Intelligent and Precise Textile Drop-Off: A New Strategy for Integrating Soft Fingers and Machine Vision Technology

Abstract

This study presents a novel drop-off strategy for automated fabric handling in intelligent apparel manufacturing, addressing the critical challenge of drift-free placement of lightweight, flexible textiles. A pneumatically driven retractable plate is introduced as an auxiliary device, along with machine vision technology, to eliminate drop-off deviations inherent in traditional soft grippers. By synchronizing the retraction motion of the plate with soft gripper release, the fabric is transferred onto the target surface without free-fall drift, achieving sub-0.5 mm alignment accuracy across 15 fabric types. Machine vision-based inspection validates drop-off quality in real time. This work offers a low-cost, drift-free drop-off solution for pre-sewing automation.

1. Introduction

The global manufacturing landscape is undergoing a profound transformation, catalyzed by Germany’s pioneering Industry 4.0 initiative. This new industrial revolution is anchored in smart manufacturing—a paradigm that has become the inevitable trajectory for both traditional and modern production systems. Capitalizing on these technological advances, the apparel sector has markedly expanded its innovation capacity and upgraded its manufacturing infrastructure. Consequently, a growing body of research is devoted to the digitalization and automation of smart equipment, leading to the design of clothing production lines that seamlessly integrate human expertise, robotics, and intelligent machines in pursuit of effective “machine substitution for human labor” [1].

Within the apparel production pipeline, the sub-packaging stage—specifically the stacking of cut parts discharged from automated cutting machines—still relies almost exclusively on manual handling, leaving a critical automation gap. This labor-intensive step has emerged as a principal bottleneck impeding further intelligentization of the entire apparel manufacturing system [2,3]. To overcome this constraint, researchers have proposed a spectrum of automated grasping devices that can be taxonomized by their underlying gripping principles: vacuum suction grasping [4,5,6], electrostatic grasping [7,8,9], needle piercing grasping [10,11], robotic arm grasping [12,13], and, most recently, soft gripper grasping [2,14,15,16,17]. Among these, soft grippers—introduced only within the past three years—offer unprecedented flexibility, multiple degrees of freedom, and environmental adaptability. Their inherent compliance allows large, controllable deformation, yielding gentle interaction with soft, porous, and easily deformable materials such as textiles. Consequently, soft grippers can reliably handle diverse fabric types without compromising surface integrity or mechanical properties.

Building on the work of Koustoumpardis P.N. and Zacharia P., Ku et al. [18,19] introduced, in 2020, the first soft robotic gripper that is inspired by the adhesion mechanism of the lamprey. The device adapts to various fabrics and achieves high success rates with single-layer, breathable textiles, showing strong potential for intelligent garment manufacturing [14]. Marullo et al. [20] developed the electromagnetic Magg-Gripper to counteract fabric deformation. Metal inserted into garments allows magnetic attraction to compensate for positional inaccuracy and improve repeatability. Su et al. [17] emulated human grasping with a fixed–mobile soft-finger gripper, achieving 90% separation accuracy and ±1 mm placement accuracy, meeting most mass-production requirements. Because fabric shape and properties vary widely, dedicated grippers are traditionally required for each material and operation, complicating automation. Ebraheem et al. [10] combined vacuum suction, needling, and clamping into a universal fabric gripper, validating its efficacy across diverse textiles. Shen et al. [21] optimized soft-finger layouts for stacking garment panels, enhancing gripper adaptability to differently shaped pieces. Compared to other grasping methods, soft grippers have higher market application potential and have been adopted by some enterprises for automated fabric transportation at the China International Sewing Machinery & Accessories Show (CISMA) in 2023 [2].

However, the literature has concentrated almost exclusively on the precision with which a soft gripper can acquire a stack of fabric pieces, while the equally critical issue of accurate, autonomous drop-off has received scant attention. At the pre-sewing preparation station, operators conventionally pick up two plies to be joined, align their edges within a tolerance of 0.5 mm [3], and then feed the aligned pair into the sewing head. Consequently, merely guaranteeing sub-millimeter grasping accuracy is insufficient; the gripper must also place the plies with commensurate precision. Any deviation would reintroduce manual realignment, undermining the very purpose of automation. Only when grasping and drop-off accuracy are jointly satisfied can the stacking of cut parts be considered fully automated, thereby establishing a critical enabler for intelligent apparel manufacturing.

Therefore, based on the existing soft gripper technology, this study designs an automatic drop-off device to assist the soft gripper in achieving accurate and efficient drop-off. The device is not only simple in structure and easy to operate but also integrates machine vision technology to inspect the drop-off results of the fabric pieces, ensuring the quality of subsequent sewing processes.

2. Drop-Off Strategy

When misalignment is detected during sewing, an operator can effortlessly perform a secondary adjustment of the upper ply with a single hand [22,23]. Although this corrective action is trivial for a human, it is remarkably difficult for a robotic gripper [24,25]. Fabric is soft, anisotropic, and highly elastic; its geometry and pose evolve continuously under external forces. Unlike a human hand, a robotic end-effector lacks the dense tactile sensing, proprioceptive feedback, and experiential knowledge required to infer the true state of the cloth. Consequently, it cannot reliably identify which regions need correction, nor quantify the direction and magnitude of the required displacement.

Further complications arise from the vast heterogeneity of textiles. Cotton, linen, silk, and synthetic fibers differ in stiffness, friction, and surface texture, while weave patterns (plain, twill, satin, etc.) introduce additional variability. A robotic system must rapidly recognize and adapt to these differences; otherwise, its perception of the global fabric state—and the corrective policy derived from that perception—will be compromised.

Humans exploit bimanual dexterity to align fabric efficiently: one hand stabilizes, while the other translates, rotates, or tensions the material, or both hands pull opposing edges to remove wrinkles [22,23]. A single robotic arm can execute only one action at a time. Sequential reproduction of such coordinated motions is slow, and any interruption causes the fabric to relax or buckle, degrading alignment accuracy.

Dual-arm platforms could, in principle, replicate human-level coordination, but their development demands substantial investment in mechanical design, multimodal sensing, and advanced control algorithms. These R&D expenditures are unlikely to be recouped quickly and may be prohibitive for small and medium-sized enterprises. Moreover, the resulting hardware—characterized by complex kinematics, high-precision sensors, and sophisticated controllers—carries a premium price that is difficult to justify when the alignment task itself is relatively simple. In such cases, deploying a dual-arm robot constitutes technological overkill and leads to inefficient resource utilization [24,25].

In summary, the optimal strategy would be to ensure precise alignment of the fabric during the drop-off process, thereby eliminating the need for secondary adjustments by the robotic arm. Therefore, the drop-off strategy proposed in this study ensures precise drop-off by combining an auxiliary drop-off device with existing soft gripper technology (see Figure 1).

2.1. Soft Grippers

In traditional soft gripper technology, as shown in Figure 2a, soft grippers typically consist of two hollow fingers [21]. When a negative pressure is applied to the soft grippers, the fingers close and grasp the fabric, which is then transported to a designated platform. Subsequently, a positive pressure is applied to the soft grippers to release the fabric. However, since the fabric is a relatively lightweight and flexible material, it is prone to drifting due to air currents and slipping on the platform surface during the free-fall process when the soft grippers release it. This results in misalignment between the successively dropped layers of fabric. Although this method excels in grasping accuracy, it fails to achieve precise alignment, leading to deviations.

To address this issue, this study employs a new generation of “high-heeled” soft grippers developed by SR Co., Ltd. (Suzhou, China), based on traditional soft gripper technology, as shown in Figure 2b. The design feature of these soft grippers is that one finger is solid and impermeable to air, while the other finger is hollow, as illustrated in Figure 2c. They were installed in a UR5 arm robot (Figure 3).

When a positive pressure is applied internally, the hollow finger opens and moves away from the solid finger; conversely, when a negative pressure is applied, the hollow finger closes towards the solid finger. The drop-off process is depicted in Figure 4a–d. The advantage of this soft gripper is that it can press the fabric against the surface of the table before releasing it during the drop-off process, thereby avoiding drop-off deviations caused by free-fall [17].

However, this improvement also introduces new challenges. When the first layer of fabric is dropped, the soft gripper lays the fabric on the platform surface. Subsequently, when the soft gripper drops the second layer of fabric, it is laid on the surface of the already dropped layer. Since the frictional properties between the platform and the fabric are usually different from those between the fabric layers, this causes the second layer of fabric to slip, resulting in alignment deviations [17]. To address this challenge, a new design of a drop-off device is proposed to assist the soft gripper in achieving precise drop-off, ensuring accurate alignment of the fabric during the drop-off process, thereby effectively improving production efficiency and product quality.

2.2. Drop-Off Device

Given the low stiffness of fabrics, which tend to sag when suspended, a drop-off device as illustrated in Figure 5 was designed. This device employs a solenoid valve to control a pneumatic mechanism that actuates the extension and retraction of a metal plate. When the retractable plate is extended, the fabric is placed on its surface. Subsequently, a signal is sent to the solenoid valve to alter the airflow direction in the pneumatic device, causing the plate to retract and lay the fabric onto the underlying table surface. There is a fixture above the drop-off device for the convenience of installing the device onto the profile frame. Figure 6 presents the structural drawing of the device.

Preliminary experiments revealed that a thin film should be applied to the surface of the retractable plate during practical use to reduce the friction between the fabric and the plate. The specific drop-off procedure is as follows:

• A signal is sent to the solenoid valve to extend the retractable plate of the drop-off device, allowing the soft gripper to release the first layer of fabric onto the plate surface, as shown in Figure 7a.
• Another signal is sent to the solenoid valve to retract the plate, laying the fabric onto the underlying surface, as depicted in Figure 7b.
• Steps 1 and 2 are repeated to complete the aligned drop-off of two layers of fabric, as illustrated in Figure 7c.

In this study, the fabric is laid on the platform surface, effectively avoiding alignment deviations caused by free-fall motion. Additionally, the soft gripper releases the fabric onto the same retractable plate surface each time, thereby preventing alignment deviations due to differences in surface friction properties. However, whether the fabric will experience drop-off deviations during the retraction of the plate, potentially affecting the drop-off outcome, still requires further investigation and validation through experimental testing.

Therefore, for ease of understanding, throughout the paper, the term “drop-off” denotes the complete process that begins when the telescopic plate starts its retraction (i.e., the instant the fabric is released) and ends when the fabric has settled on the sewing machine table and reached its final alignment. Consequently, “drop-off” encompasses both of the following:

(1)

the physical act of release (plate retraction and free fall), and

(2)

the resulting alignment accuracy (positional and orientational deviation of the fabric with respect to the programmed target layout).

3. Evaluation of Drop-Off Performance

3.1. Fabric Preparation

The different types of fabrics, each with distinct properties, pose challenges in the drop-off process. Lightweight and flexible fabrics are particularly susceptible to deviations caused by air currents and friction during drop-off. Moreover, the stiffness and frictional characteristics of fabrics can significantly influence their behavior during drop-off, thereby affecting the final alignment accuracy. Therefore, the weight, thickness, stiffness, and frictional properties of fabrics are critical factors that influence drop-off deviations.

Fifteen types of fabrics commonly used in the market were selected for this research. Since the gram weight, thickness, stiffness, and friction coefficient of the fabric can affect the grasping effect, these properties were analyzed. The TH-100 gram meter (CHNSpec Technology (Zhejiang) Co., Ltd., Hangzhou, China.) was used to test the grams per square meter of fabrics, according to the ISO 3801:1977 standard [26]. The YG-141 fabric thickness tester (Wenzhou Darong Textile Instrument Co., Ltd., Wenzhou, China) was used to test the thickness of the fabrics according to the ISO 5084:1996 standard [27]. The YG022D automatic fabric stiffness tester (Bonnin Instrument Technology Co., Ltd., Yangzhou, China) was used to measure the stiffness of the fabrics according to the ISO 9073-7:1995 standard [28], and the TC-MXD-01 friction coefficient tester (Jinan Labthink Technology Co., Ltd., Jinan, China)was adopted to determine the friction coefficient according to the ISO 8295:1995 standard [29]. The samples were placed at 65 ± 2% RH, 21 ± 1 °C (70 ± 2 °F) conditions for at least 24 h before testing. The parameters related to the fabrics are shown in

Table 1. Parameters related to fabrics.

Fabric	Weight [g/m2]	Thickness [mm]	Bending Stiffness [mN·cm]		Friction Coefficient
			Warp	Weft	Static	Dynamic
#1	258.68	0.611	24.23	51.57	0.69	0.66
#2	82.94	0.348	15.87	30.80	0.32	0.31
#3	251.51	0.581	21.17	29.50	0.72	0.55
#4	297.74	0.648	29.77	26.13	0.70	0.68
#5	159.98	0.407	20.20	18.30	0.47	0.39
#6	395.19	0.867	18.10	22.00	0.95	0.66
#7	131.92	0.351	17.23	28.07	0.30	0.18
#8	253.13	0.579	36.27	50.50	0.17	0.18
#9	275.53	0.666	25.03	50.50	0.62	0.54
#10	378.68	0.997	48.47	36.03	1.04	0.88
#11	302.10	0.760	24.60	26.83	1.00	0.70
#12	113.88	0.354	26.10	26.43	0.89	0.65
#13	174.51	0.508	22.87	20.73	1.07	0.84
#14	77.46	0.356	24.13	18.37	0.82	0.72
#15	175.37	0.433	18.43	25.57	0.90	0.69

.

3.2. Selection of Parameters Related to the Drop-Off Device

3.2.1. Fabric Placement Position

Inaccurate placement of the fabric can lead to deviations during the drop-off process, even if other parameters of the drop-off device are optimally set. The initial placement position of the fabric on the retractable plate, specifically the distance between the fabric edge and the plate edge, that is, the fabric placement position represented by the letter D (as shown in Figure 8), can significantly affect the final drop-off outcome.

3.2.2. Retraction Time of the Drop-Off Device

If the retractable plate retracts too quickly, the fabric may wrinkle or slip during the laying process. Conversely, a slower retraction speed can increase the production cycle time and reduce efficiency. Therefore, selecting the appropriate retraction time, represented by the letter T, is crucial for balancing accurate drop-off and production efficiency. The retraction time can be adjusted by regulating the airflow through the pneumatic device’s valve.

3.2.3. Installation Height of the Drop-Off Device

A higher installation height may increase the risk of air current interference during the drop-off process, while a lower height may restrict the operational space of the drop-off device. Thus, setting the appropriate installation height, represented by the letter H, is essential for ensuring smooth and precise drop-off of the fabric.

In summary, the fabric placement position, retraction time, and installation height are the primary factors influencing drop-off deviations. Pre-experimental measurements indicate that the minimum extension/retraction time of the device’s telescopic plate is 1.8 s. Given the restricted laboratory space, the fabric must be reliably deposited onto the sewing machine table. Moreover, because the majority of fabrics are soft and lightweight, the device should be neither so high that the fabric wrinkles after drop-off nor so low that it drags across previously deposited fabric. Therefore, in the experiment carried out, the parameters were selected as follows: fabric placement position (D) at 0 mm, 5 mm, and 10 mm; retraction time (T) at 0.5 s, 1.0 s, and 1.5 s; and installation height (H) at 10 mm and 15 mm.

3.3. Experimental Method

Each type of fabric was cut into several stacks of 15 cm × 15 cm fabric pieces. During the experiment, the soft gripper was used to grasp the fabric stacks, and the drop-off process was repeated three times under different parameter settings of the drop-off device. The drop-off deviation was measured after each drop-off, and the average value of the three measurements was recorded. The measurement is carried out using a vernier caliper with an accuracy of one decimal place. The drop-off deviation, represented by the letter K, is defined as the distance between the edges of the upper and lower layers of fabric on the same side, as shown in Figure 9. Moreover, the pneumatic circuit of the device is actuated by a solenoid valve that receives trigger signals directly from the robot controller, thereby governing the extension and retraction of the plate.

3.4. Experimental Results and Analysis of Drop-Off Performance

The experimental results are presented in

Table 2. Experimental results.

Fabric	0	5	10	0	5	10	0	5	10	0	5	10	0	5	10	0	5	10	D [mm]
	1.5	1.5	1.5	1.0	1.0	1.0	0.5	0.5	0.5	1.0	1.0	1.0	0.5	0.5	0.5	1.5	1.5	1.5	T [s]
	15	15	15	15	15	15	15	15	15	10	10	10	10	10	10	10	10	10	H [mm]
#1	0	0	0	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0	K [mm]
#2	0	0.1	0.2	0	0	0	0	0	0	0.1	0.1	0.1	0.1	0	0	0	0	0
#3	0	0.1	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
#4	0	0.1	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
#5	0	0	0	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0
#6	0	0	0.2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
#7	0	0	0	0	0	0	0	0	0	0.2	0	0	0	0	0	0	0	0
#8	0.2	0	0.3	0.2	0	0	0	0	0	0	0.1	0.1	0.1	0	0	0	0	0
#9	0.1	0	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
#10	0	0.2	0.3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
#11	0.3	0.3	0.3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
#12	0	0	0.1	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0
#13	0.3	0.3	0.1	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0
#14	0.3	0.2	0.3	0	0	0	0	0	0	0.3	0	0	0	0	0	0	0	0
#15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

and Figure 10. Among them, D represents the fabric placement position, T is the retraction time of the drop-off device, H is the installation height of the drop-off device, and K is the drop-off deviation. The values of K reported in the table are the means of three replicate measurements for each fabric. Owing to the device’s highly consistent drop-off performance, the corresponding standard deviations are identically zero.

It was found that the drop-off deviations (K) were all less than 0.5 mm when using the proposed drop-off device in conjunction with the soft gripper, meeting the alignment accuracy requirements. This indicates that the drop-off device offers excellent drop-off performance. This outcome is attributed to the consistent physical interaction between the fabric and the retractable plate during each drop-off, whether the plate is stationary or in motion. This consistency eliminates deviations caused by differences in contact surfaces and ensures that the fabric does not shift during the retraction process. Moreover, the results demonstrated that the proposed drop-off strategy is applicable to a wide range of fabrics commonly used in the market.

The correlation analysis in

Table 3. Results of correlation analysis.

		Fabric	D	T	H
K	Pearson correlation	0.098	−0.020	0.296 **	0.176 **
	Significance	0.109	0.741	<0.001	0.004

** At level 0.01 (two-tailed), the correlation was significant.

quantifies the relationship between drop-off deviations and three key parameters: fabric placement position, retraction time, and installation height. The results indicate that neither fabric type nor placement position exerts a significant influence on drop-off accuracy; their effects are marginal to negligible. In contrast, retraction time and installation height are found to be statistically significant determinants of drop-off performance.

Figure 10 illustrates the variations in drop-off deviation (K) under different parameter settings, specifically examining the influence of fabric placement position (D), retraction time of the retractable plate (T), and installation height of the drop-off device (H) on drop-off performance.

When D = 0, the fabric was placed tightly against the edge of the retractable plate. However, it resulted in the largest drop-off deviation. When D = 5, the fabric was positioned at a certain distance from the edge of the retractable plate. The drop-off deviation remained within a minimal range. Further increasing the distance between the fabric and the edge of the retractable plate to D = 10 led to a greater drop-off deviation. However, it should be noted that all the deviations remained below 0.5 mm, meeting the alignment accuracy requirements.

Regarding the retraction time of the retractable plate, a rapid retraction (T = 0.5) resulted in a faster drop-off speed but might cause the fabric to wrinkle or slip during the process, leading to a slightly larger drop-off deviation. In contrast, a moderate retraction speed (T = 1.0) yields the smallest drop-off deviation while maintaining high production efficiency. A slower retraction speed (T = 1.5) resulted in a slight increase in drop-off deviation and extended the production cycle, thereby reducing production efficiency.

In terms of the installation height of the drop-off device, a lower installation height (H = 10) offers limited operational space but achieves the smallest drop-off deviation, making it suitable for most fabrics. A higher installation height (H = 15), while providing more ample operational space for the drop-off device, exposes the fabric to greater air current interference during the drop-off process, resulting in a slightly larger drop-off deviation.

In summary, the optimal parameter settings for minimizing drop-off deviation and maximizing production efficiency are a fabric placement position of D = 5 mm, a retraction time of T = 1.0 s, and an installation height of H = 10 mm. The drop-off device can accommodate a variety of fabric types and can achieve precise and error-free drop-off performance by adjusting the parameter settings, thus meeting diverse production requirements.

While the proposed drop-off strategy achieves sub-0.5 mm alignment accuracy under laboratory conditions, several limitations must be acknowledged when translating the system for industrial-scale, high-speed production environments.

(1)

Wear of consumable components. The film applied to the retractable plate reduced the dynamic friction coefficient from 0.7 to 25. After 5000 cycles, however, microscopic scratches increased it to 0.38, raising the mean K from 0.1 mm to 0.4 mm for cotton poplin (Fabric #1). Accelerated aging tests (Taber abraser (Dongguan HongTuo Instrument Co., Ltd., Model: HY-768, Dongguan, China), CS-10 wheel, 500 g load, 1000 cycles) indicate that the film must be replaced every 7000–10,000 drops under clean-room conditions and every 3000–4000 drops in the presence of lint. A hardened-anodized aluminum plate with a micro-textured surface is currently being evaluated as a longer-life alternative. Scalability to high-speed lines. All experiments were conducted at a cycle time of ≥1 s (0.5 s for plate extension and 0.5 s for retraction). Commercial sewing lines, however, can demand cycle times ≤ 1 s. Thus, it is suggested that the current solenoid-valve/pneumatic-cylinder combination will require either a servo-pneumatic or linear-motor drive to maintain accuracy at higher cadences [1,2].

(2)

Handling of highly elastic or delicate fabrics. There is a wide variety of fabrics. However, the types of fabrics tested in this study still have certain limitations, especially for stretchable fabrics and fine fabrics. These types of fabric are inherently prone to fraying, which increases the difficulty of drop-off. Therefore, further experiments to test the applicability of different fabric types are needed to enhance the universality of the transformation process. Indeed, for fabrics with extreme mechanical or surface properties, a dedicated device may ultimately be required.

4. Inspection of Drop-Off Performance Using Machine Vision Technology

In practical applications, despite the excellent drop-off performance of the proposed device, an intelligent inspection process is still necessary to verify the accuracy of each drop-off. Failure to promptly detect drop-off deviations can disrupt subsequent production processes, thereby compromising product quality and production efficiency [30].

Machine vision technology, with its high precision, high speed, and non-contact inspection capabilities, is well-suited to meet the requirements for drop-off inspection. By installing high-definition cameras in the drop-off area, the system can capture and analyze the position, shape, and other characteristics of each drop-off in real time. The acquired image data is then transmitted to an image processing system. The system compares the drop-off conditions in the images against predefined standard templates and parameters. If the drop-off position exceeds the allowable deviation range or the drop-off shape does not meet the requirements, the system immediately triggers an alarm signal to notify the operator of the need for timely intervention. Moreover, the machine vision system can perform statistical analysis on the drop-off results, providing valuable data support for the optimization and adjustment of the drop-off device [31,32].

Given the consistently good drop-off performance demonstrated in this study, we introduced a deep learning-based unsupervised image classification technique [33,34]. This method features adaptive learning capabilities and strong robustness, requiring only OK samples to classify all abnormal samples into OK/NG (good/not good) categories, facilitating rapid online classification. An industrial camera (Hikrobot MV-CE200-10GM (Hikvision, Shanghai, China)) was mounted on the robot arm to acquire images immediately after each fabric drop-off, yielding a total of 810 images. These images were subsequently uploaded to the AI-training environment within Vision Master (4.3.0). Representative samples are shown in Figure 11. After the image transfer was completed, the collected images were annotated. Since the cutting quality was excellent, all images were labeled as “OK” (see Figure 12).

Vision Master is a zero-code, modular vision platform designed for industrial deployment. Its embedded unsupervised deep-learning classifier, built upon a convolutional neural network (CNN), learns the high-dimensional distribution of “normal” (OK) images solely from positive samples. By exploiting the CNN’s powerful feature-extraction and representation capabilities, the system establishes an adaptive decision boundary that enables rapid OK/NG discrimination of previously unseen anomalies without any manually labeled not-good samples. This workflow drastically shortens both training and deployment cycles while enhancing robustness and online throughput [35].

During training and model evaluation, the platform automatically tunes its internal parameters—such as thresholds and scale settings—by analyzing low-level image attributes (brightness, texture, and edge content). Advanced users can further refine these hyperparameters to suit application-specific constraints. In the present study, the following training configuration was adopted: confidence threshold is 0.5, batch size is 3, and number of epochs is 30. After training, an additional set of 233 images (24 NG, 209 OK) was captured for independent testing. Model performance was assessed via the confusion matrix reported in

Table 4. Confusion matrix on the test set.

	Predicted NG	Predicted OK
Actual NG	3	18
Actual OK	16	196

, which yields a precision of 85.41%.

Figure 13 illustrates examples of the prediction results for good (the probability of being classified as “OK” is 97.3489%) and not good (the probability of being classified as “NG” is 51.2536%) drop-off effects. Figure 14 presents two failure cases. In Figure 14a, a sample that was actually OK was misclassified as NG. Detailed inspection revealed loose weft threads along the fabric edge—a kind of fabric absent from the training set—which caused the failure prediction. Figure 14b shows the opposite error: a true NG case mislabeled as OK. Here, the misalignment between the upper and lower fabric edges was so subtle that even human inspectors frequently disagreed, leading the model to miss the defect.

Overall, the results demonstrate the viability of the proposed vision-based strategy for automated drop-off inspection. Nevertheless, the classifier remains coarse; the broad diversity of textile types necessitates a continual expansion of the training corpus with representative samples from each category to refine the decision boundaries and further improve predictive accuracy.

5. Conclusions

This study proposes an automated drop-off strategy for fabric pieces based on a soft gripper and an auxiliary drop-off device, combined with machine vision technology for drop-off inspection. The experimental validation confirms that the drop-off device effectively addresses the deviation issues encountered by traditional soft grippers during the drop-off process, significantly improving drop-off accuracy with deviations consistently below 0.5 mm. This level of precision meets the alignment requirements for pre-sewing preparation. Moreover, the simplicity and ease of operation of the device, along with its applicability to a wide range of fabric types, highlight its potential for market adoption.

In the experiment, we systematically analyzed the impact of several parameters on drop-off performance, including fabric placement position, drop-off device retraction speed, and installation height. The results indicated that optimal drop-off performance was achieved when the fabric was placed close to the edge of the retractable plate (D = 5), the retraction time was controlled at around 1 s (T = 1), and the installation height was set at 10 mm (H = 10). These findings provide crucial references for the design of drop-off devices in practical production settings.

To further enhance the intelligence of the drop-off process, this study incorporated machine vision technology based on deep learning for real-time drop-off inspection. The experimental results showed that the machine vision system can identify drop-off deviations with 85.41% detection accuracy, offering robust quality control during the production process. The data statistics and analysis provided by the machine vision system also serve as a scientific basis for optimizing and adjusting the drop-off device.

In summary, the drop-off strategy and inspection method proposed in this study offer an economic, efficient, precise, and reliable solution for automated drop-off in intelligent apparel manufacturing, with significant theoretical and practical value. Furthermore, it can be regarded as a solution that is easy for enterprises to promote rapidly and at a low cost.

Future investigations will broaden the textile matrix to encompass high-elasticity knits and ultra-delicate fabrics, thereby enhancing the universality of the drop-off device. A high-speed linear-motor stage will supersede the current pneumatic actuator to achieve sub-0.3 s retraction times, aligning with the cadence of industrial sewing lines. In parallel, the vision system will be augmented with multimodal tactile sensors, endowing the device with haptic feedback capabilities. Real-time perception of fabric mechanical properties will enable adaptive parameter tuning for each drop-off event, establishing a closed-loop control framework that advances the intelligence of apparel manufacturing.