing heating duration sharply reduces the number of parts processed per shift. The heatmap representation highlights that reductions in heating time yield disproportionately larger productivity gains than incremental increases in batch size. At longer cycle times, throughput remains limited even when large batches are employed, emphasizing the dominant role of cycle time in furnace productivity.

Finally, these results indicate that short-cycle furnace technologies offer the greatest potential for scaling TiNT annealing to high-volume production. Furnace designs that prioritize rapid ramp rates, efficient heat transfer, and thermal uniformity, such as systems incorporating infrared or other high-intensity heating approaches, can achieve substantial improvements in throughput without significantly increasing energy cost per implant. In industrial practice, integrating predictive furnace modeling with batch-scale validation provides a robust pathway for costefficient, high-throughput annealing while ensuring consistent anatase formation across complex implant geometries.

Fig. 5 Throughput versus batch size for 5−30 min heating cycles. The plot shows a near-linear scaling, with diminishing returns at very high batch sizes due to practical handling limits.

6 Conclusion

The scalable implementation of titania nanotube (TiO₂ NT) coatings on orthopedic implants requires a shift from laboratory-scale surface optimization toward integrated manufacturing process control. In this work, two primary bottlenecks limiting industrial translation were addressed: electrolyte lifecycle management during anodization and thermal processing throughput during post-anodization annealing.

Electrolyte reuse studies demonstrated that dissolved metal contamination and fluoride depletion evolve predictably with cumulative anodized surface area. While filtration strategies were shown to reduce particulate contamination, they were insufficient to fully mitigate dissolved ionic species, underscoring the limitations of filtration as a standalone control measure. By quantifying fluoride consumption rates and establishing a simple predictive depletion model, this study provides a practical framework for scheduling fluoride replenishment to maintain stable anodization conditions over multiple reuse cycles. Such data-driven control strategies are essential for minimizing chemical waste, improving reproducibility, and enabling continuous anodization in industrial environments.

Thermal processing was identified as the second critical determinant of scalability. Furnace energy modeling confirmed that the dominant energy demand arises from heating the bulk implant rather than the nanotube coating itself, resulting in low direct energy costs per implant. However, throughput analysis revealed that furnace cycle time – rather than batch size or energy consumption – is the primary lever governing productivity and cost efficiency. Short, uniform annealing cycles enabled by appropriately designed furnaces can deliver order-of-magnitude improvements in throughput without increasing per-part energy costs.

Taken together, these findings demonstrate that scalable TiO₂ nanotube manufacturing is governed by predictable process variables that can be actively managed through engineering controls. By integrating electrolyte reuse strategies with furnace energy and throughput modeling, this work provides a practical roadmap for transitioning TiNT surface technologies from laboratory demonstrations toward industrially viable, high-volume implant manufacturing. The framework presented here establishes a foundation for future studies that incorporate pilot-scale validation, advanced electrolyte management techniques, and biological performance assessment within a manufacturing-relevant context.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflicts of interest

The authors declare no Conflicts of interests.

Data availability statement

The authors declare that the data supporting the findings of this study are available within the paper.

Author contribution statement

Sachin M. Bhosle contributed in research experimentation design, actual research work and paper writing. Shrikant C. Mahadik contributed in data collection and paper writing.

References

All Tables

All Figures

	Fig. 1 SEM micrograph of DMLS-fabricated titanium cylinder after anodization, showing uniform titania nanotube (TiNT) coverage on powder particle surfaces and surrounding regions. Nanotube diameters range from 90−110 nm, with average lengths of ∼500 nm.
In the text

	Fig. 2 SEM micrograph of anodized EBM titanium cylinder, illustrating uniform titania nanotube (TiNT) arrays distributed across powder particle surfaces and bulk substrate regions. Tube diameters range from 80−100 nm with lengths between 400−470 nm (50,000 × magnification).
In the text

	Fig. 3 Proportional accumulation of dissolved metallic species as a function of the total anodized surface area, based on previously reported experimental observations [22,23].
In the text

	Fig. 4 Ti concentration (mg/L) remaining in electrolyte after filtration as a function of cumulative anodized area and filtration efficiency.
In the text

	Fig. 5 Throughput versus batch size for 5−30 min heating cycles. The plot shows a near-linear scaling, with diminishing returns at very high batch sizes due to practical handling limits.
In the text