Quality SICK Laser Sensor & IFM Pressure Sensor factory from China

quality SICK Laser Sensor & IFM Pressure Sensor factory

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A Tantalizing Question Shortly after the launch of GPT-4V in early 2023, we received a call from a long-term client. He served as the technical director of a home appliance manufacturer. Two years prior, we had deployed a surface inspection system based on YOLOv5 for their factory, which had been operating stably ever since. He raised a thought-provoking question over the phone: “I’ve seen that GPT-4V can interpret all kinds of images and recognize nearly everything. Can we adopt it directly for quality inspection? Would that eliminate the need for data labeling entirely?" I held back a straightforward answer back then. Truth be told, we were equally captivated by the idea ourselves. Demos of multimodal large models are undeniably impressive. Feed the model any random image, and it can outline contents, pinpoint defects and classify fault types. No training or labeling is required; it delivers zero-shot performance out of the box. If this capability translated seamlessly to factories, the entire rulebook for industrial visual inspection would be rewritten. We spent nearly two years testing diverse multimodal large model solutions across multiple projects. Our conclusion is clear: tempting as the technology may seem, real-world industrial application comes with harsh limitations. This article documents all the pitfalls we encountered over these two years. II. Establish the Current Landscape: YOLO Has Become the De Facto Standard Before diving into multimodal large models, it is critical to lay out the industry baseline: The dominant solution for today’s industrial visual inspection relies on object detection and segmentation models represented by the YOLO series. This is hardly a new trend. Starting from YOLOv3, through the widely deployed YOLOv8, YOLOv9 and YOLOv10, the YOLO family has been implemented in industrial production lines for years, boasting a fully mature technical stack. Why Has YOLO Become the De Facto Standard? First, ultra-fast inference speed. Equipped on standard edge computing boxes paired with industrial cameras, YOLOv8 completes inference for one frame within 10 to 30 milliseconds, matching the takt time of most production lines. Second, sufficient detection accuracy. With adequate labeled datasets, the YOLO series achieves outstanding precision for common defect categories, easily hitting an mAP of over 90%. Third, mature deployment ecosystem. Ready-made toolchains support multiple deployment frameworks including ONNX, TensorRT and OpenVINO. The full workflow from model training to on-site deployment has been validated by countless industrial projects. Fourth, comprehensive open-source ecosystem. The active open-source community provides accessible fixes for most technical hurdles, with abundant pre-trained weights, data augmentation kits and labeling tools readily available. Therefore, the YOLO series is practically the default choice for industrial visual inspection projects launched in 2024. There is no need to debate whether deep learning should be adopted — that question was settled a decade ago. The new core question now arises: With the emergence of multimodal large models, does YOLO still remain the optimal solution? III. The Allure of Multimodal Large Models: A Promising Mirage 2023 witnessed an explosive wave of multimodal large model releases. Models including GPT-4V, Gemini and Claude 3 deliver powerful general image comprehension capabilities. We have run tests on these models, and honestly, their demo performances are truly impressive: Allure 1: Zero-Shot Capability Traditional workflow: To inspect a specific type of defect, you first need to collect, label and train on images of that defect. No data means no usable model. Multimodal large models: Simply describe your demand in natural language, such as “Check whether there are scratches in this image", and the model will return results instantly. No training or labeling required. What does this mean? The cold-start cost drops close to zero. When launching new products, there is no need to spend two weeks on data collection, labeling and model training. You can put the model into use merely with a few lines of prompts. Allure 2: Advanced Semantic Comprehension Traditional models only output bounding boxes and confidence scores, e.g. “A defect exists within this box with a confidence of 0.87". Multimodal large models generate descriptive natural language: “A scratch of around 2cm appears at the top-left corner of the picture, likely formed during transportation. It is recommended to optimize the packaging process." What does this mean? Inspection results can be directly converted into formal quality inspection reports. Allure 3: Powerful Generalization Capacity Traditional models can only recognize defect types seen during training; they fail to identify brand-new unseen defects. In theory, multimodal large models have processed massive images sourced from the internet, enabling them to potentially recognize all kinds of rare and irregular defects. What does this mean? Coverage for long-tail defects and abnormal edge cases is drastically improved. Allure 4: Interactive Inspection Logic Traditional solutions embed fixed inspection rules into the model. Revising inspection criteria requires full retraining. Multimodal large models support dynamic adjustment of standards via prompts. For instance, you can set the threshold as “scratches over 1cm count as NG" one day and switch it to “0.5cm" the next without modifying the underlying model. What does this mean? Tuning inspection standards becomes extremely flexible. Reading all these advantages, you may also be tempted — just as we were back then. That’s why we decided to deploy multimodal large models in several real projects, only to run into a string of costly pitfalls afterward. IV. Six Costly Pitfalls Encountered in Practical Deployment Pitfall 1: Excessive Inference Latency Unsuitable for Production Lines Our pilot project focused on appearance inspection for mobile phone housings. The production line processes one workpiece every 3 seconds, meaning total inspection latency must stay below 2 seconds to reserve 1 second for robotic sorting. We tested the GPT-4V API workflow: Upload the image and input the prompt Wait for server response Receive inspection results Average latency hit 4–6 seconds, and could exceed 10 seconds amid network fluctuations — far too slow for the assembly line. You might suggest self-hosted open-source multimodal models such as LLaVA and Qwen-VL instead. We tested these as well. Running LLaVA-13B on an A100 GPU yields single-image inference latency of roughly 800ms to 1.2 seconds. While faster than cloud APIs, it remains dozens of times slower than YOLO. Pitfall 2: Skyrocketing Throughput and Computing Costs Even if we tolerate the latency for argument’s sake, the cost calculation tells a harsh story. How many images does one production line process daily? Assuming one workpiece every 3 seconds and 20 hours of daily operation, a single line generates around 24,000 inspection images per day. For GPT-4V API, unit pricing ranged from $0.01 to $0.03 per image, depending on resolution and token consumption: Daily cost per line: $240–$720 Monthly cost per line: $7,200–$21,600 Annual cost per line: $86,400–$259,200 This only accounts for one line, while our client operated 12 production lines — an unaffordable expense for manufacturers. What about self-hosted open-source models? A single A100 GPU delivers roughly 1–2 QPS (queries per second). A single line peaks at around 0.3 QPS, seemingly manageable with one card for multiple lines. However, factoring in servers, IDC space and maintenance, the annual operating cost for an A100 deployment runs into hundreds of thousands of RMB. In contrast, a YOLO deployment only requires an edge computing box costing a few thousand RMB to support one full production line. The cost gap spans two orders of magnitude. Pitfall 3: Unstable, Probabilistic Outputs — Inconsistent Results for Identical Images This proved our most frustrating roadblock. Industrial inspection demands absolute determinism: identical images must yield identical inspection results every single time, otherwise standardized quality control and traceability become impossible. Multimodal large models, however, produce probabilistic outputs. We ran a controlled test: feeding the same defective image with an identical prompt to GPT-4V ten separate times. The outcomes varied drastically: 7 runs labeled the product defective 2 runs marked it suspected defective requiring manual review 1 run claimed no obvious defects existed All from the exact same input and prompt. Such randomness is fatal for factory quality control. Inspectors cannot act on a “70% chance of defect" output — every workpiece needs a definitive OK or NG verdict. Some propose setting temperature to 0 for consistency. We tried this method, which improved stability yet failed to guarantee 100% identical outputs. Large models generate results via sampling mechanisms, and minor deviations persist for edge cases even with temperature = 0. Pitfall 4: Fragile Prompt Engineering — Minor Wording Shifts Alter Judgments Multimodal model performance hinges entirely on prompt design, which we spent extensive manpower optimizing to boost accuracy and stability. We soon discovered prompts are extremely sensitive to wording changes. Three prompts with nearly identical core requests delivered vastly different inspection outcomes: Prompt A: “Check whether surface defects exist in this image." Prompt B: “Carefully examine the product surface and identify scratches, pits, foreign matter and other defects." Prompt C: “Act as a professional quality inspector. Locate and classify any appearance defects on the product in this image." Worse still, prompts fine-tuned for Product A lose efficacy when applied to Product B, requiring full rework of prompt logic for every new product variant. How does this differ from retraining YOLO models for new products? YOLO training relies on quantifiable evaluation metrics to clearly signal when the model meets standards; prompt tuning depends entirely on subjective trial and error, with no clear benchmark for optimal performance. Pitfall 5: Hallucination — Fabricating Non-Existent Defects with Confidence Hallucination is a well-documented flaw of large language and multimodal models: the system confidently invents details that do not exist. In industrial inspection, this manifests as three typical failures: Flagging defect-free products as defective Misstating defect positions (e.g. locating scratches on the left when they appear on the right) Misclassifying defect types (e.g. labeling pits as scratches) One test case exemplifies the severity: an entirely flawless product image triggered a highly detailed fabricated analysis: “A shallow scratch approximately 3mm long is detected at the bottom-right corner, functional impact assessment recommended." Upon close visual review, no mark or scratch was present in that region at all. If such hallucinations infiltrate mass production lines, severe consequences follow: either defective goods slip through undetected (missed inspection) or qualified products get wrongly rejected (false rejection). Pitfall 6: High Resource Barriers for Private On-Premise Deployment As cloud APIs suffer high latency and excessive cost, self-hosted deployment seems like an alternative. We evaluated hardware and software requirements for mainstream open-source multimodal models: How About YOLO? YOLOv8-m runs smoothly even on a GTX 1080 with 8GB VRAM. It can even be deployed on edge computing hardware such as NVIDIA Jetson modules with power consumption of merely tens of watts. The computational resource threshold differs by an entire order of magnitude. For most factories, installing an A100 server on the production floor is impractical in terms of both capital expenditure and daily operation & maintenance. V. Back to First Principles: What Exactly Does Industrial Visual Inspection Require? After stumbling through all the above pitfalls, we stepped back to reflect on a fundamental question: What core capabilities are essentially demanded by industrial visual inspection? Deterministic Output Identical images must yield 100% consistent results. This forms the foundation of standardized quality control and full traceability; probabilistic outputs are unacceptable. Ultra-Low Latency Millisecond-level response. Production line takt time is rigid, and inspection cannot become a bottleneck. A 10ms inference time and a 1,000ms inference time represent entirely different operational realities. High Throughput How many frames can be processed per second? How many workpieces can be inspected daily? Computational costs must remain controllable, avoiding annual expenses of hundreds of thousands of US dollars for a single production line. Edge Deployment Compatibility Factory network environments are complex; many workshops lack stable or accessible internet connections. Models must operate locally on edge devices rather than relying on cloud APIs. Interpretable Inspection Results When a defect is detected, the system needs to clearly inform inspectors of its exact location and category. Ideally, it should output defect coordinates, area and confidence scores for downstream system integration. Controllable Maintenance Costs Products get upgraded and inspection standards are revised on a regular basis. The adaptation cost for every iteration must be manageable, without full reconstruction each time. Matching these six core requirements against the two technical routes reveals a clear contrast: YOLO Series meets all six criteria perfectly Determinism: 100% consistent outputs given identical input Low latency: 10–30 millisecond inference High throughput: Dozens to over a hundred QPS per single GPU Edge-deployable: Fully compatible with Jetson hardware and industrial PCs Interpretable outputs: Bounding boxes, defect categories and confidence values Low maintenance overhead: Mature toolchains for incremental training and transfer learning Multimodal Large Models fail nearly every requirement Determinism: Inherently probabilistic output Latency constraint: Second-scale inference Throughput limit: Single GPU only supports single-digit QPS Edge deployment barrier: Demands A100-class high-end GPUs Interpretability gap: Raw natural language descriptions require secondary parsing Unpredictable maintenance: Prompt engineering lacks quantifiable optimization standards So can multimodal large models replace YOLO? The conclusion is unambiguous: At the current stage of technical maturity, multimodal large models are unsuitable as the primary solution for industrial visual inspection. Its strengths including zero-shot reasoning, deep semantic comprehension and strong generalization deliver little practical value on production lines; meanwhile its critical flaws — high latency, prohibitive costs and unstable outputs — are catastrophic for industrial quality control. VI. Not Replacement, But Complementation This does not mean multimodal large models are completely useless for industrial visual inspection. The key lies in identifying their proper niche. After two years of field trials, we have summarized four scenarios where multimodal large models create tangible value: Scenario 1: Auxiliary Automated Data Annotation Annotation constitutes the biggest cost driver of traditional inspection projects. An industrial vision task usually requires thousands to tens of thousands of annotated images. Outsourcing annotation services costs several tenths to several US dollars per frame, with labeling expenses accounting for 30%–50% of total project investment. Multimodal large models deliver pre-labeling capability: The model generates preliminary annotation masks and boxes from raw images first. Human staff only need to review and revise results instead of labeling from scratch. Our field tests prove this workflow boosts annotation efficiency by 3–5 times, cutting average labeling time per image from 30 seconds to under 10 seconds. Scenario 2: Fallback Coverage for Long-Tail Defects The performance ceiling of YOLO models is straightforward: they can only recognize defect types featured in training datasets. Unprecedented rare defects will trigger missed detection by YOLO. Although such long-tail anomalies occur infrequently, they often signal severe abnormal manufacturing conditions, carrying higher operational risks. Multimodal large models act as a fallback verification layer: When YOLO outputs a borderline confidence score (roughly 0.3–0.7, the gray zone of uncertainty), the corresponding image is sent to the multimodal model for secondary judgment. The zero-shot generalization strength of large models covers these unseen rare anomalies. Under this mechanism, only 5%–10% of all images are forwarded to the multimodal model, keeping total costs manageable while drastically improving coverage of long-tail defects. Scenario 3: Semantic Conversion of Raw Inspection Data YOLO only outputs structured data: bounding boxes, defect categories and confidence scores. While sufficient for backend industrial systems, these raw metrics are unintuitive for human inspectors, who need answers to practical questions: How severe is the defect? What caused it? What corrective action should be taken? Multimodal large models perform semantic report generation: Input: Defect coordinates, classification labels, product model and manufacturing process parameters Output: Natural language inspection report, e.g. “A 5mm scratch is detected on the left edge of the product, likely caused by mold abrasion; mold maintenance is recommended." This task is latency-insensitive (reports can be generated asynchronously) and cost-efficient (only executed on NG non-conforming products with limited volume). Scenario 4: Rapid Cold Start for Small-Sample Urgent Projects Clients occasionally face tight deadlines: new products scheduled for mass production the following week with merely dozens of defective sample images, insufficient for full YOLO training. Traditional workflow cannot launch inspection under such limited data. Multimodal large models serve as a transitional temporary solution: Zero-shot capability enables immediate deployment with acceptable yet imperfect accuracy, far outperforming full manual inspection. Data can be continuously collected during pilot operation to train a formal YOLO model for long-term use once sufficient samples are accumulated. VII. Hybrid Architecture: Our Practical Deployment Paradigm Based on the above analysis, we have adopted a hybrid dual-channel architecture for recent industrial projects: Main Inspection Channel: YOLO Handles over 95% of all inspection workloads Deployed locally on edge hardware with 10–20ms inference latency Outputs structured bounding boxes, defect types and confidence scores Auxiliary Channel: Multimodal Large Model Only processes borderline low-confidence images within the gray zone Invoked asynchronously without disrupting main line throughput Functions for long-tail defect fallback verification, semantic report generation and auxiliary labeling Core design principles of this hybrid framework: YOLO acts as the core primary system; multimodal models serve as auxiliary tools — avoid reversing their roles Data shunting instead of serial processing: multimodal models stay off the critical production path and impose no impact on main-line latency or throughput Confidence-based traffic splitting: high-confidence results pass through directly, while ambiguous samples are forwarded for secondary multimodal validation Predictable cost control: only a small fraction of images consumes multimodal model computing resources VIII. Technical Selection Decision Framework Below is a summarized decision tree for teams selecting industrial visual inspection algorithms: Latency Requirement Required inference

.gtr-container-f8g7h2 { font-family: Verdana, Helvetica, "Times New Roman", Arial, sans-serif; color: #333; line-height: 1.6; padding: 16px; max-width: 100%; box-sizing: border-box; overflow-wrap: break-word; word-wrap: break-word; } .gtr-container-f8g7h2 p { font-size: 14px; margin-bottom: 1em; text-align: left !important; } .gtr-container-f8g7h2 strong { font-weight: bold; } .gtr-container-f8g7h2 .gtr-main-title { font-size: 18px; font-weight: bold; color: #0000FF; margin-bottom: 1.5em; text-align: left !important; } .gtr-container-f8g7h2 .gtr-section-title { font-size: 18px; font-weight: bold; color: #0000FF; margin-top: 2em; margin-bottom: 1em; text-align: left !important; } .gtr-container-f8g7h2 .gtr-subsection-title { font-size: 16px; font-weight: bold; color: #0000FF; margin-top: 1.5em; margin-bottom: 0.8em; text-align: left !important; } .gtr-container-f8g7h2 ul, .gtr-container-f8g7h2 ol { margin: 1em 0; padding-left: 20px; } .gtr-container-f8g7h2 li { list-style: none !important; position: relative; margin-bottom: 0.5em; padding-left: 1.5em; text-align: left !important; } .gtr-container-f8g7h2 ul li::before { content: "•" !important; color: #0000FF; font-size: 1.2em; position: absolute !important; left: 0 !important; top: 0; } .gtr-container-f8g7h2 ol { counter-reset: list-item; } .gtr-container-f8g7h2 ol li::before { content: counter(list-item) "." !important; color: #0000FF; font-weight: bold; position: absolute !important; left: 0 !important; top: 0; width: 1.2em; text-align: right; margin-right: 0.5em; } .gtr-container-f8g7h2 div[style*="display: block; flex: 0 1 auto; flex-direction: row; justify-content: normal; align-items: normal;"] { margin-bottom: 1em; } @media (min-width: 768px) { .gtr-container-f8g7h2 { padding: 24px; } .gtr-container-f8g7h2 .gtr-main-title { font-size: 20px; } .gtr-container-f8g7h2 .gtr-section-title { font-size: 20px; } .gtr-container-f8g7h2 .gtr-subsection-title { font-size: 18px; } } Full English Translation (Industry In-depth Article Tone) Industry Pains and Transformations Amid the Restructuring of Full-Chain Strategy For practitioners engaged in machine vision and equipment integration, one common headache has lingered since last year: Hikrobot industrial cameras have grown increasingly hard to source. From the industry’s most widely deployed measuring models — 2/3-inch 5MP, 1-inch 20MP C-mount variants — to standard area-scan cameras, spot inventories across distribution channels remain chronically tight, with lead times repeatedly extended. This has spurred widespread speculation across the trade: Is Hikrobot deliberately limiting output to drive price hikes? Or leveraging its market dominance to crowd out competitors? English Translation (Industry analysis formal tone) However, stepping back from the immediate spot supply shortage and analyzing from the perspective of corporate strategy and industry cycles reveals that the current stock shortage is by no means a simple market manipulation. Instead, it is an inevitable outcome of Hikrobot’s top-down comprehensive strategic adjustments covering product lines, production capacity, distribution channels and business priorities. Restraints in the upstream supply chain and surging downstream demand have only exacerbated the severity of supply shortages. In short: strategic layout adjustment is the fundamental root cause, market consolidation a collateral outcome, and supply-demand mismatch a short-term aggravating factor. 一. Core Fundamental Logic: Supply Shortages Root in Full Industrial Chain Strategic Restructuring Many people equate the supply shortage with "capping output to drive up prices", but they have confused cause and effect. Hikrobot’s core strategic move is to complete a comprehensive upgrade and restructuring of all business lines during the transition window of product renewal and capacity relocation. The supply shortage is only a temporary growing pain arising from this transition. 1. Product Line Iteration: Full Migration to CU Platform, Phase-out of Legacy CS/CH Series Starting from the second half of 2025, Hikrobot has issued multiple Product Change Notices (PCNs), gradually discontinuing its high-volume legacy CS and CH industrial camera series, and fully shifting to the new-generation cost-effective CU models and premium AI-CH cameras. Supply chain perspective: No additional orders will be placed for legacy CMOS and FPGA chips. Formal production suspension takes effect once existing raw materials are depleted, and distributors will no longer receive restock allocations for discontinued models. Market perspective: The widely adopted 2/3-inch 5-megapixel C-mount global shutter cameras — the primary models compatible with domestic telecentric lenses — have borne the brunt, resulting in widespread supply outages. Underlying strategic goal: Standardize the hardware R&D platform to streamline production lines and cut material management costs. In addition, the new platform embeds ISP and lightweight AI preprocessing functions to precisely meet emerging high-end inspection demands from lithium battery, photovoltaic, 3C electronics and other manufacturing sectors. 二. Restructuring of Capacity Layout: Ramp-up of Tonglu New Base Creates Supply Gap During Transition Between Old and New Production Lines The mismatch between dwindling old capacity and yet-to-mature new production lines constitutes the most direct supply-side cause of product shortages. Hikrobot’s Tonglu Intelligent Manufacturing Base, with a total investment of 1.534 billion RMB, is designed for an annual output of 5 million machine vision products and only entered full-scale production in early 2026. Meanwhile, the old factories have gradually cut production and begun equipment relocation. During the overlapping operation period of old and new production lines, production capacity was split between two sites, making it impossible to fulfill the massive order volume as before. Coupled with the explosive concentrated demand for inspection equipment in lithium battery, photovoltaic and semiconductor industries over the past two years, the manufacturer can only allocate available stock by project priority. Top key customers receive priority supply, leaving small and medium-sized equipment integrators and scattered retail orders struggling to secure cameras. 三. Shift of Business Focus: Resource Reallocation Toward 3D Vision and Full-Stack Solutions In recent years, Hikrobot’s strategic priority has shifted far beyond standalone hardware sales to full-stack embodied intelligent manufacturing solutions. Integrated systems combining vision, AGVs and mobile robots represent its core growth driver for the future. At the supply chain level, procurement quotas for upstream CMOS sensors and storage chips are prioritized for high-margin, high-value-added products including 3D cameras, smart code readers and vision controllers, while chip allocations for traditional 2D area-scan cameras are intentionally reduced. Compounding the strain, global wafer fabs are diverting most capacity to AI computing chips and HBM memory, resulting in a more than 30% capacity contraction for industrial-grade global shutter CMOS and FPGAs. The dual pressure of internal resource reallocation and external component shortages has drastically widened supply gaps for 2D cameras. 4. Revamp of Distribution System: Cut Bulk Spot Allocations, Secure Long-Term Orders via Direct Contracts with Top Clients Tightened distribution policies are the most visible trigger for widespread stock shortages among end users. Since late 2025, Hikrobot has rolled out stricter channel rules, slashing spot inventory quotas for small and medium-sized distributors. Instead, it prioritizes signing annual framework agreements with leading equipment manufacturers in lithium battery and photovoltaic sectors, locking up large volumes of spot stock under long-term contracts in advance. This has created a clear industry divide: large manufacturers enjoy stable order fulfillment with guaranteed supply, while small and medium integrators and small-batch urgent retail orders face a severe lack of available stock, amplifying the perception of shortages across distribution channels. II. Objective Outcome: Accelerated Industrial Consolidation, Not a Deliberate Target It is critical to clarify that the current supply shortage was not engineered by Hikrobot to deliberately cut output, suppress competitors or monopolize the market. Industrial reshuffling and market restructuring are merely secondary side effects arising from its strategic overhaul. Replacement Opportunities for Second-Tier Domestic Brands Massive numbers of small and medium equipment integrators have been forced to adopt domestic alternative solutions, driving a sharp surge in orders for brands including Huaray, Daheng, ECOVIS and MindVision, alongside rapid growth in their market share. Phase-Out of Low-End Low-Margin Capacity Hikrobot’s voluntary discontinuation of low-margin legacy camera models is depleting low-price stock in the market, lifting the overall average product price and weeding out small vision manufacturers that rely solely on price competition without proprietary solution capabilities. Widened Competitive Edge for Industry Leaders For Hikrobot, hardware shortages barely impact delivery of its integrated solution orders. Long-term partnerships anchored by full-set solutions solidify its core major customer base, widening the gap with small manufacturers that only supply standalone cameras. In short: consolidation is a consequence, not an original objective. This is not a premeditated market crackdown, but a natural industrial reshuffle brought about by corporate upgrading. III. Three Overlapping Factors Exacerbating Supply Shortages While strategic restructuring constitutes the fundamental root of stock shortages, the triple convergence of upstream constraints, downstream demand spikes and product transition cycles has pushed supply gaps to a level felt throughout the entire industry. Hard Constraints from Upstream Supply Chains Global semiconductor foundries prioritize capacity for high-end computing chips, leaving industrial CMOS and FPGAs hardest hit: production capacity for related components has shrunk by over 30%, with lead times extended from the original 4 weeks to more than 12 weeks. Even running production lines at full tilt, manufacturers face a critical shortage of core components. Meanwhile, rising prices of raw materials such as copper and PCBs discourage manufacturers from excessive stockpiling due to cash flow and inventory risk concerns, further limiting supply flexibility. Concentrated Surge in Downstream Demand 2026 marks a peak year for mass production of new energy and semiconductor inspection equipment. Mass rollout of projects including lithium electrode inspection, photovoltaic silicon wafer sorting and semiconductor appearance inspection has driven a year-on-year surge of over 65% in demand for high-precision measuring cameras, far outpacing the release speed of existing production capacity. Supply Disruption During Transition Between Old and New Product Lines Full discontinuation of legacy models coincides with low mass-production yield rates for the new CU series, creating a natural 3–6 month supply vacuum. Limited initial production capacity of the CU platform is allocated first to major key customers, further squeezing spot inventory available through open distribution channels. IV. Three Profound Long-Term Impacts of Industry-Wide Supply Tightness Widespread stock shortages send ripple effects across every participant in the industrial chain, bringing short-term growing pains alongside lasting structural shifts. 1. For Equipment Integrators: Short-Term Disruption, Long-Term Resilient Supply Chains Short-term impact: Project delivery timelines are delayed due to depleted mainstream C-mount measuring camera stock. Many integrators are forced to switch to alternative brands temporarily, incurring extra costs for prototype testing and program adaptation. Long-term benefit: Companies are compelled to build multi-brand alternative product libraries, reducing reliance on a single supplier and boosting overall supply chain risk resistance. 2. For the Competitive Landscape: Stratified Domestic Market, Benefits for Supporting Industries A two-leader domestic market pattern is taking shape: Hikrobot dominates the high-end integrated solution segment, while Huaray absorbs substitution demand with steady stock to capture mainstream market share. Brands such as Daheng and MindVision rapidly seize market space previously held by small integrators. Imported brands see mild short-term demand recovery: players including Basler and Cognex have secured partial high-end replacement orders, yet lead times exceeding 8 weeks restrict their application to only premium precision inspection scenarios. 3. For Hikrobot Itself: Short-Term Loss of Retail Clients, Improved Long-Term Corporate Value Short-term downsides: A large volume of small-batch retail orders is lost to competitors, with some projects poached by rival manufacturers; distributors face mounting inventory pressure and growing dissatisfaction. Long-term upsides: Low-margin product lines are phased out, shifting the product portfolio toward high-value 3D vision and AI inspection solutions. Once the Tonglu manufacturing base reaches full capacity, overall output will double for drastically improved long-term supply stability. Direct long-term contracts with major clients also lock in revenue streams for years to come. 五. When Will Shortages Ease? Practical Solutions Available Right Now This is the top concern for all industry practitioners. We provide forecasts based on production capacity and product cycles, along with implementable solutions for mainstream application scenarios. 1. Forecasted Timeline for Supply Recovery Based on current progress, the Tonglu Intelligent Manufacturing Base is expected to reach full capacity by the end of 2026. Coupled with steady mass production yields of the CU series and newly launched upstream CMOS wafer production capacity, supply of standard 2D area-scan cameras is projected to return to normal in Q1 2027. It is important to note that legacy CS and CH series have been permanently discontinued with no plans for resumption of production. Future system design must fully adopt the new platform or alternative brands. 2. Readily Applicable Camera Selection Strategies Two categories of recommendations are provided for the most widely deployed camera applications across industries: Emergency Replacement Solution Brands including Huaray and Daheng offer products with fully matching parameters equivalent to discontinued Hikrobot legacy models, supported by ample spot inventory. Minimal software modification is required to enable fast migration. Long-Term Project Solution Enterprises planning new projects may place advance orders to reserve stock of Hikrobot’s new CU series cameras. Closing Remarks Looking back at the development of China’s machine vision industry, every iteration of production capacity and product lineup is accompanied by cyclical supply and demand fluctuations. The ongoing supply shortage of Hikrobot cameras is essentially an inevitable transition for a market leader upgrading from a pure hardware manufacturer to a full-stack solution provider. Phasing out outdated production capacity, migrating to new hardware platforms, and restructuring distribution channels and business priorities all come with transitional growing pains. Cyclical volatility in upstream semiconductor supply chains and the explosive demand from the new energy sector have amplified the industry-wide impact of this transformation. For all players in the sector, rather than dwelling on debates over intentional price manipulation, it is more prudent to establish multi-brand camera libraries and diversified supply chain backups to maintain stable operations amid industry shifts.

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SICK DBS36E-S3AK00100 - Rotation-Speed-Detect/TTL-Pulse-Signal-Output - Compact 36mm Metal 100PPR Solid Shaft Industrial Encoder

SICK DFS60A-S4PC65536 - Rotation-Measure/High-Density-Pulse-Output - IP65 M12 Alloy Solid Shaft Ultra High Res Rotary Encoder

SICK DFS60A-S4PC00S06 - Rotation-Sensing/Adjustable-Pulse-Output - IP65 M12 Alloy Solid Shaft Programmable Industrial Rotary Encoder

SICK DFS60B-BHAA03000 - Rotation-Detect/TTL-Pulse-Output - IP65 Alloy Blind Hollow 3000PPR Rotary Encoder

SICK DFS60B-BHPA10000 - Rotation-Position-Detect/Programmable-HTL-Pulse-Output - IP67 Aluminum 15mm Blind Hollow 10000PPR Rotary Encoder

SICK DFS60B-S1PA10000 - Rotation-Track/HTL-Pulse-Output - IP65 M23 Single Cable Alloy Solid Shaft 10000PPR Rotary Encoder

SICK DFS60B-S1PC10000 - Rotation-Detect/Incremental-Pulse-Transmit - IP65 M12 Single-Cable Solid Shaft 10000PPR Rotary Encoder

SICK DFS60B-S4EA05000 - Rotation-Measure/HTL-Pulse-Signal-Transmit - IP65 Metal M23 Solid Shaft 5000PPR Industrial Rotary Encoder